Update app.py

app.py

@@ -11,7 +11,7 @@ from langchain.chains.question_answering import load_qa_chain  # Import load_qa_
 async def initialize(file_path, question):
     genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
     model = genai.GenerativeModel('gemini-pro')
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.0)
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
     
     # Refined prompt template to encourage precise and concise answers
     prompt_template = """Answer the question precisely and concisely using the provided context. Avoid any additional commentary or system messages.
@@ -56,22 +56,24 @@ async def initialize(file_path, question):
         max_score = max(page_scores)
         top_pages = [i+1 for i, score in enumerate(page_scores) if score == max_score]
         
-        # Optionally, limit the number of top pages to display
-        num_top_pages = 5  # Adjust this value based on your needs
-        if len(top_pages) > num_top_pages:
-            top_pages = sorted(top_pages)[:num_top_pages]
+        # Limit to the top 2 pages
+        num_top_pages = 2
+        top_pages = sorted(top_pages)[:num_top_pages]
+        
+        # Generate links for each top page
+        file_name = os.path.basename(file_path)
+        page_links = [f"[Page {p}](file://{os.path.abspath(file_path)}#page={p})" for p in top_pages]
+        page_links_str = ', '.join(page_links)
         
         if top_pages:
-            page_numbers = ', '.join(str(p) for p in sorted(top_pages))
-            source_str = f"Top relevant page(s): {page_numbers}"
+            source_str = f"Top relevant page(s): {page_links_str}"
         else:
             source_str = "Top relevant page(s): Not found in specific page"
 
         # Create a clickable link for the document
-        file_name = os.path.basename(file_path)
-        source_link = f"[{file_name}](file://{os.path.abspath(file_path)})"
+        source_link = f"[Document: {file_name}](file://{os.path.abspath(file_path)})"
         
-        return f"Answer: {answer}\n{source_str}\n[Document: {source_link}]"
+        return f"Answer: {answer}\n{source_str}\n{source_link}"
     else:
         return "Error: Unable to process the document. Please ensure the PDF file is valid."