Update llm.py

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llm.py

@@ -1,220 +1,220 @@
-
-import streamlit as st
-import requests
-from model import fetch_data, calculate_indicators, calculate_support_resistance
-import os
-
-# import argparse
-
-# parser = argparse.ArgumentParser()
-# parser.add_argument('--token', required=True)
-# args = parser.parse_args()
-
-# API_TOKEN = args.token
-# Hugging Face API token and model URL
-# API_TOKEN = os.environ['HUGGING_FACE_TOKEN']
-API_TOKEN ='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'
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B-Instruct"
-
-def generate_prompt(ticker, start_date, end_date):
-    """Fetch data, calculate indicators, and prepare the prompt."""
-    data = fetch_data(ticker, start_date, end_date)
-    
-    if data is None:
-        return "No data available for the given ticker and date range.", None
-    
-    data = calculate_indicators(data)
-    support, resistance = calculate_support_resistance(data)
-    
-    # Additional statistics
-    highest_close = data['Close'].max()
-    lowest_close = data['Close'].min()
-    average_close = data['Close'].mean()
-    average_volume = data['Volume'].mean()
-    highest_volume = data['Volume'].max()
-    lowest_volume = data['Volume'].min()
-    daily_returns = data['Close'].pct_change().dropna()
-    volatility = daily_returns.std()
-    
-    recent_trend = "uptrend" if data['Close'].iloc[-1] > data['Close'].iloc[0] else "downtrend" if data['Close'].iloc[-1] < data['Close'].iloc[0] else "sideways"
-    
-    # Summarize the key statistics
-    summary = {
-        'latest_close': data['Close'].iloc[-1],
-        'SMA_50': data['SMA_50'].iloc[-1],
-        'EMA_50': data['EMA_50'].iloc[-1],
-        'RSI': data['RSI'].iloc[-1],
-        'MACD': data['MACD'].iloc[-1],
-        'MACD_Signal': data['MACD_Signal'].iloc[-1],
-        'Bollinger_High': data['Bollinger_High'].iloc[-1],
-        'Bollinger_Low': data['Bollinger_Low'].iloc[-1],
-        'ATR': data['ATR'].iloc[-1],
-        'OBV': data['OBV'].iloc[-1],
-        'Support': support,
-        'Resistance': resistance,
-        'Highest_Close': highest_close,
-        'Lowest_Close': lowest_close,
-        'Average_Close': average_close,
-        'Average_Volume': average_volume,
-        'Highest_Volume': highest_volume,
-        'Lowest_Volume': lowest_volume,
-        'Volatility': volatility,
-        'Recent_Trend': recent_trend,
-        'Percentage_Change': (data['Close'].iloc[-1] - data['Close'].iloc[-2]) / data['Close'].iloc[-2] * 100 if len(data) > 1 else 0
-    }
-    
-    prompt = f"""
-    Analyze the following stock data for {ticker} and provide a buy/sell recommendation:
-    Latest Close Price: {summary['latest_close']}
-    SMA 50: {summary['SMA_50']}
-    EMA 50: {summary['EMA_50']}
-    RSI: {summary['RSI']}
-    MACD: {summary['MACD']}
-    MACD Signal: {summary['MACD_Signal']}
-    Bollinger Bands High: {summary['Bollinger_High']}
-    Bollinger Bands Low: {summary['Bollinger_Low']}
-    ATR: {summary['ATR']}
-    OBV: {summary['OBV']}
-    Support Level: {summary['Support']}
-    Resistance Level: {summary['Resistance']}
-    Highest Close Price: {summary['Highest_Close']}
-    Lowest Close Price: {summary['Lowest_Close']}
-    Average Close Price: {summary['Average_Close']}
-    Average Volume: {summary['Average_Volume']}
-    Highest Volume: {summary['Highest_Volume']}
-    Lowest Volume: {summary['Lowest_Volume']}
-    Volatility: {summary['Volatility']}
-    Recent Trend: {summary['Recent_Trend']}
-    Percentage Change: {summary['Percentage_Change']}%
-    """
-    
-    return prompt, summary
-
-def get_recommendation(prompt):
-    """Get stock recommendation from Hugging Face API."""
-    headers = {
-        "Authorization": f"Bearer {API_TOKEN}",
-        "Content-Type": "application/json"
-    }
-    payload = {"inputs": prompt}
-    
-    response = requests.post(API_URL, headers=headers, json=payload)
-    response.raise_for_status()  # Raise an error for HTTP issues
-    result = response.json()
-    
-    return result[0]['generated_text'].strip()
-
-def display_recommendation(ticker, start_date, end_date):
-    """Fetch data, generate prompt, get recommendation, and display it in a nice format."""
-    prompt, summary = generate_prompt(ticker, start_date, end_date)
-    
-    if summary is None:
-        st.error(prompt)
-        return
-
-    try:
-        recommendation = get_recommendation(prompt)
-    except Exception as e:
-        st.error(f"An error occurred while getting recommendation: {e}")
-        return 
-
-    # Display in a box/table format using Streamlit
-    st.markdown(f"### Stock Analysis & Recommendation for {ticker}")
-
-    st.markdown(f"""
-    <div style='border:2px solid #4CAF50; padding: 15px; border-radius: 10px;'>
-        <table style='width:100%; border-collapse: collapse;'>
-            <tr>
-                <th style='text-align: left;'>Indicator</th>
-                <th style='text-align: left;'>Value</th>
-            </tr>
-            <tr>
-                <td>Latest Close Price</td>
-                <td>{summary['latest_close']}</td>
-            </tr>
-            <tr>
-                <td>SMA 50</td>
-                <td>{summary['SMA_50']}</td>
-            </tr>
-            <tr>
-                <td>EMA 50</td>
-                <td>{summary['EMA_50']}</td>
-            </tr>
-            <tr>
-                <td>RSI</td>
-                <td>{summary['RSI']}</td>
-            </tr>
-            <tr>
-                <td>MACD</td>
-                <td>{summary['MACD']}</td>
-            </tr>
-            <tr>
-                <td>MACD Signal</td>
-                <td>{summary['MACD_Signal']}</td>
-            </tr>
-            <tr>
-                <td>Bollinger Bands High</td>
-                <td>{summary['Bollinger_High']}</td>
-            </tr>
-            <tr>
-                <td>Bollinger Bands Low</td>
-                <td>{summary['Bollinger_Low']}</td>
-            </tr>
-            <tr>
-                <td>ATR</td>
-                <td>{summary['ATR']}</td>
-            </tr>
-            <tr>
-                <td>OBV</td>
-                <td>{summary['OBV']}</td>
-            </tr>
-            <tr>
-                <td>Support Level</td>
-                <td>{summary['Support']}</td>
-            </tr>
-            <tr>
-                <td>Resistance Level</td>
-                <td>{summary['Resistance']}</td>
-            </tr>
-            <tr>
-                <td>Highest Close Price</td>
-                <td>{summary['Highest_Close']}</td>
-            </tr>
-            <tr>
-                <td>Lowest Close Price</td>
-                <td>{summary['Lowest_Close']}</td>
-            </tr>
-            <tr>
-                <td>Average Close Price</td>
-                <td>{summary['Average_Close']}</td>
-            </tr>
-            <tr>
-                <td>Average Volume</td>
-                <td>{summary['Average_Volume']}</td>
-            </tr>
-            <tr>
-                <td>Highest Volume</td>
-                <td>{summary['Highest_Volume']}</td>
-            </tr>
-            <tr>
-                <td>Lowest Volume</td>
-                <td>{summary['Lowest_Volume']}</td>
-            </tr>
-            <tr>
-                <td>Volatility</td>
-                <td>{summary['Volatility']}</td>
-            </tr>
-            <tr>
-                <td>Recent Trend</td>
-                <td>{summary['Recent_Trend']}</td>
-            </tr>
-            <tr>
-                <td>Percentage Change</td>
-                <td>{summary['Percentage_Change']}%</td>
-            </tr>
-        </table>
-    </div>
-    """, unsafe_allow_html=True)
-
-    st.write('AI Recommendation:')
-    st.write(recommendation)
+
+import streamlit as st
+import requests
+from model import fetch_data, calculate_indicators, calculate_support_resistance
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Hugging Face API token and model URL
+# Fetch API token from environment variable (works locally and in Hugging Face Spaces)
+API_TOKEN = os.getenv('HF_API_KEY')
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+if not API_TOKEN:
+    raise ValueError("HF_API_KEY not found in environment variables. Please set it in .env file or Hugging Face Spaces secrets.")
+
+def generate_prompt(ticker, start_date, end_date):
+    """Fetch data, calculate indicators, and prepare the prompt."""
+    data = fetch_data(ticker, start_date, end_date)
+    
+    if data is None:
+        return "No data available for the given ticker and date range.", None
+    
+    data = calculate_indicators(data)
+    support, resistance = calculate_support_resistance(data)
+    
+    # Additional statistics
+    highest_close = data['Close'].max()
+    lowest_close = data['Close'].min()
+    average_close = data['Close'].mean()
+    average_volume = data['Volume'].mean()
+    highest_volume = data['Volume'].max()
+    lowest_volume = data['Volume'].min()
+    daily_returns = data['Close'].pct_change().dropna()
+    volatility = daily_returns.std()
+    
+    recent_trend = "uptrend" if data['Close'].iloc[-1] > data['Close'].iloc[0] else "downtrend" if data['Close'].iloc[-1] < data['Close'].iloc[0] else "sideways"
+    
+    # Summarize the key statistics
+    summary = {
+        'latest_close': data['Close'].iloc[-1],
+        'SMA_50': data['SMA_50'].iloc[-1],
+        'EMA_50': data['EMA_50'].iloc[-1],
+        'RSI': data['RSI'].iloc[-1],
+        'MACD': data['MACD'].iloc[-1],
+        'MACD_Signal': data['MACD_Signal'].iloc[-1],
+        'Bollinger_High': data['Bollinger_High'].iloc[-1],
+        'Bollinger_Low': data['Bollinger_Low'].iloc[-1],
+        'ATR': data['ATR'].iloc[-1],
+        'OBV': data['OBV'].iloc[-1],
+        'Support': support,
+        'Resistance': resistance,
+        'Highest_Close': highest_close,
+        'Lowest_Close': lowest_close,
+        'Average_Close': average_close,
+        'Average_Volume': average_volume,
+        'Highest_Volume': highest_volume,
+        'Lowest_Volume': lowest_volume,
+        'Volatility': volatility,
+        'Recent_Trend': recent_trend,
+        'Percentage_Change': (data['Close'].iloc[-1] - data['Close'].iloc[-2]) / data['Close'].iloc[-2] * 100 if len(data) > 1 else 0
+    }
+    
+    prompt = f"""
+    Analyze the following stock data for {ticker} and provide a buy/sell recommendation:
+    Latest Close Price: {summary['latest_close']}
+    SMA 50: {summary['SMA_50']}
+    EMA 50: {summary['EMA_50']}
+    RSI: {summary['RSI']}
+    MACD: {summary['MACD']}
+    MACD Signal: {summary['MACD_Signal']}
+    Bollinger Bands High: {summary['Bollinger_High']}
+    Bollinger Bands Low: {summary['Bollinger_Low']}
+    ATR: {summary['ATR']}
+    OBV: {summary['OBV']}
+    Support Level: {summary['Support']}
+    Resistance Level: {summary['Resistance']}
+    Highest Close Price: {summary['Highest_Close']}
+    Lowest Close Price: {summary['Lowest_Close']}
+    Average Close Price: {summary['Average_Close']}
+    Average Volume: {summary['Average_Volume']}
+    Highest Volume: {summary['Highest_Volume']}
+    Lowest Volume: {summary['Lowest_Volume']}
+    Volatility: {summary['Volatility']}
+    Recent Trend: {summary['Recent_Trend']}
+    Percentage Change: {summary['Percentage_Change']}%
+    """
+    
+    return prompt, summary
+
+def get_recommendation(prompt):
+    """Get stock recommendation from Hugging Face API."""
+    headers = {
+        "Authorization": f"Bearer {API_TOKEN}",
+        "Content-Type": "application/json"
+    }
+    payload = {"inputs": prompt}
+    
+    response = requests.post(API_URL, headers=headers, json=payload)
+    response.raise_for_status()  # Raise an error for HTTP issues
+    result = response.json()
+    
+    return result[0]['generated_text'].strip()
+
+def display_recommendation(ticker, start_date, end_date):
+    """Fetch data, generate prompt, get recommendation, and display it in a nice format."""
+    prompt, summary = generate_prompt(ticker, start_date, end_date)
+    
+    if summary is None:
+        st.error(prompt)
+        return
+
+    try:
+        recommendation = get_recommendation(prompt)
+    except Exception as e:
+        st.error(f"An error occurred while getting recommendation: {e}")
+        return 
+
+    # Display in a box/table format using Streamlit
+    st.markdown(f"### Stock Analysis & Recommendation for {ticker}")
+
+    st.markdown(f"""
+    <div style='border:2px solid #4CAF50; padding: 15px; border-radius: 10px;'>
+        <table style='width:100%; border-collapse: collapse;'>
+            <tr>
+                <th style='text-align: left;'>Indicator</th>
+                <th style='text-align: left;'>Value</th>
+            </tr>
+            <tr>
+                <td>Latest Close Price</td>
+                <td>{summary['latest_close']}</td>
+            </tr>
+            <tr>
+                <td>SMA 50</td>
+                <td>{summary['SMA_50']}</td>
+            </tr>
+            <tr>
+                <td>EMA 50</td>
+                <td>{summary['EMA_50']}</td>
+            </tr>
+            <tr>
+                <td>RSI</td>
+                <td>{summary['RSI']}</td>
+            </tr>
+            <tr>
+                <td>MACD</td>
+                <td>{summary['MACD']}</td>
+            </tr>
+            <tr>
+                <td>MACD Signal</td>
+                <td>{summary['MACD_Signal']}</td>
+            </tr>
+            <tr>
+                <td>Bollinger Bands High</td>
+                <td>{summary['Bollinger_High']}</td>
+            </tr>
+            <tr>
+                <td>Bollinger Bands Low</td>
+                <td>{summary['Bollinger_Low']}</td>
+            </tr>
+            <tr>
+                <td>ATR</td>
+                <td>{summary['ATR']}</td>
+            </tr>
+            <tr>
+                <td>OBV</td>
+                <td>{summary['OBV']}</td>
+            </tr>
+            <tr>
+                <td>Support Level</td>
+                <td>{summary['Support']}</td>
+            </tr>
+            <tr>
+                <td>Resistance Level</td>
+                <td>{summary['Resistance']}</td>
+            </tr>
+            <tr>
+                <td>Highest Close Price</td>
+                <td>{summary['Highest_Close']}</td>
+            </tr>
+            <tr>
+                <td>Lowest Close Price</td>
+                <td>{summary['Lowest_Close']}</td>
+            </tr>
+            <tr>
+                <td>Average Close Price</td>
+                <td>{summary['Average_Close']}</td>
+            </tr>
+            <tr>
+                <td>Average Volume</td>
+                <td>{summary['Average_Volume']}</td>
+            </tr>
+            <tr>
+                <td>Highest Volume</td>
+                <td>{summary['Highest_Volume']}</td>
+            </tr>
+            <tr>
+                <td>Lowest Volume</td>
+                <td>{summary['Lowest_Volume']}</td>
+            </tr>
+            <tr>
+                <td>Volatility</td>
+                <td>{summary['Volatility']}</td>
+            </tr>
+            <tr>
+                <td>Recent Trend</td>
+                <td>{summary['Recent_Trend']}</td>
+            </tr>
+            <tr>
+                <td>Percentage Change</td>
+                <td>{summary['Percentage_Change']}%</td>
+            </tr>
+        </table>
+    </div>
+    """, unsafe_allow_html=True)
+
+    st.write('AI Recommendation:')
+    st.write(recommendation)