Update app.py

app.py

@@ -483,26 +483,45 @@ def setup_qa(db):
     return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
 
 # --- Process Answer ---
-def process_answer(user_question, full_text):
-    if not full_text:
-        return "No content was extracted from the PDF. Please try another file."
+def process_answer(question, full_text):
+    # STEP 1: Chunk the PDF text
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    docs = text_splitter.create_documents([full_text])
 
-    docs = split_text_into_chunks(full_text)
-    db = create_vectorstore(docs)
-    qa = setup_qa(db)
+    # STEP 2: Create embeddings
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = Chroma.from_documents(docs, embeddings)
 
-    prompt = f"""
-    You are a helpful AI assistant. Based on the provided context from a PDF document,
-    generate an accurate, informative answer to the following question:
+    # STEP 3: Retrieve relevant chunks using the question
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+    relevant_docs = retriever.get_relevant_documents(question)
+
+    # STEP 4: Format the context
+    context = "\n\n".join([doc.page_content for doc in relevant_docs])
+
+    # STEP 5: Prompting
+    prompt_template = """
+You are a helpful assistant that answers questions based on the context below.
+
+Context:
+{context}
+
+Question: {question}
+
+Answer:
+    """.strip()
+
+    prompt = prompt_template.format(context=context, question=question)
+
+    # STEP 6: Load the model and generate response
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="MBZUAI/LaMini-T5-738M",
+        task="text2text-generation",
+        model_kwargs={"temperature": 0.3, "max_length": 256},
+    )
+
+    return llm.invoke(prompt)
 
-    {user_question}
-    """
-    try:
-        result = qa({"query": prompt})
-        return result['result']
-    except Exception as e:
-        logging.error(f"Error generating answer: {e}")
-        return "Sorry, I couldn't generate an answer due to an internal error."
 
 # --- UI Layout ---
 with st.sidebar: