waiting for the review

app.py

@@ -0,0 +1,286 @@
+import io
+import pandas as pd
+import torch
+import plotly.graph_objects as go
+from PIL import Image
+import numpy as np
+import gradio as gr
+
+# ----------------------------
+# Helper functions (logic mostly unchanged)
+# ----------------------------
+
+torch.manual_seed(42)
+_forecast_tensor = torch.load("stocks_data_forecast.pt")  # shape = (n_series, pred_len, n_q)
+
+
+def model_forecast(input_data):
+    return _forecast_tensor
+
+
+def plot_forecast_plotly(timeseries, quantile_predictions, timeseries_name):
+    # Create an interactive Plotly figure
+    fig = go.Figure()
+    x_hist = list(range(len(timeseries)))
+    # Historical data trace
+    fig.add_trace(go.Scatter(
+        x=x_hist,
+        y=timeseries,
+        mode='lines+markers',
+        name=f"{timeseries_name} - Given Data",
+        line=dict(width=2),
+    ))
+
+    # Prediction data traces for each quantile
+    x_pred = list(range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions)))
+    for i in range(quantile_predictions.shape[1]):
+        fig.add_trace(go.Scatter(
+            x=x_pred,
+            y=quantile_predictions[:, i],
+            mode='lines',
+            name=f"{timeseries_name} - Quantile {i+1}",
+            opacity=0.8,
+        ))
+
+    fig.update_layout(
+        title=dict(text=f"Timeseries: {timeseries_name}", x=0.5, font=dict(size=16, family="Arial", color="#000")),
+        xaxis_title="Time",
+        yaxis_title="Value",
+        hovermode='x unified'
+    )
+    return fig
+
+
+def load_table(file_path):
+    ext = file_path.split(".")[-1].lower()
+    if ext == "csv":
+        return pd.read_csv(file_path)
+    elif ext in ("xls", "xlsx"):
+        return pd.read_excel(file_path)
+    elif ext == "parquet":
+        return pd.read_parquet(file_path)
+    else:
+        raise ValueError("Unsupported format. Use CSV, XLS, XLSX, or PARQUET.")
+
+
+def extract_names_and_update(file, preset_filename):
+    try:
+        if file is not None:
+            df = load_table(file.name)
+        else:
+            if not preset_filename:
+                return gr.update(choices=[], value=[]), []
+            df = load_table(preset_filename)
+
+        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():
+            names = df.iloc[:, 0].tolist()
+        else:
+            names = [f"Series {i}" for i in range(len(df))]
+        return gr.update(choices=names, value=names), names
+    except Exception:
+        return gr.update(choices=[], value=[]), []
+
+
+def filter_names(search_term, all_names):
+    if not all_names:
+        return gr.update(choices=[], value=[])
+    if not search_term:
+        return gr.update(choices=all_names, value=all_names)
+    lower = search_term.lower()
+    filtered = [n for n in all_names if lower in str(n).lower()]
+    return gr.update(choices=filtered, value=filtered)
+
+
+def check_all(names_list):
+    return gr.update(value=names_list)
+
+
+def uncheck_all(_):
+    return gr.update(value=[])
+
+
+def display_filtered_forecast(file, preset_filename, selected_names):
+    try:
+        # If no file uploaded and no valid preset chosen, return early
+        if file is None and (preset_filename is None or preset_filename == "-- No preset selected --"):
+            return None, "No file selected."
+
+        # Load data
+        if file is not None:
+            df = load_table(file.name)
+        else:
+            df = load_table(preset_filename)
+
+        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():
+            all_names = df.iloc[:, 0].tolist()
+            data_only = df.iloc[:, 1:].astype(float)
+        else:
+            all_names = [f"Series {i}" for i in range(len(df))]
+            data_only = df.astype(float)
+
+        mask = [name in selected_names for name in all_names]
+        if not any(mask):
+            return None, "No timeseries chosen to plot."
+
+        filtered_data = data_only.iloc[mask, :].values
+        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]
+        out = _forecast_tensor[mask]   # slice forecasts to match filtered rows
+        inp = torch.tensor(filtered_data)
+
+        # If multiple series selected, create a subplot for each in a single figure
+        fig = go.Figure()
+        for idx in range(inp.shape[0]):
+            ts = inp[idx].numpy().tolist()
+            qp = out[idx].numpy()
+            series_name = filtered_names[idx]
+            x_hist = list(range(len(ts)))
+            # Historical data
+            fig.add_trace(go.Scatter(
+                x=x_hist,
+                y=ts,
+                mode='lines+markers',
+                name=f"{series_name} - Given Data"
+            ))
+            # Quantiles
+            x_pred = list(range(len(ts) - 1, len(ts) - 1 + qp.shape[0]))
+            for i in range(qp.shape[1]):
+                fig.add_trace(go.Scatter(
+                    x=x_pred,
+                    y=qp[:, i],
+                    mode='lines',
+                    name=f"{series_name} - Quantile {i+1}",
+                    opacity=0.6
+                ))
+
+        fig.update_layout(
+            title=dict(text="Forecasts for Selected Timeseries", x=0.5, font=dict(size=16, family="Arial", color="#000")),
+            xaxis_title="Time",
+            yaxis_title="Value",
+            hovermode='x unified'
+        )
+        return fig, ""
+    except Exception as e:
+        return None, f"Error: {e}. Use CSV, XLS, XLSX, or PARQUET."
+
+
+# ----------------------------
+# Gradio layout: two columns + instructions
+# ----------------------------
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 📈 TiRex - timeseries forecasting 📊")
+    gr.Markdown("Upload data or choose a preset, filter by name, then click Plot.")
+
+    with gr.Row():
+        # Left column: controls
+        with gr.Column():
+            gr.Markdown("## Data Selection")
+            file_input = gr.File(
+                label="Upload CSV / XLSX / PARQUET",
+                file_types=[".csv", ".xls", ".xlsx", ".parquet"]
+            )
+            preset_choices = ["-- No preset selected --", "stocks_data_noindex.csv", "stocks_data.csv"]
+
+            preset_dropdown = gr.Dropdown(
+                label="Or choose a preset:",
+                choices=preset_choices,
+                value="-- No preset selected --"
+            )
+
+
+
+            gr.Markdown("## Search / Filter")
+            search_box = gr.Textbox(placeholder="Type to filter (e.g. 'AMZN')")
+            filter_checkbox = gr.CheckboxGroup(
+                choices=[], value=[], label="Select which timeseries to show"
+            )
+
+            with gr.Row():
+                check_all_btn = gr.Button("✅ Check All")
+                uncheck_all_btn = gr.Button("❎ Uncheck All")
+
+            plot_button = gr.Button("▶️ Plot Forecasts")
+            errbox = gr.Textbox(label="Error Message", interactive=False)
+            with gr.Row():
+                gr.Image("static/nxai_logo.png", width=150, show_label=False, container=False)
+                gr.Image("static/tirex.jpeg", width=150, show_label=False, container=False)
+
+        # Right column: interactive plot + instructions
+        with gr.Column():
+            gr.Markdown("## Forecast Plot")
+            plot_output = gr.Plot()
+
+            # Instruction text below plot
+            gr.Markdown("## Instructions")
+            gr.Markdown(
+                """
+                **How to format your data:**
+                - Your file must be a table (CSV, XLS, XLSX, or Parquet).
+                - **One row per timeseries.** Each row is treated as a separate series.
+                - If you want to **name** each series, put the name as the first value in **every** row:
+                  - Example (CSV):  
+                    `AAPL, 120.5, 121.0, 119.8, ...`  
+                    `AMZN, 3300.0, 3310.5, 3295.2, ...`  
+                  - In that case, the first column is not numeric, so it will be used as the series name.
+                - If you do **not** want named series, simply leave out the first column entirely and have all values numeric:
+                  - Example:  
+                    `120.5, 121.0, 119.8, ...`  
+                    `3300.0, 3310.5, 3295.2, ...`  
+                  - Then every row will be auto-named “Series 0, Series 1, …” in order.
+                - **Consistency rule:** Either all rows have a non-numeric first entry for the name, or none do. Do not mix.
+                - The rest of the columns (after the optional name) must be numeric data points for that series.
+                - You can filter by typing in the search box. Then check or uncheck individual names before plotting.
+                - Use “Check All” / “Uncheck All” to quickly select or deselect every series.
+                - Finally, click **Plot Forecasts** to view the quantile forecast for each selected series (for 64 steps ahead).
+                """
+            )
+
+    names_state = gr.State([])
+
+    # When file or preset changes, update names
+    file_input.change(
+        fn=extract_names_and_update,
+        inputs=[file_input, preset_dropdown],
+        outputs=[filter_checkbox, names_state]
+    )
+    preset_dropdown.change(
+        fn=extract_names_and_update,
+        inputs=[file_input, preset_dropdown],
+        outputs=[filter_checkbox, names_state]
+    )
+
+    # When search term changes, filter names
+    search_box.change(
+        fn=filter_names,
+        inputs=[search_box, names_state],
+        outputs=[filter_checkbox]
+    )
+
+    # Check All / Uncheck All
+    check_all_btn.click(fn=check_all, inputs=names_state, outputs=filter_checkbox)
+    uncheck_all_btn.click(fn=uncheck_all, inputs=names_state, outputs=filter_checkbox)
+
+    # Plot button
+    plot_button.click(
+        fn=display_filtered_forecast,
+        inputs=[file_input, preset_dropdown, filter_checkbox],
+        outputs=[plot_output, errbox]
+    )
+    demo.launch()
+
+
+
+
+
+
+# '''
+# 1. Prepared datasets
+# 2. Plots of different quiantilies (different colors)
+# 3. Filters for plots...
+# 4. Different input options
+# 5. README.md in there (in UI) (contact us for fine-tuning)
+# 6. Requirements for dimensions
+# 7. LOGO of NX-AI and xLSTM and tirex
+# 8. *Range of prediction length customizable 
+# 9. *Multivariate data (x_t is vector)
+# '''

stocks_data.csv

@@ -0,0 +1,11 @@
+Ticker,2024-12-04,2024-12-05,2024-12-06,2024-12-09,2024-12-10,2024-12-11,2024-12-12,2024-12-13,2024-12-16,2024-12-17,2024-12-18,2024-12-19,2024-12-20,2024-12-23,2024-12-24,2024-12-26,2024-12-27,2024-12-30,2024-12-31,2025-01-02,2025-01-03,2025-01-06,2025-01-07,2025-01-08,2025-01-10,2025-01-13,2025-01-14,2025-01-15,2025-01-16,2025-01-17,2025-01-21,2025-01-22,2025-01-23,2025-01-24,2025-01-27,2025-01-28,2025-01-29,2025-01-30,2025-01-31,2025-02-03,2025-02-04,2025-02-05,2025-02-06,2025-02-07,2025-02-10,2025-02-11,2025-02-12,2025-02-13,2025-02-14,2025-02-18,2025-02-19,2025-02-20,2025-02-21,2025-02-24,2025-02-25,2025-02-26,2025-02-27,2025-02-28,2025-03-03,2025-03-04,2025-03-05,2025-03-06,2025-03-07,2025-03-10,2025-03-11,2025-03-12,2025-03-13,2025-03-14,2025-03-17,2025-03-18,2025-03-19,2025-03-20,2025-03-21,2025-03-24,2025-03-25,2025-03-26,2025-03-27,2025-03-28,2025-03-31,2025-04-01,2025-04-02,2025-04-03,2025-04-04,2025-04-07,2025-04-08,2025-04-09,2025-04-10,2025-04-11,2025-04-14,2025-04-15,2025-04-16,2025-04-17,2025-04-21,2025-04-22,2025-04-23,2025-04-24,2025-04-25,2025-04-28,2025-04-29,2025-04-30,2025-05-01,2025-05-02,2025-05-05,2025-05-06,2025-05-07,2025-05-08,2025-05-09,2025-05-12,2025-05-13,2025-05-14,2025-05-15,2025-05-16,2025-05-19,2025-05-20,2025-05-21,2025-05-22,2025-05-23,2025-05-27,2025-05-28,2025-05-29,2025-05-30,2025-06-02,2025-06-03,2025-06-04
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test.ipynb

@@ -0,0 +1,1682 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  10 of 10 completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yfinance as yf\n",
+    "import pandas as pd\n",
+    "\n",
+    "# List of 10 example tickers (you can replace these with any tickers you prefer)\n",
+    "tickers = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"JPM\", \"JNJ\", \"V\"]\n",
+    "\n",
+    "# Download daily adjusted close prices for the last month (≈30 calendar days)\n",
+    "data = yf.download(tickers, period=\"6mo\", interval=\"1d\", auto_adjust=False)[\"Adj Close\"]\n",
+    "\n",
+    "# Transpose so that each row is one company’s month-long timeseries\n",
+    "df = pd.DataFrame(data.transpose())\n",
+    "\n",
+    "# At this point, `df` has:\n",
+    "# • Index: the 10 tickers\n",
+    "# • Columns: one column per trading day in the last month\n",
+    "# • Values: adjusted close price for that ticker on that date\n",
+    "df.to_csv(\"stocks_data_noindex.csv\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stable version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import gradio as gr\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "output = torch.load(\"stocks_data_forecast.pt\")  # (n_timeseries, pred_len, n_quantiles)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return output\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    \"\"\"Returns a NumPy array of the plotted figure.\"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
+    "    ax.plot(timeseries, color=\"blue\")\n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\")\n",
+    "    buf = io.BytesIO()\n",
+    "\n",
+    "    # Add title\n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\")\n",
+    "    # Add labels to the legend (quantiles)\n",
+    "    labels = [f\"Quantile {i+1}\" for i in range(quantile_predictions.shape[1])]\n",
+    "    ax.legend(labels, loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "    plt.tight_layout(rect=[0, 0, 0.85, 1])\n",
+    "\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)  # Return as an H×W×3 array\n",
+    "\n",
+    "def display_forecast(file, preset_filename):\n",
+    "    accepted_formats = ['csv', 'xls', 'xlsx', 'parquet']\n",
+    "\n",
+    "    def load_table(file_path):\n",
+    "        ext = file_path.split('.')[-1].lower()\n",
+    "        if ext == 'csv':\n",
+    "            return pd.read_csv(file_path)\n",
+    "        elif ext in ['xls', 'xlsx']:\n",
+    "            return pd.read_excel(file_path)\n",
+    "        elif ext == 'parquet':\n",
+    "            return pd.read_parquet(file_path)\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unsupported file format '.{ext}'. Acceptable formats: CSV, XLS, XLSX, PARQUET.\")\n",
+    "    \n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"Please upload a file or select a preset.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "        \n",
+    "        # Check first column for timeseries names\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object:\n",
+    "            if not df.iloc[:, 0].str.isnumeric().all():\n",
+    "                timeseries_names = df.iloc[:, 0].tolist()\n",
+    "                df = df.iloc[:, 1:]\n",
+    "            else:\n",
+    "                timeseries_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "        else:\n",
+    "            timeseries_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "\n",
+    "        _input = torch.tensor(df.values)\n",
+    "        _output = model_forecast(_input)\n",
+    "\n",
+    "        gallery_images = []\n",
+    "        for i in range(_input.shape[0]):\n",
+    "            img_array = plot_forecast_image(_input[i], _output[i], timeseries_names[i])\n",
+    "            gallery_images.append(img_array)\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Please upload files in one of the following formats: CSV, XLS, XLSX, PARQUET.\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "iface = gr.Interface(\n",
+    "    fn=display_forecast,\n",
+    "    inputs=[\n",
+    "        gr.File(label=\"Upload your CSV file (optional)\"),\n",
+    "        gr.Dropdown(\n",
+    "            label=\"Or select a preset CSV file\",\n",
+    "            choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "            value=\"stocks_data_noindex.csv\"\n",
+    "        )\n",
+    "    ],\n",
+    "    outputs=[\n",
+    "        gr.Gallery(label=\"Forecast Plots (one per row)\"), \n",
+    "        gr.Textbox(label=\"Error Message\")\n",
+    "    ],\n",
+    "    title=\"CSV→Dynamic Forecast Gallery\",\n",
+    "    description=\"Upload a CSV with any number of rows; each row’s forecast becomes one image in a gallery.\",\n",
+    "    allow_flagging=\"never\",\n",
+    ")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    iface.launch()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# '''\n",
+    "# 1. Prepared datasets\n",
+    "# 2. Plots of different quiantilies (different colors)\n",
+    "# 3. Filters for plots...\n",
+    "# 4. Different input options\n",
+    "# 5. README.md in there (in UI) (contact us for fine-tuning)\n",
+    "# 6. Requirements for dimensions\n",
+    "# 7. Multivariate data (x_t is vector)\n",
+    "# 8. LOGO of NX-AI and xLSTM and tirex\n",
+    "# '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticker</th>\n",
+       "      <th>2024-12-04</th>\n",
+       "      <th>2024-12-05</th>\n",
+       "      <th>2024-12-06</th>\n",
+       "      <th>2024-12-09</th>\n",
+       "      <th>2024-12-10</th>\n",
+       "      <th>2024-12-11</th>\n",
+       "      <th>2024-12-12</th>\n",
+       "      <th>2024-12-13</th>\n",
+       "      <th>2024-12-16</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2025-05-21</th>\n",
+       "      <th>2025-05-22</th>\n",
+       "      <th>2025-05-23</th>\n",
+       "      <th>2025-05-27</th>\n",
+       "      <th>2025-05-28</th>\n",
+       "      <th>2025-05-29</th>\n",
+       "      <th>2025-05-30</th>\n",
+       "      <th>2025-06-02</th>\n",
+       "      <th>2025-06-03</th>\n",
+       "      <th>2025-06-04</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>242.425201</td>\n",
+       "      <td>242.455124</td>\n",
+       "      <td>242.255600</td>\n",
+       "      <td>246.156204</td>\n",
+       "      <td>247.173752</td>\n",
+       "      <td>245.896820</td>\n",
+       "      <td>247.363297</td>\n",
+       "      <td>247.532883</td>\n",
+       "      <td>250.435867</td>\n",
+       "      <td>...</td>\n",
+       "      <td>202.089996</td>\n",
+       "      <td>201.360001</td>\n",
+       "      <td>195.270004</td>\n",
+       "      <td>200.210007</td>\n",
+       "      <td>200.419998</td>\n",
+       "      <td>199.949997</td>\n",
+       "      <td>200.850006</td>\n",
+       "      <td>201.699997</td>\n",
+       "      <td>203.270004</td>\n",
+       "      <td>203.150101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AMZN</td>\n",
+       "      <td>218.160004</td>\n",
+       "      <td>220.550003</td>\n",
+       "      <td>227.029999</td>\n",
+       "      <td>226.089996</td>\n",
+       "      <td>225.039993</td>\n",
+       "      <td>230.259995</td>\n",
+       "      <td>228.970001</td>\n",
+       "      <td>227.460007</td>\n",
+       "      <td>232.929993</td>\n",
+       "      <td>...</td>\n",
+       "      <td>201.119995</td>\n",
+       "      <td>203.100006</td>\n",
+       "      <td>200.990005</td>\n",
+       "      <td>206.020004</td>\n",
+       "      <td>204.720001</td>\n",
+       "      <td>205.699997</td>\n",
+       "      <td>205.009995</td>\n",
+       "      <td>206.649994</td>\n",
+       "      <td>205.710007</td>\n",
+       "      <td>207.472000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GOOGL</td>\n",
+       "      <td>173.970016</td>\n",
+       "      <td>172.244003</td>\n",
+       "      <td>174.309265</td>\n",
+       "      <td>175.168259</td>\n",
+       "      <td>184.956985</td>\n",
+       "      <td>195.175217</td>\n",
+       "      <td>191.739182</td>\n",
+       "      <td>189.601639</td>\n",
+       "      <td>196.433777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>168.559998</td>\n",
+       "      <td>170.869995</td>\n",
+       "      <td>168.470001</td>\n",
+       "      <td>172.899994</td>\n",
+       "      <td>172.360001</td>\n",
+       "      <td>171.860001</td>\n",
+       "      <td>171.740005</td>\n",
+       "      <td>169.029999</td>\n",
+       "      <td>166.179993</td>\n",
+       "      <td>167.785004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JNJ</td>\n",
+       "      <td>148.006256</td>\n",
+       "      <td>147.071823</td>\n",
+       "      <td>146.865265</td>\n",
+       "      <td>147.150513</td>\n",
+       "      <td>146.786560</td>\n",
+       "      <td>144.238968</td>\n",
+       "      <td>143.845535</td>\n",
+       "      <td>144.219299</td>\n",
+       "      <td>141.494659</td>\n",
+       "      <td>...</td>\n",
+       "      <td>151.877960</td>\n",
+       "      <td>151.312805</td>\n",
+       "      <td>151.639999</td>\n",
+       "      <td>153.250000</td>\n",
+       "      <td>152.429993</td>\n",
+       "      <td>153.580002</td>\n",
+       "      <td>155.210007</td>\n",
+       "      <td>155.399994</td>\n",
+       "      <td>154.419998</td>\n",
+       "      <td>153.419998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JPM</td>\n",
+       "      <td>240.666992</td>\n",
+       "      <td>242.723633</td>\n",
+       "      <td>244.582520</td>\n",
+       "      <td>241.072388</td>\n",
+       "      <td>240.133057</td>\n",
+       "      <td>240.795532</td>\n",
+       "      <td>238.817978</td>\n",
+       "      <td>237.245850</td>\n",
+       "      <td>236.889893</td>\n",
+       "      <td>...</td>\n",
+       "      <td>261.040009</td>\n",
+       "      <td>260.670013</td>\n",
+       "      <td>260.709991</td>\n",
+       "      <td>265.290009</td>\n",
+       "      <td>263.489990</td>\n",
+       "      <td>264.369995</td>\n",
+       "      <td>264.000000</td>\n",
+       "      <td>264.660004</td>\n",
+       "      <td>266.269989</td>\n",
+       "      <td>265.065002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>META</td>\n",
+       "      <td>612.740173</td>\n",
+       "      <td>607.898376</td>\n",
+       "      <td>622.713257</td>\n",
+       "      <td>612.530518</td>\n",
+       "      <td>618.270813</td>\n",
+       "      <td>631.608154</td>\n",
+       "      <td>629.721375</td>\n",
+       "      <td>619.299072</td>\n",
+       "      <td>623.685120</td>\n",
+       "      <td>...</td>\n",
+       "      <td>635.500000</td>\n",
+       "      <td>636.570007</td>\n",
+       "      <td>627.059998</td>\n",
+       "      <td>642.320007</td>\n",
+       "      <td>643.580017</td>\n",
+       "      <td>645.049988</td>\n",
+       "      <td>647.489990</td>\n",
+       "      <td>670.900024</td>\n",
+       "      <td>666.849976</td>\n",
+       "      <td>685.159973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MSFT</td>\n",
+       "      <td>435.744720</td>\n",
+       "      <td>440.924805</td>\n",
+       "      <td>441.871155</td>\n",
+       "      <td>444.311768</td>\n",
+       "      <td>441.632050</td>\n",
+       "      <td>447.270416</td>\n",
+       "      <td>447.838196</td>\n",
+       "      <td>445.556976</td>\n",
+       "      <td>449.860413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>452.570007</td>\n",
+       "      <td>454.859985</td>\n",
+       "      <td>450.179993</td>\n",
+       "      <td>460.690002</td>\n",
+       "      <td>457.359985</td>\n",
+       "      <td>458.679993</td>\n",
+       "      <td>460.359985</td>\n",
+       "      <td>461.970001</td>\n",
+       "      <td>462.970001</td>\n",
+       "      <td>464.190002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>NVDA</td>\n",
+       "      <td>145.116653</td>\n",
+       "      <td>145.046661</td>\n",
+       "      <td>142.426895</td>\n",
+       "      <td>138.797226</td>\n",
+       "      <td>135.057587</td>\n",
+       "      <td>139.297180</td>\n",
+       "      <td>137.327362</td>\n",
+       "      <td>134.237656</td>\n",
+       "      <td>131.987854</td>\n",
+       "      <td>...</td>\n",
+       "      <td>131.800003</td>\n",
+       "      <td>132.830002</td>\n",
+       "      <td>131.289993</td>\n",
+       "      <td>135.500000</td>\n",
+       "      <td>134.809998</td>\n",
+       "      <td>139.190002</td>\n",
+       "      <td>135.130005</td>\n",
+       "      <td>137.380005</td>\n",
+       "      <td>141.220001</td>\n",
+       "      <td>141.854996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>357.929993</td>\n",
+       "      <td>369.489990</td>\n",
+       "      <td>389.220001</td>\n",
+       "      <td>389.790009</td>\n",
+       "      <td>400.989990</td>\n",
+       "      <td>424.769989</td>\n",
+       "      <td>418.100006</td>\n",
+       "      <td>436.230011</td>\n",
+       "      <td>463.019989</td>\n",
+       "      <td>...</td>\n",
+       "      <td>334.619995</td>\n",
+       "      <td>341.040009</td>\n",
+       "      <td>339.339996</td>\n",
+       "      <td>362.890015</td>\n",
+       "      <td>356.899994</td>\n",
+       "      <td>358.429993</td>\n",
+       "      <td>346.459991</td>\n",
+       "      <td>342.690002</td>\n",
+       "      <td>344.269989</td>\n",
+       "      <td>334.671600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>V</td>\n",
+       "      <td>308.866455</td>\n",
+       "      <td>308.049194</td>\n",
+       "      <td>309.972778</td>\n",
+       "      <td>307.271790</td>\n",
+       "      <td>311.338196</td>\n",
+       "      <td>312.743500</td>\n",
+       "      <td>313.182037</td>\n",
+       "      <td>313.690308</td>\n",
+       "      <td>314.836517</td>\n",
+       "      <td>...</td>\n",
+       "      <td>358.299988</td>\n",
+       "      <td>357.970001</td>\n",
+       "      <td>353.540009</td>\n",
+       "      <td>359.299988</td>\n",
+       "      <td>359.730011</td>\n",
+       "      <td>362.399994</td>\n",
+       "      <td>365.190002</td>\n",
+       "      <td>365.320007</td>\n",
+       "      <td>365.859985</td>\n",
+       "      <td>368.179993</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 125 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Ticker  2024-12-04  2024-12-05  2024-12-06  2024-12-09  2024-12-10  \\\n",
+       "0   AAPL  242.425201  242.455124  242.255600  246.156204  247.173752   \n",
+       "1   AMZN  218.160004  220.550003  227.029999  226.089996  225.039993   \n",
+       "2  GOOGL  173.970016  172.244003  174.309265  175.168259  184.956985   \n",
+       "3    JNJ  148.006256  147.071823  146.865265  147.150513  146.786560   \n",
+       "4    JPM  240.666992  242.723633  244.582520  241.072388  240.133057   \n",
+       "5   META  612.740173  607.898376  622.713257  612.530518  618.270813   \n",
+       "6   MSFT  435.744720  440.924805  441.871155  444.311768  441.632050   \n",
+       "7   NVDA  145.116653  145.046661  142.426895  138.797226  135.057587   \n",
+       "8   TSLA  357.929993  369.489990  389.220001  389.790009  400.989990   \n",
+       "9      V  308.866455  308.049194  309.972778  307.271790  311.338196   \n",
+       "\n",
+       "   2024-12-11  2024-12-12  2024-12-13  2024-12-16  ...  2025-05-21  \\\n",
+       "0  245.896820  247.363297  247.532883  250.435867  ...  202.089996   \n",
+       "1  230.259995  228.970001  227.460007  232.929993  ...  201.119995   \n",
+       "2  195.175217  191.739182  189.601639  196.433777  ...  168.559998   \n",
+       "3  144.238968  143.845535  144.219299  141.494659  ...  151.877960   \n",
+       "4  240.795532  238.817978  237.245850  236.889893  ...  261.040009   \n",
+       "5  631.608154  629.721375  619.299072  623.685120  ...  635.500000   \n",
+       "6  447.270416  447.838196  445.556976  449.860413  ...  452.570007   \n",
+       "7  139.297180  137.327362  134.237656  131.987854  ...  131.800003   \n",
+       "8  424.769989  418.100006  436.230011  463.019989  ...  334.619995   \n",
+       "9  312.743500  313.182037  313.690308  314.836517  ...  358.299988   \n",
+       "\n",
+       "   2025-05-22  2025-05-23  2025-05-27  2025-05-28  2025-05-29  2025-05-30  \\\n",
+       "0  201.360001  195.270004  200.210007  200.419998  199.949997  200.850006   \n",
+       "1  203.100006  200.990005  206.020004  204.720001  205.699997  205.009995   \n",
+       "2  170.869995  168.470001  172.899994  172.360001  171.860001  171.740005   \n",
+       "3  151.312805  151.639999  153.250000  152.429993  153.580002  155.210007   \n",
+       "4  260.670013  260.709991  265.290009  263.489990  264.369995  264.000000   \n",
+       "5  636.570007  627.059998  642.320007  643.580017  645.049988  647.489990   \n",
+       "6  454.859985  450.179993  460.690002  457.359985  458.679993  460.359985   \n",
+       "7  132.830002  131.289993  135.500000  134.809998  139.190002  135.130005   \n",
+       "8  341.040009  339.339996  362.890015  356.899994  358.429993  346.459991   \n",
+       "9  357.970001  353.540009  359.299988  359.730011  362.399994  365.190002   \n",
+       "\n",
+       "   2025-06-02  2025-06-03  2025-06-04  \n",
+       "0  201.699997  203.270004  203.150101  \n",
+       "1  206.649994  205.710007  207.472000  \n",
+       "2  169.029999  166.179993  167.785004  \n",
+       "3  155.399994  154.419998  153.419998  \n",
+       "4  264.660004  266.269989  265.065002  \n",
+       "5  670.900024  666.849976  685.159973  \n",
+       "6  461.970001  462.970001  464.190002  \n",
+       "7  137.380005  141.220001  141.854996  \n",
+       "8  342.690002  344.269989  334.671600  \n",
+       "9  365.320007  365.859985  368.179993  \n",
+       "\n",
+       "[10 rows x 125 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv(\"stocks_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Not checked but with labels filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "\n",
+    "# Set random seed and load your pretrained forecast tensor\n",
+    "torch.manual_seed(42)\n",
+    "_forecast_tensor = torch.load(\"stocks_data_forecast.pt\")  # shape = (n_series, pred_len, n_q)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return _forecast_tensor\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    \"\"\"Given one 1D series + quantile‐matrix, return a NumPy array of the plotted figure.\"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
+    "    ax.plot(timeseries, color=\"blue\")\n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\")\n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\")\n",
+    "\n",
+    "    labels = [f\"Quantile {i}\" for i in range(quantile_predictions.shape[1])]\n",
+    "    ax.legend(labels, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
+    "    plt.tight_layout(rect=[0, 0, 0.85, 1])\n",
+    "\n",
+    "    buf = io.BytesIO()\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def load_table(file_path):\n",
+    "    \"\"\"Load CSV / XLS(X) / Parquet by extension, else raise.\"\"\"\n",
+    "    ext = file_path.split(\".\")[-1].lower()\n",
+    "    if ext == \"csv\":\n",
+    "        return pd.read_csv(file_path)\n",
+    "    elif ext in (\"xls\", \"xlsx\"):\n",
+    "        return pd.read_excel(file_path)\n",
+    "    elif ext == \"parquet\":\n",
+    "        return pd.read_parquet(file_path)\n",
+    "    else:\n",
+    "        raise ValueError(\n",
+    "            f\"Unsupported file format '.{ext}'. Accepted: CSV, XLS, XLSX, PARQUET.\"\n",
+    "        )\n",
+    "\n",
+    "def extract_names_and_update(file, preset_filename):\n",
+    "    \"\"\"\n",
+    "    Read the table (uploaded or preset), extract timeseries names, and return:\n",
+    "    1) gr.update for the CheckboxGroup (all names pre‐checked)\n",
+    "    2) the full list of names to store in state.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return gr.update(choices=[], value=[]), []\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            names = df.iloc[:, 0].tolist()\n",
+    "        else:\n",
+    "            names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "\n",
+    "        return gr.update(choices=names, value=names), names\n",
+    "    except Exception:\n",
+    "        return gr.update(choices=[], value=[]), []\n",
+    "\n",
+    "def filter_names(search_term, all_names):\n",
+    "    \"\"\"\n",
+    "    Filter the full list of names (all_names) by the search_term (case‐insensitive substring).\n",
+    "    Return gr.update with filtered choices and keep checked those that remain in both.\n",
+    "    \"\"\"\n",
+    "    if not all_names:\n",
+    "        return gr.update(choices=[], value=[])\n",
+    "    if not search_term:\n",
+    "        # No search term → show all\n",
+    "        return gr.update(choices=all_names, value=all_names)\n",
+    "    lower = search_term.lower()\n",
+    "    filtered = [n for n in all_names if lower in str(n).lower()]\n",
+    "    return gr.update(choices=filtered, value=filtered)\n",
+    "\n",
+    "def check_all(names_list):\n",
+    "    \"\"\"Return an update that checks all names in the checkbox.\"\"\"\n",
+    "    return gr.update(value=names_list)\n",
+    "\n",
+    "def uncheck_all(_):\n",
+    "    \"\"\"Return an update that unchecks all names.\"\"\"\n",
+    "    return gr.update(value=[])\n",
+    "\n",
+    "def display_filtered_forecast(file, preset_filename, selected_names):\n",
+    "    \"\"\"\n",
+    "    Load the table, filter by selected_names, run forecast, and return:\n",
+    "    - list of images (NumPy arrays) for the gallery\n",
+    "    - error string (empty if OK)\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"No file selected.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            all_names = df.iloc[:, 0].tolist()\n",
+    "            data_only = df.iloc[:, 1:].astype(float)\n",
+    "        else:\n",
+    "            all_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "            data_only = df.astype(float)\n",
+    "\n",
+    "        mask = [name in selected_names for name in all_names]\n",
+    "        if not any(mask):\n",
+    "            return [], \"No timeseries chosen to plot.\"\n",
+    "\n",
+    "        filtered_data = data_only.iloc[mask, :].values\n",
+    "        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]\n",
+    "\n",
+    "        inp = torch.tensor(filtered_data)           # (n_chosen, length)\n",
+    "        out = model_forecast(inp)                   # (n_chosen, pred_len, n_q)\n",
+    "\n",
+    "        gallery_images = []\n",
+    "        for i in range(inp.shape[0]):\n",
+    "            gallery_images.append(\n",
+    "                plot_forecast_image(inp[i], out[i], filtered_names[i])\n",
+    "            )\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Please upload a valid CSV, XLS, XLSX, or PARQUET file.\"\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"## Upload or select a preset → search/filter by name → click Plot\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        file_input = gr.File(\n",
+    "            label=\"Upload CSV/XLSX/PARQUET (optional)\",\n",
+    "            file_types=[\".csv\", \".xls\", \".xlsx\", \".parquet\"]\n",
+    "        )\n",
+    "        preset_dropdown = gr.Dropdown(\n",
+    "            label=\"Or pick a preset:\",\n",
+    "            choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "            value=\"stocks_data_noindex.csv\"\n",
+    "        )\n",
+    "\n",
+    "    # A text box to type a substring (search term)\n",
+    "    search_box = gr.Textbox(\n",
+    "        label=\"Search/Filter timeseries by name\",\n",
+    "        placeholder=\"Type to filter (e.g. 'AMZN')\",\n",
+    "        value=\"\"\n",
+    "    )\n",
+    "\n",
+    "    # A CheckboxGroup to show matching names; choices/value will be updated dynamically\n",
+    "    filter_checkbox = gr.CheckboxGroup(\n",
+    "        choices=[], value=[], label=\"Select which timeseries to show\"\n",
+    "    )\n",
+    "\n",
+    "    # Buttons to check or uncheck all\n",
+    "    with gr.Row():\n",
+    "        check_all_btn = gr.Button(\"Check All\")\n",
+    "        uncheck_all_btn = gr.Button(\"Uncheck All\")\n",
+    "\n",
+    "    plot_button = gr.Button(\"Plot\")\n",
+    "\n",
+    "    gallery = gr.Gallery(label=\"Forecast Plots (filtered)\")\n",
+    "    errbox = gr.Textbox(label=\"Error Message\")\n",
+    "\n",
+    "    # State to hold the full list of names\n",
+    "    names_state = gr.State([])\n",
+    "\n",
+    "    # 1) When file or preset changes, extract full names and update the checkbox + state\n",
+    "    file_input.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "    preset_dropdown.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "\n",
+    "    # 2) When search text changes, filter names_state and update the checkbox\n",
+    "    search_box.change(\n",
+    "        fn=filter_names,\n",
+    "        inputs=[search_box, names_state],\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # 3) Check All button: set checkbox value to all names in state\n",
+    "    check_all_btn.click(\n",
+    "        fn=check_all,\n",
+    "        inputs=names_state,\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # 4) Uncheck All button: set checkbox value to empty list\n",
+    "    uncheck_all_btn.click(\n",
+    "        fn=uncheck_all,\n",
+    "        inputs=names_state,\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # 5) When \"Plot\" is clicked, generate the filtered plots\n",
+    "    plot_button.click(\n",
+    "        fn=display_filtered_forecast,\n",
+    "        inputs=[file_input, preset_dropdown, filter_checkbox],\n",
+    "        outputs=[gallery, errbox],\n",
+    "    )\n",
+    "\n",
+    "demo.launch()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checked, filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Helper functions (logic unchanged)\n",
+    "# ----------------------------\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "_forecast_tensor = torch.load(\"stocks_data_forecast.pt\")  # shape = (n_series, pred_len, n_q)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return _forecast_tensor\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
+    "    ax.plot(timeseries, color=\"blue\")\n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\")\n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\")\n",
+    "    labels = [f\"Quantile {i}\" for i in range(quantile_predictions.shape[1])]\n",
+    "    ax.legend(labels, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
+    "    plt.tight_layout(rect=[0, 0, 0.85, 1])\n",
+    "    buf = io.BytesIO()\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def load_table(file_path):\n",
+    "    ext = file_path.split(\".\")[-1].lower()\n",
+    "    if ext == \"csv\":\n",
+    "        return pd.read_csv(file_path)\n",
+    "    elif ext in (\"xls\", \"xlsx\"):\n",
+    "        return pd.read_excel(file_path)\n",
+    "    elif ext == \"parquet\":\n",
+    "        return pd.read_parquet(file_path)\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported format. Use CSV, XLS, XLSX, or PARQUET.\")\n",
+    "\n",
+    "def extract_names_and_update(file, preset_filename):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return gr.update(choices=[], value=[]), []\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            names = df.iloc[:, 0].tolist()\n",
+    "        else:\n",
+    "            names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "        return gr.update(choices=names, value=names), names\n",
+    "    except Exception:\n",
+    "        return gr.update(choices=[], value=[]), []\n",
+    "\n",
+    "def filter_names(search_term, all_names):\n",
+    "    if not all_names:\n",
+    "        return gr.update(choices=[], value=[])\n",
+    "    if not search_term:\n",
+    "        return gr.update(choices=all_names, value=all_names)\n",
+    "    lower = search_term.lower()\n",
+    "    filtered = [n for n in all_names if lower in str(n).lower()]\n",
+    "    return gr.update(choices=filtered, value=filtered)\n",
+    "\n",
+    "def check_all(names_list):\n",
+    "    return gr.update(value=names_list)\n",
+    "\n",
+    "def uncheck_all(_):\n",
+    "    return gr.update(value=[])\n",
+    "\n",
+    "def display_filtered_forecast(file, preset_filename, selected_names):\n",
+    "    \"\"\"\n",
+    "    Load the table, filter by selected_names, run forecast (correctly sliced),\n",
+    "    and return a gallery + error string.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"No file selected.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        # Extract all_names and numeric data\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object \\\n",
+    "           and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            all_names = df.iloc[:, 0].tolist()\n",
+    "            data_only = df.iloc[:, 1:].astype(float)\n",
+    "        else:\n",
+    "            all_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "            data_only = df.astype(float)\n",
+    "\n",
+    "        # Build mask and filtered subset\n",
+    "        mask = [name in selected_names for name in all_names]\n",
+    "        if not any(mask):\n",
+    "            return [], \"No timeseries chosen to plot.\"\n",
+    "\n",
+    "        filtered_data = data_only.iloc[mask, :].values\n",
+    "        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]\n",
+    "\n",
+    "        # ------------------------\n",
+    "        # HERE is the only change:\n",
+    "        # Instead of calling model_forecast(inp), slice the full tensor by mask:\n",
+    "        # ------------------------\n",
+    "        out = _forecast_tensor[mask]   # shape = (n_chosen, pred_len, n_q)\n",
+    "        inp = torch.tensor(filtered_data)\n",
+    "\n",
+    "        # Plot each chosen series against its properly‐aligned forecast\n",
+    "        gallery_images = []\n",
+    "        for i in range(inp.shape[0]):\n",
+    "            gallery_images.append(\n",
+    "                plot_forecast_image(inp[i], out[i], filtered_names[i])\n",
+    "            )\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Please upload a valid CSV, XLS, XLSX, or PARQUET file.\"\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio layout: two columns\n",
+    "# ----------------------------\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"# 📈 Stock Forecast Viewer 📊\")\n",
+    "    gr.Markdown(\"Upload data or choose a preset, filter by name, then click Plot.\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        # Left column: controls\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Data Selection\")\n",
+    "            file_input = gr.File(\n",
+    "                label=\"Upload CSV / XLSX / PARQUET\",\n",
+    "                file_types=[\".csv\", \".xls\", \".xlsx\", \".parquet\"]\n",
+    "            )\n",
+    "            preset_dropdown = gr.Dropdown(\n",
+    "                label=\"Or choose a preset:\",\n",
+    "                choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "                value=\"stocks_data_noindex.csv\"\n",
+    "            )\n",
+    "\n",
+    "            gr.Markdown(\"## Search / Filter\")\n",
+    "            search_box = gr.Textbox(\n",
+    "                placeholder=\"Type to filter (e.g. 'AMZN')\"\n",
+    "            )\n",
+    "            filter_checkbox = gr.CheckboxGroup(\n",
+    "                choices=[], value=[], label=\"Select which timeseries to show\"\n",
+    "            )\n",
+    "\n",
+    "            with gr.Row():\n",
+    "                check_all_btn = gr.Button(\"✅ Check All\")\n",
+    "                uncheck_all_btn = gr.Button(\"❎ Uncheck All\")\n",
+    "\n",
+    "            plot_button = gr.Button(\"▶️ Plot Forecasts\")\n",
+    "            errbox = gr.Textbox(interactive=False, placeholder=\"\")\n",
+    "\n",
+    "        # Right column: gallery\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Forecast Gallery\")\n",
+    "            gallery = gr.Gallery()\n",
+    "\n",
+    "    names_state = gr.State([])\n",
+    "\n",
+    "    # When file or preset changes, update names\n",
+    "    file_input.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "    preset_dropdown.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "\n",
+    "    # When search term changes, filter names\n",
+    "    search_box.change(\n",
+    "        fn=filter_names,\n",
+    "        inputs=[search_box, names_state],\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # Check All / Uncheck All\n",
+    "    check_all_btn.click(fn=check_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "    uncheck_all_btn.click(fn=uncheck_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "\n",
+    "    # Plot button\n",
+    "    plot_button.click(\n",
+    "        fn=display_filtered_forecast,\n",
+    "        inputs=[file_input, preset_dropdown, filter_checkbox],\n",
+    "        outputs=[gallery, errbox]\n",
+    "    )\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checked, almost ideal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Helper functions (logic unchanged)\n",
+    "# ----------------------------\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "_forecast_tensor = torch.load(\"stocks_data_forecast.pt\")  # shape = (n_series, pred_len, n_q)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return _forecast_tensor\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
+    "    ax.plot(timeseries, color=\"blue\")\n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\")\n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\")\n",
+    "    labels = [f\"Quantile {i}\" for i in range(quantile_predictions.shape[1])]\n",
+    "    ax.legend(labels, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
+    "    plt.tight_layout(rect=[0, 0, 0.85, 1])\n",
+    "    buf = io.BytesIO()\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def load_table(file_path):\n",
+    "    ext = file_path.split(\".\")[-1].lower()\n",
+    "    if ext == \"csv\":\n",
+    "        return pd.read_csv(file_path)\n",
+    "    elif ext in (\"xls\", \"xlsx\"):\n",
+    "        return pd.read_excel(file_path)\n",
+    "    elif ext == \"parquet\":\n",
+    "        return pd.read_parquet(file_path)\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported format. Use CSV, XLS, XLSX, or PARQUET.\")\n",
+    "\n",
+    "def extract_names_and_update(file, preset_filename):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return gr.update(choices=[], value=[]), []\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            names = df.iloc[:, 0].tolist()\n",
+    "        else:\n",
+    "            names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "        return gr.update(choices=names, value=names), names\n",
+    "    except Exception:\n",
+    "        return gr.update(choices=[], value=[]), []\n",
+    "\n",
+    "def filter_names(search_term, all_names):\n",
+    "    if not all_names:\n",
+    "        return gr.update(choices=[], value=[])\n",
+    "    if not search_term:\n",
+    "        return gr.update(choices=all_names, value=all_names)\n",
+    "    lower = search_term.lower()\n",
+    "    filtered = [n for n in all_names if lower in str(n).lower()]\n",
+    "    return gr.update(choices=filtered, value=filtered)\n",
+    "\n",
+    "def check_all(names_list):\n",
+    "    return gr.update(value=names_list)\n",
+    "\n",
+    "def uncheck_all(_):\n",
+    "    return gr.update(value=[])\n",
+    "\n",
+    "def display_filtered_forecast(file, preset_filename, selected_names):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"No file selected.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            all_names = df.iloc[:, 0].tolist()\n",
+    "            data_only = df.iloc[:, 1:].astype(float)\n",
+    "        else:\n",
+    "            all_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "            data_only = df.astype(float)\n",
+    "\n",
+    "        mask = [name in selected_names for name in all_names]\n",
+    "        if not any(mask):\n",
+    "            return [], \"No timeseries chosen to plot.\"\n",
+    "\n",
+    "        filtered_data = data_only.iloc[mask, :].values\n",
+    "        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]\n",
+    "        out = _forecast_tensor[mask]   # slice forecasts to match filtered rows\n",
+    "        inp = torch.tensor(filtered_data)\n",
+    "\n",
+    "        gallery_images = []\n",
+    "        for i in range(inp.shape[0]):\n",
+    "            gallery_images.append(plot_forecast_image(inp[i], out[i], filtered_names[i]))\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Use CSV, XLS, XLSX, or PARQUET.\"\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio layout: two columns + instructions\n",
+    "# ----------------------------\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"# 📈 Stock Forecast Viewer 📊\")\n",
+    "    gr.Markdown(\"Upload data or choose a preset, filter by name, then click Plot.\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        # Left column: controls\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Data Selection\")\n",
+    "            file_input = gr.File(\n",
+    "                label=\"Upload CSV / XLSX / PARQUET\",\n",
+    "                file_types=[\".csv\", \".xls\", \".xlsx\", \".parquet\"]\n",
+    "            )\n",
+    "            preset_dropdown = gr.Dropdown(\n",
+    "                label=\"Or choose a preset:\",\n",
+    "                choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "                value=\"stocks_data_noindex.csv\"\n",
+    "            )\n",
+    "\n",
+    "            gr.Markdown(\"## Search / Filter\")\n",
+    "            search_box = gr.Textbox(placeholder=\"Type to filter (e.g. 'AMZN')\")\n",
+    "            filter_checkbox = gr.CheckboxGroup(\n",
+    "                choices=[], value=[], label=\"Select which timeseries to show\"\n",
+    "            )\n",
+    "\n",
+    "            with gr.Row():\n",
+    "                check_all_btn = gr.Button(\"✅ Check All\")\n",
+    "                uncheck_all_btn = gr.Button(\"❎ Uncheck All\")\n",
+    "\n",
+    "            plot_button = gr.Button(\"▶️ Plot Forecasts\")\n",
+    "            errbox = gr.Textbox(interactive=False, placeholder=\"\")\n",
+    "\n",
+    "        # Right column: gallery + instructions\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Forecast Gallery\")\n",
+    "            gallery = gr.Gallery()\n",
+    "\n",
+    "            # Instruction text below gallery\n",
+    "            gr.Markdown(\n",
+    "                \"\"\"\n",
+    "                **How to format your data:**\n",
+    "                - Your file must be a table (CSV, XLS, XLSX, or Parquet).\n",
+    "                - If you haven't prepared the data, the preset file will be used.\n",
+    "                - **One row per timeseries.** Each row is treated as a separate series.\n",
+    "                - If you want to **name** each series, put the name as the first value in **every** row:\n",
+    "                  - Example (CSV):  \n",
+    "                    `AAPL, 120.5, 121.0, 119.8, ...`  \n",
+    "                    `AMZN, 3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - In that case, the first column is not numeric, so it will be used as the series name.\n",
+    "                - If you do **not** want named series, simply leave out the first column entirely and have all values numeric:\n",
+    "                  - Example:  \n",
+    "                    `120.5, 121.0, 119.8, ...`  \n",
+    "                    `3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - Then every row will be auto-named “Series 0, Series 1, …” in order.\n",
+    "                - **Consistency rule:** Either all rows have a non-numeric first entry for the name, or none do. Do not mix.\n",
+    "                - The rest of the columns (after the optional name) must be numeric data points for that series.\n",
+    "                - You can filter by typing in the search box. Then check or uncheck individual names before plotting.\n",
+    "                - Use “Check All” / “Uncheck All” to quickly select or deselect every series.\n",
+    "                - Finally, click **Plot Forecasts** to view the quantile forecast for each selected series.\n",
+    "                \"\"\"\n",
+    "            )\n",
+    "\n",
+    "    names_state = gr.State([])\n",
+    "\n",
+    "    # When file or preset changes, update names\n",
+    "    file_input.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "    preset_dropdown.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "\n",
+    "    # When search term changes, filter names\n",
+    "    search_box.change(\n",
+    "        fn=filter_names,\n",
+    "        inputs=[search_box, names_state],\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # Check All / Uncheck All\n",
+    "    check_all_btn.click(fn=check_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "    uncheck_all_btn.click(fn=uncheck_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "\n",
+    "    # Plot button\n",
+    "    plot_button.click(\n",
+    "        fn=display_filtered_forecast,\n",
+    "        inputs=[file_input, preset_dropdown, filter_checkbox],\n",
+    "        outputs=[gallery, errbox]\n",
+    "    )\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The default choice isn't processed when the default choice is chosen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Helper functions (logic unchanged)\n",
+    "# ----------------------------\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "_forecast_tensor = torch.load(\"stocks_data_forecast.pt\")  # shape = (n_series, pred_len, n_q)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return _forecast_tensor\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=150)\n",
+    "    ax.plot(timeseries, color=\"blue\")\n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\")\n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\")\n",
+    "    labels = [f\"Quantile {i}\" for i in range(quantile_predictions.shape[1])]\n",
+    "    ax.legend(labels, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
+    "    plt.tight_layout(rect=[0, 0, 0.85, 1])\n",
+    "    buf = io.BytesIO()\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def load_table(file_path):\n",
+    "    ext = file_path.split(\".\")[-1].lower()\n",
+    "    if ext == \"csv\":\n",
+    "        return pd.read_csv(file_path)\n",
+    "    elif ext in (\"xls\", \"xlsx\"):\n",
+    "        return pd.read_excel(file_path)\n",
+    "    elif ext == \"parquet\":\n",
+    "        return pd.read_parquet(file_path)\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported format. Use CSV, XLS, XLSX, or PARQUET.\")\n",
+    "\n",
+    "def extract_names_and_update(file, preset_filename):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return gr.update(choices=[], value=[]), []\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            names = df.iloc[:, 0].tolist()\n",
+    "        else:\n",
+    "            names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "        return gr.update(choices=names, value=names), names\n",
+    "    except Exception:\n",
+    "        return gr.update(choices=[], value=[]), []\n",
+    "\n",
+    "def filter_names(search_term, all_names):\n",
+    "    if not all_names:\n",
+    "        return gr.update(choices=[], value=[])\n",
+    "    if not search_term:\n",
+    "        return gr.update(choices=all_names, value=all_names)\n",
+    "    lower = search_term.lower()\n",
+    "    filtered = [n for n in all_names if lower in str(n).lower()]\n",
+    "    return gr.update(choices=filtered, value=filtered)\n",
+    "\n",
+    "def check_all(names_list):\n",
+    "    return gr.update(value=names_list)\n",
+    "\n",
+    "def uncheck_all(_):\n",
+    "    return gr.update(value=[])\n",
+    "\n",
+    "def display_filtered_forecast(file, preset_filename, selected_names):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"No file selected.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            all_names = df.iloc[:, 0].tolist()\n",
+    "            data_only = df.iloc[:, 1:].astype(float)\n",
+    "        else:\n",
+    "            all_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "            data_only = df.astype(float)\n",
+    "\n",
+    "        mask = [name in selected_names for name in all_names]\n",
+    "        if not any(mask):\n",
+    "            return [], \"No timeseries chosen to plot.\"\n",
+    "\n",
+    "        filtered_data = data_only.iloc[mask, :].values\n",
+    "        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]\n",
+    "        out = _forecast_tensor[mask]   # slice forecasts to match filtered rows\n",
+    "        inp = torch.tensor(filtered_data)\n",
+    "\n",
+    "        gallery_images = []\n",
+    "        for i in range(inp.shape[0]):\n",
+    "            gallery_images.append(plot_forecast_image(inp[i], out[i], filtered_names[i]))\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Use CSV, XLS, XLSX, or PARQUET.\"\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio layout: two columns + instructions\n",
+    "# ----------------------------\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"# 📈 Stock Forecast Viewer 📊\")\n",
+    "    gr.Markdown(\"Upload data or choose a preset, filter by name, then click Plot.\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        # Left column: controls\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Data Selection\")\n",
+    "            gr.Markdown(\"*If you haven't prepared the data, the preset file will be used.*\")\n",
+    "            file_input = gr.File(\n",
+    "                label=\"Upload CSV / XLSX / PARQUET\",\n",
+    "                file_types=[\".csv\", \".xls\", \".xlsx\", \".parquet\"]\n",
+    "            )\n",
+    "            preset_dropdown = gr.Dropdown(\n",
+    "                label=\"Or choose a preset:\",\n",
+    "                choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "                value=\"stocks_data_noindex.csv\"\n",
+    "            )\n",
+    "\n",
+    "            gr.Markdown(\"## Search / Filter\")\n",
+    "            search_box = gr.Textbox(placeholder=\"Type to filter (e.g. 'AMZN')\")\n",
+    "            filter_checkbox = gr.CheckboxGroup(\n",
+    "                choices=[], value=[], label=\"Select which timeseries to show\"\n",
+    "            )\n",
+    "\n",
+    "            with gr.Row():\n",
+    "                check_all_btn = gr.Button(\"✅ Check All\")\n",
+    "                uncheck_all_btn = gr.Button(\"❎ Uncheck All\")\n",
+    "\n",
+    "            plot_button = gr.Button(\"▶️ Plot Forecasts\")\n",
+    "            errbox = gr.Textbox(label=\"Error Message\", interactive=False)\n",
+    "\n",
+    "        # Right column: gallery + instructions\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Forecast Gallery\")\n",
+    "            gallery = gr.Gallery()\n",
+    "\n",
+    "            # Instruction text below gallery\n",
+    "            gr.Markdown(\n",
+    "                \"\"\"\n",
+    "                **How to format your data:**\n",
+    "                - Your file must be a table (CSV, XLS, XLSX, or Parquet).\n",
+    "                - **One row per timeseries.** Each row is treated as a separate series.\n",
+    "                - If you want to **name** each series, put the name as the first value in **every** row:\n",
+    "                  - Example (CSV):  \n",
+    "                    `AAPL, 120.5, 121.0, 119.8, ...`  \n",
+    "                    `AMZN, 3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - In that case, the first column is not numeric, so it will be used as the series name.\n",
+    "                - If you do **not** want named series, simply leave out the first column entirely and have all values numeric:\n",
+    "                  - Example:  \n",
+    "                    `120.5, 121.0, 119.8, ...`  \n",
+    "                    `3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - Then every row will be auto-named “Series 0, Series 1, …” in order.\n",
+    "                - **Consistency rule:** Either all rows have a non-numeric first entry for the name, or none do. Do not mix.\n",
+    "                - The rest of the columns (after the optional name) must be numeric data points for that series.\n",
+    "                - You can filter by typing in the search box. Then check or uncheck individual names before plotting.\n",
+    "                - Use “Check All” / “Uncheck All” to quickly select or deselect every series.\n",
+    "                - Finally, click **Plot Forecasts** to view the quantile forecast for each selected series.\n",
+    "                \"\"\"\n",
+    "            )\n",
+    "\n",
+    "    names_state = gr.State([])\n",
+    "\n",
+    "    # When file or preset changes, update names\n",
+    "    file_input.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "    preset_dropdown.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "\n",
+    "    # When search term changes, filter names\n",
+    "    search_box.change(\n",
+    "        fn=filter_names,\n",
+    "        inputs=[search_box, names_state],\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # Check All / Uncheck All\n",
+    "    check_all_btn.click(fn=check_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "    uncheck_all_btn.click(fn=uncheck_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "\n",
+    "    # Plot button\n",
+    "    plot_button.click(\n",
+    "        fn=display_filtered_forecast,\n",
+    "        inputs=[file_input, preset_dropdown, filter_checkbox],\n",
+    "        outputs=[gallery, errbox]\n",
+    "    )\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Default choice - None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Helper functions (logic unchanged)\n",
+    "# ----------------------------\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "_forecast_tensor = torch.load(\"stocks_data_forecast.pt\")  # shape = (n_series, pred_len, n_q)\n",
+    "\n",
+    "def model_forecast(input_data):\n",
+    "    return _forecast_tensor\n",
+    "\n",
+    "def plot_forecast_image(timeseries, quantile_predictions, timeseries_name):\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=300)\n",
+    "    \n",
+    "    # Plot the original timeseries with thicker line and marker\n",
+    "    ax.plot(timeseries, color=\"blue\", linewidth=2.5, marker='o', label=\"Given Data\")\n",
+    "    \n",
+    "    x_pred = range(len(timeseries) - 1, len(timeseries) - 1 + len(quantile_predictions))\n",
+    "    # Use distinct colors with higher alpha for smoothness\n",
+    "    for i in range(quantile_predictions.shape[1]):\n",
+    "        ax.plot(x_pred, quantile_predictions[:, i], color=f\"C{i}\", linewidth=2, alpha=0.8, label=f\"Quantile {i+1}\")\n",
+    "    \n",
+    "    ax.set_title(f\"Timeseries: {timeseries_name}\", fontsize=16, fontweight='bold')\n",
+    "    ax.set_xlabel(\"Time\", fontsize=12)\n",
+    "    ax.set_ylabel(\"Value\", fontsize=12)\n",
+    "    \n",
+    "    ax.grid(True, which='both', linestyle='--', linewidth=0.7, alpha=0.6)\n",
+    "    ax.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), fontsize=10, frameon=True, shadow=True)\n",
+    "    \n",
+    "    plt.tight_layout(rect=[0, 0, 0.82, 1])\n",
+    "    \n",
+    "    buf = io.BytesIO()\n",
+    "    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\", transparent=True)\n",
+    "    plt.close(fig)\n",
+    "    buf.seek(0)\n",
+    "    img = Image.open(buf).convert(\"RGB\")\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def load_table(file_path):\n",
+    "    ext = file_path.split(\".\")[-1].lower()\n",
+    "    if ext == \"csv\":\n",
+    "        return pd.read_csv(file_path)\n",
+    "    elif ext in (\"xls\", \"xlsx\"):\n",
+    "        return pd.read_excel(file_path)\n",
+    "    elif ext == \"parquet\":\n",
+    "        return pd.read_parquet(file_path)\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported format. Use CSV, XLS, XLSX, or PARQUET.\")\n",
+    "\n",
+    "def extract_names_and_update(file, preset_filename):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return gr.update(choices=[], value=[]), []\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            names = df.iloc[:, 0].tolist()\n",
+    "        else:\n",
+    "            names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "        return gr.update(choices=names, value=names), names\n",
+    "    except Exception:\n",
+    "        return gr.update(choices=[], value=[]), []\n",
+    "\n",
+    "def filter_names(search_term, all_names):\n",
+    "    if not all_names:\n",
+    "        return gr.update(choices=[], value=[])\n",
+    "    if not search_term:\n",
+    "        return gr.update(choices=all_names, value=all_names)\n",
+    "    lower = search_term.lower()\n",
+    "    filtered = [n for n in all_names if lower in str(n).lower()]\n",
+    "    return gr.update(choices=filtered, value=filtered)\n",
+    "\n",
+    "def check_all(names_list):\n",
+    "    return gr.update(value=names_list)\n",
+    "\n",
+    "def uncheck_all(_):\n",
+    "    return gr.update(value=[])\n",
+    "\n",
+    "def display_filtered_forecast(file, preset_filename, selected_names):\n",
+    "    try:\n",
+    "        if file is not None:\n",
+    "            df = load_table(file.name)\n",
+    "        else:\n",
+    "            if not preset_filename:\n",
+    "                return [], \"No file selected.\"\n",
+    "            df = load_table(preset_filename)\n",
+    "\n",
+    "        if df.shape[1] > 0 and df.iloc[:, 0].dtype == object and not df.iloc[:, 0].str.isnumeric().all():\n",
+    "            all_names = df.iloc[:, 0].tolist()\n",
+    "            data_only = df.iloc[:, 1:].astype(float)\n",
+    "        else:\n",
+    "            all_names = [f\"Series {i}\" for i in range(len(df))]\n",
+    "            data_only = df.astype(float)\n",
+    "\n",
+    "        mask = [name in selected_names for name in all_names]\n",
+    "        if not any(mask):\n",
+    "            return [], \"No timeseries chosen to plot.\"\n",
+    "\n",
+    "        filtered_data = data_only.iloc[mask, :].values\n",
+    "        filtered_names = [all_names[i] for i, m in enumerate(mask) if m]\n",
+    "        out = _forecast_tensor[mask]   # slice forecasts to match filtered rows\n",
+    "        inp = torch.tensor(filtered_data)\n",
+    "\n",
+    "        gallery_images = []\n",
+    "        for i in range(inp.shape[0]):\n",
+    "            gallery_images.append(plot_forecast_image(inp[i], out[i], filtered_names[i]))\n",
+    "\n",
+    "        return gallery_images, \"\"\n",
+    "    except Exception as e:\n",
+    "        return [], f\"Error: {e}. Use CSV, XLS, XLSX, or PARQUET.\"\n",
+    "\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Gradio layout: two columns + instructions\n",
+    "# ----------------------------\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"# 📈 Stock Forecast Viewer 📊\")\n",
+    "    gr.Markdown(\"Upload data or choose a preset, filter by name, then click Plot.\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        # Left column: controls\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Data Selection\")\n",
+    "            file_input = gr.File(\n",
+    "                label=\"Upload CSV / XLSX / PARQUET\",\n",
+    "                file_types=[\".csv\", \".xls\", \".xlsx\", \".parquet\"]\n",
+    "            )\n",
+    "            preset_dropdown = gr.Dropdown(\n",
+    "                label=\"Or choose a preset:\",\n",
+    "                choices=[\"stocks_data_noindex.csv\", \"stocks_data.csv\"],\n",
+    "                value=\"No file selected\"\n",
+    "            )\n",
+    "\n",
+    "            gr.Markdown(\"## Search / Filter\")\n",
+    "            search_box = gr.Textbox(placeholder=\"Type to filter (e.g. 'AMZN')\")\n",
+    "            filter_checkbox = gr.CheckboxGroup(\n",
+    "                choices=[], value=[], label=\"Select which timeseries to show\"\n",
+    "            )\n",
+    "\n",
+    "            with gr.Row():\n",
+    "                check_all_btn = gr.Button(\"✅ Check All\")\n",
+    "                uncheck_all_btn = gr.Button(\"❎ Uncheck All\")\n",
+    "\n",
+    "            plot_button = gr.Button(\"▶️ Plot Forecasts\")\n",
+    "            errbox = gr.Textbox(label=\"Error Message\", interactive=False)\n",
+    "\n",
+    "        # Right column: gallery + instructions\n",
+    "        with gr.Column():\n",
+    "            gr.Markdown(\"## Forecast Gallery\")\n",
+    "            gallery = gr.Gallery()\n",
+    "\n",
+    "            # Instruction text below gallery\n",
+    "            gr.Markdown(\"## Instructions\")\n",
+    "            gr.Markdown(\n",
+    "                \"\"\"\n",
+    "                **How to format your data:**\n",
+    "                - Your file must be a table (CSV, XLS, XLSX, or Parquet).\n",
+    "                - **One row per timeseries.** Each row is treated as a separate series.\n",
+    "                - If you want to **name** each series, put the name as the first value in **every** row:\n",
+    "                  - Example (CSV):  \n",
+    "                    `AAPL, 120.5, 121.0, 119.8, ...`  \n",
+    "                    `AMZN, 3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - In that case, the first column is not numeric, so it will be used as the series name.\n",
+    "                - If you do **not** want named series, simply leave out the first column entirely and have all values numeric:\n",
+    "                  - Example:  \n",
+    "                    `120.5, 121.0, 119.8, ...`  \n",
+    "                    `3300.0, 3310.5, 3295.2, ...`  \n",
+    "                  - Then every row will be auto-named “Series 0, Series 1, …” in order.\n",
+    "                - **Consistency rule:** Either all rows have a non-numeric first entry for the name, or none do. Do not mix.\n",
+    "                - The rest of the columns (after the optional name) must be numeric data points for that series.\n",
+    "                - You can filter by typing in the search box. Then check or uncheck individual names before plotting.\n",
+    "                - Use “Check All” / “Uncheck All” to quickly select or deselect every series.\n",
+    "                - Finally, click **Plot Forecasts** to view the quantile forecast for each selected series.\n",
+    "                \"\"\"\n",
+    "            )\n",
+    "\n",
+    "    names_state = gr.State([])\n",
+    "\n",
+    "    # When file or preset changes, update names\n",
+    "    file_input.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "    preset_dropdown.change(\n",
+    "        fn=extract_names_and_update,\n",
+    "        inputs=[file_input, preset_dropdown],\n",
+    "        outputs=[filter_checkbox, names_state]\n",
+    "    )\n",
+    "\n",
+    "    # When search term changes, filter names\n",
+    "    search_box.change(\n",
+    "        fn=filter_names,\n",
+    "        inputs=[search_box, names_state],\n",
+    "        outputs=filter_checkbox\n",
+    "    )\n",
+    "\n",
+    "    # Check All / Uncheck All\n",
+    "    check_all_btn.click(fn=check_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "    uncheck_all_btn.click(fn=uncheck_all, inputs=names_state, outputs=filter_checkbox)\n",
+    "\n",
+    "    # Plot button\n",
+    "    plot_button.click(\n",
+    "        fn=display_filtered_forecast,\n",
+    "        inputs=[file_input, preset_dropdown, filter_checkbox],\n",
+    "        outputs=[gallery, errbox]\n",
+    "    )\n",
+    "\n",
+    "demo.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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+Subproject commit 341adb31e1f727d181682993db223bf249d9aa9a