Add Default time series

app.py

@@ -31,6 +31,14 @@ model.eval()
 
 # ─── HELPER FUNCTIONS ──────────────────────────────────────────────────────────
 
+def create_default_timeseries():
+    """Create default time series with sudden increase"""
+    seq_len = 256
+    y = np.zeros(seq_len, dtype=np.float32)
+    y[100:] += 1
+    df = pd.DataFrame({"default_series": y})
+    return df
+
 def process_csv_file(csv_file):
     """Process CSV file and return DataFrame with validation"""
     if csv_file is None:
@@ -81,25 +89,26 @@ def process_csv_file(csv_file):
     except Exception as e:
         return None, f"Error processing file: {str(e)}"
 
+def get_current_data(csv_file):
+    """Get current data (either uploaded CSV or default)"""
+    if csv_file is None:
+        return create_default_timeseries(), "Using default time series with sudden increase at step 100"
+    else:
+        return process_csv_file(csv_file)
+
 def preview_csv(csv_file):
     """Preview uploaded CSV file immediately"""
-    if csv_file is None:
-        return gr.LinePlot(), "Please upload a CSV file first", gr.Dropdown()
-    
-    df, message = process_csv_file(csv_file)
+    df, message = get_current_data(csv_file)
     
     if df is None:
-        return gr.LinePlot(), message, gr.Dropdown()
-    
-    # Add index as x-axis
-    df_with_index = df.copy()
-    # df_with_index["_chatts_internal_index"] = np.
+        return gr.LinePlot(value=pd.DataFrame()), message, gr.Dropdown()
     
     # Create dropdown choices
     column_choices = list(df.columns)
     
     # Create plot with first column as default
     first_column = column_choices[0]
+    df_with_index = df.copy()
     df_with_index["_internal_idx"] = np.arange(len(df[first_column].values))
     plot = gr.LinePlot(
         df_with_index,
@@ -115,16 +124,16 @@ def preview_csv(csv_file):
         label="Select Time Series"
     )
     
-    print("Successfully generated preview_csv!")
+    print("Successfully generated preview!")
     
     return plot, message, dropdown
 
 def update_plot(csv_file, selected_column):
     """Update plot based on selected column"""
-    if csv_file is None or selected_column is None:
-        return gr.LinePlot()
+    if selected_column is None:
+        return gr.LinePlot(value=pd.DataFrame())
     
-    df, _ = process_csv_file(csv_file)
+    df, _ = get_current_data(csv_file)
     if df is None:
         return gr.LinePlot(value=pd.DataFrame())
     
@@ -140,6 +149,32 @@ def update_plot(csv_file, selected_column):
     
     return plot
 
+def initialize_interface():
+    """Initialize interface with default time series"""
+    df = create_default_timeseries()
+    column_choices = list(df.columns)
+    first_column = column_choices[0]
+    
+    df_with_index = df.copy()
+    df_with_index["_internal_idx"] = np.arange(len(df[first_column].values))
+    
+    plot = gr.LinePlot(
+        df_with_index,
+        x="_internal_idx",
+        y=first_column,
+        title=f"Time Series: {first_column}"
+    )
+    
+    dropdown = gr.Dropdown(
+        choices=column_choices,
+        value=first_column,
+        label="Select Time Series"
+    )
+    
+    message = "Using default time series with sudden increase at step 100"
+    
+    return plot, message, dropdown
+
 # ─── INFERENCE + VALIDATION ────────────────────────────────────────────────────
 
 @spaces.GPU  # dynamically allocate & release a ZeroGPU device on each call
@@ -148,16 +183,13 @@ def infer_chatts_stream(prompt: str, csv_file):
     Streaming version of ChatTS inference
     """
     print("Start inferring!!!")
-    if csv_file is None:
-        yield "Please upload a CSV file first"
-        return
         
     if not prompt.strip():
         yield "Please enter a prompt"
         return
     
-    # Process CSV file
-    df, error_msg = process_csv_file(csv_file)
+    # Get current data (CSV or default)
+    df, error_msg = get_current_data(csv_file)
     if df is None:
         yield f"Error: {error_msg}"
         return
@@ -210,20 +242,6 @@ def infer_chatts_stream(prompt: str, csv_file):
             model_output += new_text
             yield model_output
         
-        # # Decode the generated text
-        # full_generated = tokenizer.decode(
-        #     outputs[0][inputs["input_ids"].shape[-1]:],
-        #     skip_special_tokens=True
-        # )
-        
-        # # Simulate streaming by yielding character by character
-        # for i, char in enumerate(full_generated):
-        #     generated_text += char
-        #     if i % 5 == 0:  # Update every 5 characters for smoother streaming
-        #         yield generated_text
-                    
-        # yield generated_text
-        
     except Exception as e:
         yield f"Error during inference: {str(e)}"
 
@@ -231,7 +249,7 @@ def infer_chatts_stream(prompt: str, csv_file):
 
 with gr.Blocks(title="ChatTS Demo") as demo:
     gr.Markdown("## ChatTS Demo: Time Series Understanding and Reasoning")
-    gr.Markdown("Upload a CSV file where each column is a time series. All columns will be treated as time series data.")
+    gr.Markdown("Upload a CSV file where each column is a time series, or use the default time series with sudden increase. All columns will be treated as time series data.")
 
     with gr.Row():
         with gr.Column(scale=1):
@@ -245,7 +263,7 @@ with gr.Blocks(title="ChatTS Demo") as demo:
                 lines=4,
                 placeholder="Enter your analysis prompt here...",
                 label="Analysis Prompt",
-                value="Please analyze these time series and provide insights about trends, patterns, and anomalies."
+                value="Please analyze this time series and provide insights about the trends, seasonality, and local fluctuations."
             )
             
             run_btn = gr.Button("Run ChatTS", variant="primary")
@@ -263,13 +281,18 @@ with gr.Blocks(title="ChatTS Demo") as demo:
                 lines=2
             )
 
-
     text_out = gr.Textbox(
         lines=10, 
         label="ChatTS Analysis Results",
         interactive=False
     )
 
+    # Initialize interface with default data
+    demo.load(
+        fn=initialize_interface,
+        outputs=[plot_out, file_status, series_selector]
+    )
+
     # Event handlers
     upload.upload(
         fn=preview_csv,