Update app.py

app.py

@@ -1,11 +1,8 @@
 import os
 import streamlit as st
 import requests
-import PyPDF2  # <-- Add this import for PyPDF2
+import PyPDF2
 from groq import Groq
-from langchain.chains import AnalyzeDocumentChain
-from langchain.prompts import PromptTemplate
-from langchain.document_loaders import TextLoader
 from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceEmbeddings
 from sentence_transformers import SentenceTransformer
@@ -24,7 +21,7 @@ def extract_text_from_pdf(pdf_url):
 
     # Read the PDF content
     with open("temp.pdf", "rb") as f:
-        reader = PyPDF2.PdfReader(f)  # Now PyPDF2 is defined
+        reader = PyPDF2.PdfReader(f)
         text = ""
         for page in reader.pages:
             text += page.extract_text()
@@ -94,17 +91,28 @@ def main():
         query = st.text_input("Enter your question here")
         if st.button("Query Document"):
             results = query_faiss(st.session_state['faiss_index'], query)
-            st.write("### Results from Document:")
-            for i, result in enumerate(results):
-                st.write(f"**Result {i+1}:** {result}")
-
-            # Use Groq API for additional insights
-            chat_completion = client.chat.completions.create(
-                messages=[{"role": "user", "content": query}],
-                model="llama-3.3-70b-versatile",
-            )
-            st.write("### Insights from Groq-powered Model:")
-            st.write(chat_completion.choices[0].message.content)
+            if not results:
+                st.warning("No relevant context found in the document.")
+            else:
+                st.write("### Results from Document:")
+                for i, result in enumerate(results):
+                    st.write(f"**Result {i+1}:** {result}")
+
+                # Combine results to provide context
+                context = "\n".join(results)
+                st.write("### Insights based on Document Context:")
+                prompt = (
+                    f"The following context is from the document:\n\n"
+                    f"{context}\n\n"
+                    f"Based on this context, answer the question:\n"
+                    f"{query}"
+                )
+
+                chat_completion = client.chat.completions.create(
+                    messages=[{"role": "user", "content": prompt}],
+                    model="llama-3.3-70b-versatile",
+                )
+                st.write(chat_completion.choices[0].message.content)
 
 if __name__ == "__main__":
     main()