Update app.py

app.py

@@ -143,23 +143,24 @@ def create_web_search_vectors(search_results):
     
     return FAISS.from_documents(documents, embed)
 
-def summarize_article(article, model, system_prompt, user_query, client, temperature=0.2):
+def summarize_article(article, content, query, model, system_prompt, client, temperature=0.2):
     prompt = f"""Using the following article:
-Title: {article['title']}
-Content (excerpt): {article['body'][:2000]}  # Truncate if too long
-URL: {article['href']}
-Using the following context from web search results (excerpt):
-{content[:2000]}  # Truncate if too long
-Write a detailed and complete research document that includes an introduction, key findings, and a conclusion that addresses the following user request: '{query}'."""
-
-    # Calculate input tokens (this is an approximation, you might need a more accurate method)
-    input_tokens = len(prompt.split()) // 4
+    Title: {article.get('title', 'No Title Available')}
+    Content (excerpt): {article.get('body', '')[:2000]}  # Truncate if too long
+    URL: {article.get('href', 'No URL Available')}
+    
+    And based on the following web search context (excerpt):
+    {content[:2000]}  # Truncate if too long
     
-    # Get the token limit for the current model
-    model_token_limit = MODEL_TOKEN_LIMITS.get(model, 8192)  # Default to 8192 if model not found
+    Write a detailed and complete research document. The document should include:
+    1. An introduction
+    2. Key findings from both the article and search context
+    3. A conclusion that directly answers the user's request: '{query}'."""
     
-    # Calculate max_new_tokens
-    max_new_tokens = min(model_token_limit - input_tokens, 6500)  # Cap at 6500 to be safe
+    # Calculate token usage and model limits
+    input_tokens = len(prompt.split()) // 4  # Approximate token count
+    model_token_limit = MODEL_TOKEN_LIMITS.get(model, 8192)  # Default limit is 8192 if model is not found
+    max_new_tokens = min(model_token_limit - input_tokens, 6500)  # Cap output tokens to avoid exceeding limits
 
     try:
         response = client.chat_completion(