Update app.py

app.py

@@ -1,44 +1,75 @@
 import streamlit as st
 import pandas as pd
-from smolagents import CodeAgent
 from typing import Union, List, Dict
 from groq import Groq
 import os
 from duckduckgo_search import DDGS
 
 class DuckDuckGoSearch:
-    """Custom DuckDuckGo search implementation"""
+    """
+    Custom DuckDuckGo search implementation with robust error handling and result processing.
+    Uses the duckduckgo_search library to fetch and format news results.
+    """
     def __init__(self):
+        # Initialize the DuckDuckGo search session
         self.ddgs = DDGS()
     
     def __call__(self, query: str, max_results: int = 5) -> str:
         try:
             # Perform the search and get results
-            search_results = list(self.ddgs.text(query, max_results=max_results))
+            # The news method is more appropriate for recent news analysis
+            search_results = list(self.ddgs.news(
+                query,
+                max_results=max_results,
+                region='wt-wt',  # Worldwide results
+                safesearch='on'
+            ))
+            
+            if not search_results:
+                return "No results found. Try modifying your search query."
             
             # Format the results into a readable string
             formatted_results = []
             for idx, result in enumerate(search_results, 1):
+                # Extract available fields with fallbacks for missing data
+                title = result.get('title', 'No title available')
+                snippet = result.get('body', result.get('snippet', 'No description available'))
+                source = result.get('source', 'Unknown source')
+                url = result.get('url', result.get('link', 'No link available'))
+                date = result.get('date', 'Date not available')
+                
+                # Format each result with available information
                 formatted_results.append(
-                    f"{idx}. Title: {result['title']}\n"
-                    f"   Summary: {result['body']}\n"
-                    f"   Source: {result['link']}\n"
+                    f"{idx}. Title: {title}\n"
+                    f"   Date: {date}\n"
+                    f"   Source: {source}\n"
+                    f"   Summary: {snippet}\n"
+                    f"   URL: {url}\n"
                 )
             
             return "\n".join(formatted_results)
+        
         except Exception as e:
-            return f"Search error: {str(e)}"
+            # Provide detailed error information for debugging
+            error_msg = f"Search error: {str(e)}\nTry again with a different search term or check your internet connection."
+            print(f"DuckDuckGo search error: {str(e)}")  # For logging
+            return error_msg
 
 class GroqLLM:
-    """Compatible LLM interface for smolagents CodeAgent"""
+    """
+    LLM interface using Groq's LLama model.
+    Handles API communication and response processing.
+    """
     def __init__(self, model_name="llama-3.1-8B-Instant"):
         self.client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
         self.model_name = model_name
     
     def __call__(self, prompt: Union[str, dict, List[Dict]]) -> str:
         try:
+            # Convert prompt to string if it's a complex structure
             prompt_str = str(prompt) if isinstance(prompt, (dict, list)) else prompt
             
+            # Make API call to Groq
             completion = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=[{
@@ -49,14 +80,18 @@ class GroqLLM:
                 max_tokens=1024,
                 stream=False
             )
+            
             return completion.choices[0].message.content if completion.choices else "Error: No response generated"
         except Exception as e:
             error_msg = f"Error generating response: {str(e)}"
-            print(error_msg)
+            print(error_msg)  # For logging
             return error_msg
 
 def create_analysis_prompt(topic: str, search_results: str) -> str:
-    """Creates a structured prompt for news analysis"""
+    """
+    Creates a detailed prompt for news analysis, structuring the request
+    to get comprehensive and well-organized results from the LLM.
+    """
     return f"""Analyze the following news information about {topic}. 
     Search Results: {search_results}
     
@@ -86,7 +121,10 @@ def create_analysis_prompt(topic: str, search_results: str) -> str:
     Please format the analysis in a clear, journalistic style with section headers."""
 
 def log_agent_activity(prompt: str, result: str, agent_name: str):
-    """Logs agent activities in the Streamlit interface"""
+    """
+    Creates an expandable log of agent activities in the Streamlit interface
+    for transparency and debugging purposes.
+    """
     with st.expander("View Agent Activity Log"):
         st.write(f"### Agent Activity ({agent_name}):")
         st.write("**Input Prompt:**")
@@ -137,19 +175,29 @@ try:
         if news_topic:
             with st.spinner("Gathering information and analyzing..."):
                 try:
+                    # Show search progress
+                    search_placeholder = st.empty()
+                    search_placeholder.info("Searching for recent news...")
+                    
                     # Perform search
                     search_results = search_tool(
                         f"Latest news about {news_topic} last 7 days",
                         max_results=search_depth
                     )
                     
-                    if not search_results.startswith("Search error"):
+                    if not search_results.startswith(("Search error", "No results")):
+                        # Update progress
+                        search_placeholder.info("Analyzing search results...")
+                        
                         # Create analysis prompt
                         analysis_prompt = create_analysis_prompt(news_topic, search_results)
                         
                         # Get analysis from LLM
                         analysis_result = llm(analysis_prompt)
                         
+                        # Clear progress messages
+                        search_placeholder.empty()
+                        
                         # Display results
                         st.subheader("📊 Analysis Results")
                         st.markdown(analysis_result)
@@ -161,7 +209,8 @@ try:
                             "News Analysis Agent"
                         )
                     else:
-                        st.error(f"Search failed: {search_results}")
+                        search_placeholder.empty()
+                        st.error(search_results)
                         
                 except Exception as e:
                     st.error(f"An error occurred during analysis: {str(e)}")