Update app.py

app.py

@@ -501,7 +501,10 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
         # Truncate context
         context = truncate_context(context)
         
-        prompt = f"{system_instruction}\n\nBased on the following data extracted from Excel spreadsheets:\n{context}\n\nPlease provide the Python code needed to execute the following task: '{query}'. Ensure that the code is derived directly from the dataset. If a chart is requested, use the matplotlib library to generate the appropriate visualization."
+        messages = [
+            {"role": "system", "content": system_instruction},
+            {"role": "user", "content": f"Based on the following data extracted from Excel spreadsheets:\n{context}\n\nPlease provide the Python code needed to execute the following task: '{query}'. Ensure that the code is derived directly from the dataset. If a chart is requested, use the matplotlib library to generate the appropriate visualization."}
+        ]
         
     elif file_type == "pdf":
         # PDF functionality
@@ -520,7 +523,10 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
         Your goal is to provide accurate, detailed, and precise summaries based on the context provided. 
         Avoid making assumptions or adding information that is not explicitly supported by the context from the PDF documents."""
 
-        prompt = f"{system_instruction}\n\nUsing the following context from the PDF documents:\n{context_str}\n\nPlease generate a step-by-step reasoning before arriving at a comprehensive and accurate summary addressing the following question: '{query}'. Ensure your response is strictly based on the provided context, highlighting key metrics, trends, and significant details relevant to the query. Avoid any speculative or unverified information."
+        messages = [
+            {"role": "system", "content": system_instruction},
+            {"role": "user", "content": f"Using the following context from the PDF documents:\n{context_str}\n\nPlease generate a step-by-step reasoning before arriving at a comprehensive and accurate summary addressing the following question: '{query}'. Ensure your response is strictly based on the provided context, highlighting key metrics, trends, and significant details relevant to the query. Avoid any speculative or unverified information."}
+        ]
 
     else:
         raise ValueError("Invalid file type. Use 'excel' or 'pdf'.")
@@ -530,7 +536,7 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
         try:
             # Generate content with streaming enabled
             for response in client.chat_completion(
-                prompt=prompt,
+                messages=messages,  # Pass messages in the required format
                 max_new_tokens=1000,  # Reduced to ensure we stay within token limits
                 temperature=temperature,
                 stream=True,