Update app.py

app.py

@@ -1,62 +1,61 @@
 import gradio as gr
-import fitz  # PyMuPDF
 from langchain.chains.question_answering import load_qa_chain
 from langchain_openai import ChatOpenAI
-
 from langchain.vectorstores import FAISS
 from langchain.document_loaders import PyPDFLoader
-
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
-import tempfile
 import os
-
 from dotenv import load_dotenv
+
 load_dotenv()
-# Set your OpenAI key
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
 
 llm = ChatOpenAI(model_name="gpt-4", temperature=0)
 embedding = OpenAIEmbeddings()
-chain = None
+
+# Global vector DB
 db = None
 
-def process_pdf(file):
+def upload_pdf(file):
     global db
-    tmp_path = file.name  # directly use the file path
-
-    loader = PyPDFLoader(tmp_path)
+    pdf_path = file.name
+    loader = PyPDFLoader(pdf_path)
     documents = loader.load()
     splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     docs = splitter.split_documents(documents)
     db = FAISS.from_documents(docs, embedding)
-    return "PDF processed successfully! You can now start asking questions."
-
+    return "✅ PDF processed. You can start chatting with it!"
 
-def ask_question(question):
+def chat_with_pdf(message, history):
     global db
     if not db:
-        return "Please upload and process a PDF first."
+        return "❌ Please upload and process a PDF first."
     retriever = db.as_retriever()
-    qa_chain = load_qa_chain(llm, chain_type="stuff")
-    docs = retriever.get_relevant_documents(question)
-    return qa_chain.run(input_documents=docs, question=question)
-
-upload_interface = gr.Interface(
-    fn=process_pdf,
-    inputs=gr.File(file_types=[".pdf"]),
-    outputs="text",
-    title="Interactive PDF Uploader",
-    description="Upload a PDF to interact with."
-)
-
-chat_interface = gr.Interface(
-    fn=ask_question,
-    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
-    outputs="text",
-    title="PDF Chat Interface",
-    description="Ask questions based on the uploaded PDF."
-)
-
-demo = gr.TabbedInterface([upload_interface, chat_interface], ["Upload PDF", "Chat with PDF"])
-demo.launch()
+    chain = load_qa_chain(llm, chain_type="stuff")
+    docs = retriever.get_relevant_documents(message)
+    response = chain.run(input_documents=docs, question=message)
+    return response
+
+with gr.Blocks(title="📄 PDF Chatbot") as demo:
+    gr.Markdown("## 📄 Interactive PDF Reader + Chatbot\nUpload a PDF and chat with it using GPT-4.")
+    
+    with gr.Row():
+        pdf_file = gr.File(label="Upload PDF", file_types=[".pdf"])
+        upload_btn = gr.Button("Process PDF")
+    
+    status = gr.Textbox(label="Status", interactive=False)
+    
+    chatbot = gr.ChatInterface(
+        fn=chat_with_pdf,
+        chatbot=gr.Chatbot(height=400),
+        textbox=gr.Textbox(placeholder="Ask anything from the PDF...", lines=2),
+        title="Talk with your PDF",
+        theme="compact",
+        examples=["Summarize this document.", "What are the key points?", "Is there a deadline mentioned?"],
+        cache_examples=False
+    )
+
+    upload_btn.click(fn=upload_pdf, inputs=pdf_file, outputs=status)
+
+demo.launch(share=True)