Update app.py

app.py

@@ -1,15 +1,13 @@
 import gradio as gr
 import os
 import numpy as np
-import pandas as pd
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import PyPDF2
 import docx
 import requests
 import json
-import tempfile
-from typing import List, Tuple
+from typing import List
 import logging
 
 # Configure logging
@@ -93,9 +91,9 @@ class RAGSystem:
         if self.documents:
             # Create embeddings for all document chunks
             self.embeddings = self.embedder.encode(self.documents)
-            return f"Successfully processed {len(files)} files with {len(self.documents)} text chunks."
+            return f"✅ Processed {len(files)} files with {len(self.documents)} text chunks."
         else:
-            return "No text could be extracted from the uploaded files."
+            return "⚠️ No text could be extracted from the uploaded files."
     
     def split_text(self, text: str, chunk_size: int = 500) -> List[str]:
         """Split text into smaller chunks"""
@@ -136,7 +134,7 @@ class RAGSystem:
     def query_groq(self, prompt: str) -> str:
         """Query Groq API with the given prompt"""
         if not self.groq_api_key:
-            return "Please set your Groq API key first."
+            return "⚠️ Please set your Groq API key first."
         
         headers = {
             "Authorization": f"Bearer {self.groq_api_key}",
@@ -144,7 +142,7 @@ class RAGSystem:
         }
         
         data = {
-            "model": "llama3-8b-8192",  # Free model
+            "model": "llama-3.1-8b-instant",  # ✅ Valid Groq model
             "messages": [
                 {
                     "role": "system",
@@ -156,31 +154,35 @@ class RAGSystem:
                 }
             ],
             "temperature": 0.7,
-            "max_tokens": 1024
+            "max_tokens": 1024,
+            "stream": False
         }
         
         try:
             response = requests.post(self.groq_base_url, headers=headers, json=data)
             response.raise_for_status()
             result = response.json()
-            return result['choices'][0]['message']['content']
+            return result["choices"][0]["message"]["content"]
         except requests.exceptions.RequestException as e:
             logger.error(f"Error querying Groq API: {e}")
             return f"Error querying Groq API: {str(e)}"
+        except KeyError:
+            logger.error(f"Unexpected Groq API response: {result}")
+            return f"Unexpected Groq API response: {json.dumps(result, indent=2)}"
     
     def answer_query(self, query: str) -> str:
         """Answer a query using RAG"""
         if not self.documents:
-            return "No documents have been processed yet. Please upload and process documents first."
+            return "⚠️ No documents have been processed yet. Please upload and process documents first."
         
         if not self.groq_api_key:
-            return "Please set your Groq API key first."
+            return "⚠️ Please set your Groq API key first."
         
         # Retrieve relevant chunks
         relevant_chunks = self.retrieve_relevant_chunks(query)
         
         if not relevant_chunks:
-            return "No relevant information found in the documents."
+            return "⚠️ No relevant information found in the documents."
         
         # Create context from relevant chunks
         context = "\n\n".join(relevant_chunks)
@@ -204,16 +206,16 @@ rag_system = RAGSystem()
 # Gradio interface functions
 def set_api_key(api_key):
     rag_system.set_api_key(api_key)
-    return "API key set successfully!"
+    return "✅ API key set successfully!"
 
 def process_files(files):
     if not files:
-        return "Please upload at least one file."
+        return "⚠️ Please upload at least one file."
     return rag_system.process_documents(files)
 
 def answer_question(query):
     if not query.strip():
-        return "Please enter a question."
+        return "⚠️ Please enter a question."
     return rag_system.answer_query(query)
 
 # Create Gradio interface
@@ -258,7 +260,7 @@ with gr.Blocks(title="RAG Document Q&A System", theme=gr.themes.Soft()) as demo:
                     lines=2
                 )
             with gr.Column(scale=1):
-                ask_btn = gr.Button("Ask Question", variant="primary", size="lg")
+                ask_btn = gr.Button("Ask Question", variant="primary")
         
         answer_output = gr.Textbox(
             label="Answer",
@@ -305,4 +307,4 @@ with gr.Blocks(title="RAG Document Q&A System", theme=gr.themes.Soft()) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)