Update app.py

app.py

@@ -43,64 +43,9 @@ st.title("Blah-2")
 # Step 1: Choose PDF Source
 pdf_source = st.radio("Upload or provide a link to a PDF:", ["Enter a PDF URL", "Upload a PDF file"], index=0, horizontal=True)
 
-if pdf_source == "Upload a PDF file":
-    uploaded_file = st.file_uploader("Upload your PDF file", type="pdf")
-    if uploaded_file:
-        st.session_state.pdf_path = "temp.pdf"
-        with open(st.session_state.pdf_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        st.session_state.pdf_loaded = False
-        st.session_state.chunked = False
-        st.session_state.vector_created = False
-
-elif pdf_source == "Enter a PDF URL":
-pdf_url = st.text_input("Enter PDF URL:", key="pdf_url", on_change=lambda: st.session_state.update({"process_pdf": True}))
-
-if st.session_state.get("process_pdf") and pdf_url:  # ✅ Triggered only when Enter is pressed
-    with st.spinner("Downloading PDF..."):
-        try:
-            # Download PDF
-            response = requests.get(pdf_url)
-            if response.status_code == 200:
-                st.session_state.pdf_path = "temp.pdf"
-                with open(st.session_state.pdf_path, "wb") as f:
-                    f.write(response.content)
-                st.success("✅ PDF Downloaded Successfully!")
-            else:
-                st.error("❌ Failed to download PDF. Check the URL.")
-                st.stop()
-
-            # Step 2: Load PDF
-            st.spinner("Loading PDF...")
-            loader = PDFPlumberLoader(st.session_state.pdf_path)
-            docs = loader.load()
-            st.session_state.documents = docs
-            st.session_state.pdf_loaded = True
-            st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
 
-            # Step 3: Chunking the document
-            st.spinner("Chunking the document...")
-            model_name = "nomic-ai/modernbert-embed-base"
-            embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
-            text_splitter = SemanticChunker(embedding_model)
 
-            if st.session_state.documents:
-                documents = text_splitter.split_documents(st.session_state.documents)
-                st.session_state.documents = documents
-                st.session_state.chunked = True
-
-                # Save chunks for persistence
-                CHUNKS_FILE = "/tmp/chunks.pkl"
-                with open(CHUNKS_FILE, "wb") as f:
-                    pickle.dump(documents, f)
-
-                st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
 
-            # Reset trigger to prevent looping
-            st.session_state.process_pdf = False
-
-        except Exception as e:
-            st.error(f"❌ Error: {e}")
 
 
 # Step 2: Load & Process PDF (Only Once)