Update app.py

app.py

@@ -1,69 +1,82 @@
-# Install dependencies (run once in Colab)
-# !pip install PyPDF2 langchain faiss-cpu sentence-transformers transformers accelerate gradio
-
-import gradio as gr
+# rag_upload_app_local.py
+import os
 from PyPDF2 import PdfReader
+import gradio as gr
+
+# LangChain imports
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
 from langchain.llms import HuggingFacePipeline
-from transformers import pipeline
-
-# ---------- Upload PDF and process ----------
-def process_pdf(file):
-    pdf_reader = PdfReader(file.name)
-    text = ""
-    for page in pdf_reader.pages:
-        page_text = page.extract_text()
-        if page_text:
-            text += page_text
-
-    # Split text into smaller chunks
-    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
-    chunks = splitter.split_text(text)
-
-    # Create embeddings
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    vectorstore = FAISS.from_texts(chunks, embeddings)
-
-    # Setup HuggingFace LLM
-    generator = pipeline(
-        "text-generation",
-        model="bigscience/bloom-560m",  # public, free
-        max_new_tokens=256,
-        temperature=0.7
-    )
-    llm = HuggingFacePipeline(pipeline=generator)
-
-    # Build conversational retrieval chain
-    chatbot = ConversationalRetrievalChain.from_llm(
-        llm,
-        retriever=vectorstore.as_retriever()
-    )
-
-    return chatbot
-
-# ---------- Chat function ----------
-def ask_pdf(chatbot, question):
-    result = chatbot({"question": question, "chat_history": []})
-    return result["answer"]
-
-# ---------- Gradio Interface ----------
-chatbot_instance = None  # global to store loaded PDF
-
-def gradio_interface(file, question):
-    global chatbot_instance
-    if chatbot_instance is None:
-        chatbot_instance = process_pdf(file)
-    return ask_pdf(chatbot_instance, question)
-
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=[gr.File(label="Upload PDF"), gr.Textbox(label="Ask a question")],
-    outputs=gr.Textbox(label="Assistant Answer"),
-    title="🤖 PDF Chat Assistant",
-    description="Upload a PDF and ask questions from it."
-)
-
-iface.launch()
+from langchain.chains import RetrievalQA
+
+# Transformers imports
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# --- CONFIG ---
+EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+LOCAL_MODEL = "google/flan-t5-base"  # lightweight model
+
+# Load local HuggingFace model
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL)
+model = AutoModelForSeq2SeqLM.from_pretrained(LOCAL_MODEL)
+pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
+llm = HuggingFacePipeline(pipeline=pipe)
+
+def process_document(file):
+    try:
+        if file is None:
+            return None, "⚠️ Please upload a document."
+
+        # Extract text from PDF
+        text = ""
+        reader = PdfReader(file)
+        for page in reader.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
+
+        if not text.strip():
+            return None, "⚠️ No text could be extracted. Try another PDF."
+
+        # Split text into chunks
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        chunks = splitter.split_text(text)
+
+        # Create embeddings + FAISS index
+        embedder = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
+        db = FAISS.from_texts(chunks, embedder)
+
+        # Create retriever + QA chain
+        retriever = db.as_retriever(search_kwargs={"k": 4})
+        qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+
+        return qa, f"✅ Document processed successfully with {len(chunks)} chunks!"
+
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}"
+
+def answer_question(qa, question):
+    if qa is None:
+        return "Please upload and process a document first."
+    return qa.run(question)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 PDF CHAT ASSISSTANT")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload PDF Document", type="filepath")
+        status = gr.Textbox(label="Status", interactive=False, lines=6)  # 🔹 bigger
+
+    process_btn = gr.Button("Process Document")
+
+    with gr.Row():
+        question = gr.Textbox(label="Ask a Question", lines=3, placeholder="Type your question here...")  # 🔹 taller
+        answer = gr.Textbox(label="Answer", lines=8)  # 🔹 taller answer box
+
+    qa_state = gr.State()
+
+    process_btn.click(fn=process_document, inputs=file_input, outputs=[qa_state, status])
+    question.submit(fn=answer_question, inputs=[qa_state, question], outputs=answer)
+
+demo.launch()