Update app.py

app.py

@@ -7,6 +7,7 @@ import requests
 from sklearn.ensemble import RandomForestClassifier
 from textblob import TextBlob
 import yfinance as yf
+import re
 
 # --- Constants ---
 CRYPTO_SYMBOLS = ["BTC-USD", "ETH-USD", "LTC-USD", "XRP-USD"]
@@ -302,59 +303,93 @@ def analyze_market(market_type, symbol, interval, forecast_steps, daily_seasonal
     forecast_df_display.rename(columns={"ds": "Date", "yhat": "Forecast", "yhat_lower": "Lower Bound", "yhat_upper": "Upper Bound"}, inplace=True)
     return forecast_plot, tech_plot, rsi_plot, macd_plot, forecast_df_display, growth_label, error_message, sentiment_score
 
-# --- Gradio Interface ---
-with gr.Blocks(theme=gr.themes.Base()) as demo:
-    gr.Markdown("# Market Analysis and Prediction")
-
-    with gr.Row():
-        with gr.Column():
-            market_type_dd = gr.Radio(label="Market Type", choices=["Crypto", "Stock"], value="Crypto")
-            symbol_dd = gr.Dropdown(label="Symbol", choices=CRYPTO_SYMBOLS, value="BTC-USD")
-            interval_dd = gr.Dropdown(label="Interval", choices=INTERVAL_OPTIONS, value="1h")
-            forecast_steps_slider = gr.Slider(label="Forecast Steps", minimum=1, maximum=100, value=DEFAULT_FORECAST_STEPS, step=1)
-            daily_box = gr.Checkbox(label="Daily Seasonality", value=DEFAULT_DAILY_SEASONALITY)
-            weekly_box = gr.Checkbox(label="Weekly Seasonality", value=DEFAULT_WEEKLY_SEASONALITY)
-            yearly_box = gr.Checkbox(label="Yearly Seasonality", value=DEFAULT_YEARLY_SEASONALITY)
-            seasonality_mode_dd = gr.Dropdown(label="Seasonality Mode", choices=["additive", "multiplicative"], value=DEFAULT_SEASONALITY_MODE)
-            changepoint_scale_slider = gr.Slider(label="Changepoint Prior Scale", minimum=0.01, maximum=1.0, step=0.01, value=DEFAULT_CHANGEPOINT_PRIOR_SCALE)
-            sentiment_keyword_txt = gr.Textbox(label="Sentiment Keyword (optional)")
-
-        with gr.Column():
-            forecast_plot = gr.Plot(label="Price Forecast")
-            with gr.Row():
-                tech_plot = gr.Plot(label="Technical Analysis")
-                rsi_plot = gr.Plot(label="RSI Indicator")
-            with gr.Row():
-                macd_plot = gr.Plot(label="MACD")
-            forecast_df = gr.Dataframe(label="Forecast Data", headers=["Date", "Forecast", "Lower Bound", "Upper Bound"])
-            growth_label_output = gr.Label(label="Explosive Growth Prediction")
-            sentiment_label_output = gr.Number(label="Sentiment Score")
-
-    def update_symbol_choices(market_type):
-        if market_type == "Crypto":
-            return gr.Dropdown(choices=CRYPTO_SYMBOLS, value="BTC-USD")
-        elif market_type == "Stock":
-            return gr.Dropdown(choices=STOCK_SYMBOLS, value="AAPL")
-        return gr.Dropdown(choices=[], value=None)
-    market_type_dd.change(fn=update_symbol_choices, inputs=[market_type_dd], outputs=[symbol_dd])
-
-    analyze_button = gr.Button("Analyze Market", variant="primary")
-    analyze_button.click(
-        fn=analyze_market,
-        inputs=[
-            market_type_dd,
-            symbol_dd,
-            interval_dd,
-            forecast_steps_slider,
-            daily_box,
-            weekly_box,
-            yearly_box,
-            seasonality_mode_dd,
-            changepoint_scale_slider,
-            sentiment_keyword_txt,
-        ],
-        outputs=[forecast_plot, tech_plot, rsi_plot, macd_plot, forecast_df, growth_label_output, gr.Label(label="Error Message"), sentiment_label_output]
+def chatbot_response(message, history):
+    market_type = "Crypto"  # Default market type
+    symbol = "BTC-USD"      # Default symbol
+    interval = "1h"         # Default interval
+    forecast_steps = DEFAULT_FORECAST_STEPS
+    daily_seasonality = DEFAULT_DAILY_SEASONALITY
+    weekly_seasonality = DEFAULT_WEEKLY_SEASONALITY
+    yearly_seasonality = DEFAULT_YEARLY_SEASONALITY
+    seasonality_mode = DEFAULT_SEASONALITY_MODE
+    changepoint_prior_scale = DEFAULT_CHANGEPOINT_PRIOR_SCALE
+    sentiment_keyword = ""
+
+    # Simple keyword parsing - improve this for more robust parsing
+    message_lower = message.lower()
+    if "stock" in message_lower:
+        market_type = "Stock"
+        symbol = "AAPL" # Default stock symbol
+    elif "crypto" in message_lower:
+        market_type = "Crypto"
+        symbol = "BTC-USD" # Default crypto symbol
+
+    for crypto_sym in CRYPTO_SYMBOLS:
+        if crypto_sym.lower() in message_lower:
+            symbol = crypto_sym
+            market_type = "Crypto"
+            break
+    for stock_sym in STOCK_SYMBOLS:
+        if stock_sym.lower() in message_lower:
+            symbol = stock_sym
+            market_type = "Stock"
+            break
+
+    for intv in INTERVAL_OPTIONS:
+        if intv in message_lower:
+            interval = intv
+            break
+
+    forecast_steps_match = re.search(r'forecast\s*(\d+)\s*steps', message_lower)
+    if forecast_steps_match:
+        forecast_steps = int(forecast_steps_match.group(1))
+
+    sentiment_match = re.search(r'sentiment\s*(.+)', message_lower)
+    if sentiment_match:
+        sentiment_keyword = sentiment_match.group(1).strip()
+
+    plots, tech_plot, rsi_plot, macd_plot, forecast_df, growth_label, error_message, sentiment_score = analyze_market(
+        market_type, symbol, interval, forecast_steps, daily_seasonality, weekly_seasonality, yearly_seasonality, seasonality_mode, changepoint_prior_scale, sentiment_keyword
     )
 
-if __name__ == "__main__":
+    response = ""
+    if error_message:
+        response += f"Error: {error_message}\n\n"
+
+    if plots and not error_message:
+        response += "Here is the price forecast plot.\n\n" # In a real chatbot, you might provide a link or embed the plot if possible.
+    else:
+        response += "Could not generate forecast plot.\n\n"
+
+    if tech_plot and rsi_plot and macd_plot and not error_message:
+        response += "Technical analysis plots (Bollinger Bands, RSI, MACD) are generated.\n\n" # Again, link or embed plots in a real chatbot
+    else:
+        response += "Could not generate technical analysis plots.\n\n"
+
+    if not error_message:
+        response += f"Explosive Growth Prediction: {growth_label}\n"
+        response += f"Sentiment Score (for keyword '{sentiment_keyword}'): {sentiment_score:.2f}\n"
+
+        if not forecast_df.empty:
+            # Summarize forecast data instead of displaying the full dataframe in text
+            forecast_summary = forecast_df.tail().to_string() # Just showing last few rows as summary
+            response += "\nForecast Data Summary (last few points):\n" + forecast_summary + "\n"
+        else:
+            response += "\nNo forecast data available.\n"
+
+    return response
+
+with gr.ChatInterface(
+    chatbot_response,
+    title="Market Analysis Chatbot",
+    description="Ask me about crypto or stock market analysis. For example, try: 'Analyze crypto BTC-USD 1d forecast 30 steps sentiment Bitcoin' or 'Stock AAPL 1h analysis'.",
+    examples=[
+        "Analyze crypto ETH-USD 1h",
+        "Stock MSFT 1d forecast 10 steps",
+        "Crypto LTC-USD 1wk sentiment Litecoin",
+        "Analyze stock GOOGL",
+        "What about crypto XRP-USD?",
+    ],
+    theme=gr.themes.Base()
+) as demo:
     demo.launch()