Update app.py

app.py

@@ -19,13 +19,16 @@ def process_file(file_path):
     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     vectorstore = FAISS.from_documents(texts, embeddings)
 
-    prompt_template = """
-    Use the following pieces of context to answer the question at the end. If you don't know the answer, just say you don't know.
+    custom_prompt_template = """
+    Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know.
     {context}
     Question: {question}
     Helpful Answer:
     """
-    prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
+    prompt = PromptTemplate(
+        template=custom_prompt_template,
+        input_variables=["context", "question"]
+    )
 
     llm = HuggingFaceEndpoint(
         repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -35,7 +38,7 @@ def process_file(file_path):
         huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN")
     )
 
-    qa_chain = RetrievalQA.from_chain_type(
+    qa = RetrievalQA.from_chain_type(
         llm=llm,
         chain_type="stuff",
         retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
@@ -43,46 +46,43 @@ def process_file(file_path):
         return_source_documents=True
     )
 
-    return qa_chain
+    return qa
 
-# Chat start
+# Chat start — show file upload prompt
 @cl.on_chat_start
 async def start():
-    await cl.Message(content="👋 Welcome! Please upload a PDF file to begin.").send()
-    files = await cl.AskFileMessage(
-        content="📄 Upload a PDF to get started.",
+    await cl.Message("👋 Welcome! Please upload a PDF to get started.").send()
+
+    # Ask user to upload a file
+    await cl.AskFileMessage(
+        content="📄 Upload a PDF file below:",
         accept=["application/pdf"],
-        max_size_mb=10,
+        max_size_mb=20,
         timeout=180
     ).send()
 
-    if not files:
-        await cl.Message(content="⚠️ No file was uploaded.").send()
-        return
-
-    file = files[0]
-    file_path = file.path
-
-    qa_chain = process_file(file_path)
-    cl.user_session.set("qa_chain", qa_chain)
-    cl.user_session.set("ready", True)
-
-    await cl.Message(content=f"✅ `{file.name}` processed successfully. You can now ask questions.").send()
-
-# Handle questions
+# Handle file and build QA chain
 @cl.on_message
 async def handle_message(message: cl.Message):
-    if not cl.user_session.get("ready"):
-        await cl.Message(content="⚠️ Please upload a PDF file first.").send()
+    if files := await cl.user_session.get("files"):
+        file = files[0]  # Get the first uploaded file
+        file_path = file.path
+
+        # Process and store the chain
+        qa_chain = process_file(file_path)
+        cl.user_session.set("qa_chain", qa_chain)
+        cl.user_session.set("ready", True)
+        await cl.Message(content=f"✅ File `{file.name}` processed. Ask your question!").send()
+        cl.user_session.set("files", None)  # Clear after processing
         return
 
-    qa_chain = cl.user_session.get("qa_chain")
-    if not qa_chain:
-        await cl.Message(content="⚠️ Error: QA chain not initialized.").send()
+    # If file was already processed, answer question
+    if not cl.user_session.get("ready"):
+        await cl.Message("⚠️ Please upload a PDF file first.").send()
         return
 
-    query = message.content
-    response = qa_chain(query)
-    await cl.Message(content=response["result"]).send()
-
+    qa_chain = cl.user_session.get("qa_chain")
+    response = qa_chain(message.content)
+    answer = response["result"]
+    await cl.Message(content=answer).send()