Update app.py

app.py

@@ -26,17 +26,17 @@ import os
 import sys
 import subprocess
 
-
 import requests
 from bs4 import BeautifulSoup
 
+
 def scrape_website(url):
     headers = {
         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
     }
-    
+
     response = requests.get(url, headers=headers)
-    
+
     if response.status_code == 200:
         soup = BeautifulSoup(response.text, "html.parser")
         return soup.prettify()  # Returns the full parsed HTML
@@ -58,6 +58,7 @@ if uploaded_file is not None:
         stock_data = json.load(uploaded_file)
         st.success("Data loaded successfully!")
 
+
     # Configuration Class
     class Config:
         ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY")
@@ -79,16 +80,18 @@ if uploaded_file is not None:
             "Real Estate",
             "Utilities"
         ]
-    
+
+
     # Create directories if they don't exist
     os.makedirs(Config.STOCK_DATA_DIR, exist_ok=True)
     os.makedirs(os.path.dirname(Config.OUTPUT_FILE), exist_ok=True)
     os.makedirs(os.path.dirname(Config.SCENARIO_OUTPUT_FILE), exist_ok=True)
 
+
     def configure_generative_ai():
         """Configures the generative AI model and starts a chat session."""
         genai.configure(api_key=Config.GOOGLE_API_KEY)
-    
+
         generation_config = {
             "temperature": 1,
             "top_p": 0.95,
@@ -96,13 +99,15 @@ if uploaded_file is not None:
             "max_output_tokens": 8192,
             "response_mime_type": "text/plain",
         }
-    
+
         model = genai.GenerativeModel(
             model_name="gemini-2.0-flash-exp",
             generation_config=generation_config,
         )
-    
+
         return model.start_chat()
+
+
     # Fetch stock data
     st.write("Fetching stock data...")
     stock_symbols = [value["symbol"] for value in stock_data.values()]
@@ -141,13 +146,50 @@ if uploaded_file is not None:
     st.write("Extracting market scenarios...")
     # context_data = asyncio.run(extract_text_from_website(url))
     context_data = scrape_website(url)
+    print(context_data)
     st.success("Market context extracted successfully!")
 
     # Generate scenario prompt
     scenario_prompt = f"""
-    # TASK: Analyze market context and identify potential market scenarios.
-    ...
-    """
+        # TASK: Analyze market context and identify potential market scenarios.
+
+        # CONTEXT:
+        {context_data}
+        # END CONTEXT
+
+        # INSTRUCTION: Based on the provided market context, analyze and identify up to three plausible market scenarios.
+        # For each scenario, determine its name (e.g., "Moderate Downturn"), the general market direction ("up" or "down"), a major trigger point that could cause the scenario to unfold, and a list of sectors that would be significantly impacted. Each 'sector_impact' list should have less than or equal to 4 sectors.
+
+        # OUTPUT FORMAT: Provide the analysis in JSON format with the following structure.
+        # Use the sector names provided:
+        {sectors}
+
+        # EXAMPLE:
+
+    json
+        {{
+            "market_scenarios": {{
+                "scenario1": {{
+                    "name": "Moderate Downturn",
+                    "direction": "down",
+                    "trigger": "Interest rate hike",
+                    "sector_impact": [
+                        "Financials",
+                        "Energy"
+                    ]
+                }},
+                "scenario2": {{
+                    "name": "Bullish Growth",
+                    "direction": "up",
+                    "trigger": "Successful vaccine rollout",
+                    "sector_impact": [
+                        "Health Care",
+                        "Information Technology"
+                    ]
+                }}
+            }}
+        }}
+        """
 
     chat_session = configure_generative_ai()
 
@@ -176,4 +218,4 @@ if uploaded_file is not None:
     st.write("Download Output Files")
     for file in [Config.OUTPUT_FILE, Config.SCENARIO_OUTPUT_FILE, simulation_results_file]:
         with open(file, "rb") as f:
-            st.download_button(label=f"Download {file.split('/')[-1]}", data=f, file_name=file.split('/')[-1]) 
+            st.download_button(label=f"Download {file.split('/')[-1]}", data=f, file_name=file.split('/')[-1])