Update app.py

app.py

@@ -3,28 +3,50 @@ from bs4 import BeautifulSoup
 import pandas as pd
 from io import StringIO
 from datetime import datetime, timedelta
-from openai import OpenAI
-import gradio as gr
 
+from langchain.tools import tool
+from langchain.agents import initialize_agent, Tool
+from langchain_openai import ChatOpenAI
+
+# === Configuration ===
 OPENROUTER_API_KEY = "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"
 
 HEADERS = {
-    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
+    'User-Agent': (
+        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
+        'AppleWebKit/537.36 (KHTML, like Gecko) '
+        'Chrome/124.0.0.0 Safari/537.36'
+    ),
+    'Accept': (
+        'text/html,application/xhtml+xml,application/xml;'
+        'q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8'
+    ),
     'Accept-Language': 'en-US,en;q=0.9',
-    'Referer': 'https://www.google.com/'
+    'Referer': 'https://www.google.com/',
+    'Connection': 'keep-alive',
+    'Cache-Control': 'max-age=0',
+    'Upgrade-Insecure-Requests': '1'
 }
 
-def get_current_price(symbol: str):
+@tool("GetCurrentPrice")
+def get_current_price(symbol: str) -> str:
+    """Get the current price for a stock symbol (e.g., 'AAPL')."""
     url = f'https://www.marketwatch.com/investing/stock/{symbol}'
     response = requests.get(url, headers=HEADERS)
     if response.status_code == 200:
         soup = BeautifulSoup(response.text, 'html.parser')
         price_tag = soup.find('bg-quote', class_='value')
         if price_tag:
-            return price_tag.get_text(strip=True)
-    return None
-
-def get_historical_data(symbol: str, days=30):
+            price = price_tag.get_text(strip=True)
+            return f"{symbol.upper()} current price: ${price} (from MarketWatch)"
+        else:
+            return "Stock price not found."
+    else:
+        return f"Failed to retrieve stock page. Status code: {response.status_code}"
+
+@tool("GetHistoricalData")
+def get_historical_data(symbol: str, days: int = 5000) -> str:
+    """Get the last N days of historical OHLCV data for a stock symbol as CSV."""
     end_date = datetime.now()
     start_date = end_date - timedelta(days=days)
     start_date_str = start_date.strftime('%m/%d/%Y 00:00:00')
@@ -39,12 +61,15 @@ def get_historical_data(symbol: str, days=30):
     if response.status_code == 200:
         try:
             df = pd.read_csv(StringIO(response.text))
-            return df
-        except Exception:
-            return None
-    return None
-
-def get_technical_analysis_docs():
+            return f"Historical data for {symbol.upper()} (from MarketWatch):\n" + df.head(10).to_csv(index=False)
+        except Exception as e:
+            return f"Failed to parse CSV data: {e}"
+    else:
+        return f"Failed to download historical data. Status code: {response.status_code}"
+
+@tool("GetTechnicalAnalysisDocs")
+def get_technical_analysis_docs(_: str = "") -> str:
+    """Get documentation for technical analysis indicators."""
     return """
 Technical Analysis: Core Concepts & Formulas
 -------------------------------------------
@@ -119,72 +144,45 @@ Key Technical Indicators and Their Formulas:
    - Hammer, Shooting Star, Engulfing, Doji, Morning/Evening Star, etc.
 """
 
-def analyze(symbol):
-    symbol = symbol.strip().lower()
-    current_price = get_current_price(symbol)
-    historical_df = get_historical_data(symbol, days=30)
-    if current_price is None or historical_df is None:
-        return "Could not retrieve data for this symbol. Try another."
-    historical_csv_snippet = historical_df.to_csv(index=False)
-    prompt = f"""
-You are a financial LLM trained in technical analysis. Your task is to predict the next day's closing price for the stock symbol '{symbol.upper()}'.
-
-The current stock price is approximately: ${current_price if current_price else 'N/A'}
-
-Below is the historical OHLCV data for the last 10 days (CSV format):
-{historical_csv_snippet if historical_csv_snippet else 'No historical data available.'}
-
----
-
-**Technical Analysis Documentation:**
-{get_technical_analysis_docs()}
-
-**Instructions:**
-You must perform the entire analysis in one go — do not ask for any further data or clarification.
-Perform the full process:
-1. Review the historical OHLCV data provided.
-2. Perform a rigorous, step-by-step technical analysis using the full range of indicators.
-3. Identify trends, chart patterns, volume analysis, risk management.
-4. Predict the next day’s closing price.
-5. Justify your prediction with detailed reasoning.
----
-**Step-by-Step Analysis Plan:**
-1. Data Review and Preparation  
-2. Chart and Trend Analysis  
-3. Apply Core Technical Indicators  
-4. Pattern Recognition  
-5. Advanced Analysis  
-6. Confirmation and Risk Management  
-7. Prediction and Justification  
-8. Review and Backtesting (Optional)
----
-**Output Format:**
-- Predicted Price for Next Day:  
-- Detailed Reasoning:
-  - Trend analysis and key levels  
-  - Indicator values and interpretations  
-  - Patterns identified  
-  - Volume analysis  
-  - Confidence level and uncertainty factors  
-  - Additional insights or warnings
-"""
-    client = OpenAI(
-        base_url="https://openrouter.ai/api/v1",
-        api_key=OPENROUTER_API_KEY,
+tools = [
+    Tool.from_function(
+        get_current_price,
+        name="GetCurrentPrice",
+        description="Get the current price for a stock symbol (e.g., AAPL)"
+    ),
+    Tool.from_function(
+        get_historical_data,
+        name="GetHistoricalData",
+        description="Get the last N days of historical OHLCV data for a stock symbol as CSV"
+    ),
+    Tool.from_function(
+        get_technical_analysis_docs,
+        name="GetTechnicalAnalysisDocs",
+        description="Provide documentation for technical analysis indicators"
     )
-    completion = client.chat.completions.create(
-        model="google/gemma-3n-e4b-it:free",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    return completion.choices[0].message.content
-
-iface = gr.Interface(
-    fn=analyze,
-    inputs=gr.Textbox(label="Stock Symbol (e.g. AAPL, TSLA, QBTS)"),
-    outputs="text",
-    title="Stock Technical Analysis & Prediction",
-    description="Enter a stock symbol to get a technical analysis and next-day price prediction."
+]
+
+llm = ChatOpenAI(
+    openai_api_key=OPENROUTER_API_KEY,
+    openai_api_base="https://openrouter.ai/api/v1",
+    model_name="google/gemma-3n-e4b-it:free"
+)
+
+agent = initialize_agent(
+    tools,
+    llm,
+    agent="zero-shot-react-description",
+    verbose=True  # This will show all reasoning and tool usage!
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    print("Welcome to the Stock Analysis Agent!")
+    print("Ask anything about stocks, technical analysis, or predictions.")
+    print("Type 'exit' to quit.")
+    while True:
+        user_input = input("\nYour question: ")
+        if user_input.lower() in ["exit", "quit"]:
+            print("Goodbye!")
+            break
+        result = agent.run(user_input)
+        print("\nAgent's answer:\n", result)