Update app.py

app.py

@@ -9,68 +9,69 @@ from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 from langchain_groq import GroqLLM
 
-# Set environment variables (You can also use os.environ or Streamlit secrets)
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+# --- Environment Variable Setup ---
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "your-groq-api-key")
+HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY", "your-huggingface-api-key")
 
-# Initialize Groq LLM
+# --- Groq LLM Initialization ---
 llm = GroqLLM(
     api_key=GROQ_API_KEY,
-    model="llama3-8b-8192",  # <- correct param
+    model="llama3-8b-8192",
     temperature=0.1
 )
 
-# HuggingFace Embeddings
-embedding = HuggingFaceEmbeddings()
+# --- HuggingFace Embeddings (add a default model name if needed) ---
+embedding = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-MiniLM-L6-v2",
+    cache_folder="./hf_cache",
+    huggingfacehub_api_token=HUGGINGFACE_API_KEY
+)
 
+# --- Streamlit UI ---
 st.title("📄 RAG Chat with Groq + HuggingFace")
 
-# Upload PDF
 uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
-
 user_query = st.text_input("Ask something about the document")
 submit_button = st.button("Submit")
 
 if uploaded_file and submit_button:
-    # Save PDF temporarily
     with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
         tmp_file.write(uploaded_file.read())
         tmp_path = tmp_file.name
 
-    # Load and split
+    # --- Load and Split PDF ---
     loader = PyPDFLoader(tmp_path)
     pages = loader.load_and_split()
 
-    # Create FAISS vectorstore
+    # --- FAISS Vector Store ---
     vectorstore = FAISS.from_documents(pages, embedding)
     retriever = vectorstore.as_retriever()
 
-    # Custom prompt (optional)
+    # --- Optional Custom Prompt ---
     prompt_template = PromptTemplate(
         input_variables=["context", "question"],
         template="""
-        Use the following context to answer the question. Be concise and accurate.
-
+        You are an intelligent assistant. Use the following context to answer the question accurately.
         Context: {context}
-
         Question: {question}
-        """
+        Answer:"""
     )
 
-    # Create QA chain
+    # --- RetrievalQA Chain ---
     qa_chain = RetrievalQA.from_chain_type(
         llm=llm,
+        chain_type="stuff",
         retriever=retriever,
         return_source_documents=True,
         chain_type_kwargs={"prompt": prompt_template}
     )
 
-    # Run QA
+    # --- Run the Chain ---
     result = qa_chain({"query": user_query})
     st.markdown("### 💬 Answer")
     st.write(result["result"])
 
-    # Optional: Show sources
+    # --- Optional: Show Source Documents ---
     with st.expander("📄 Sources"):
-        for doc in result["source_documents"]:
-            st.write(doc.metadata["source"])
+        for i, doc in enumerate(result["source_documents"]):
+            st.write(f"**Page {i+1}** — {doc.metadata.get('source', 'Unknown')}")