added app.py

app.py

@@ -0,0 +1,187 @@
+import os
+import time
+import shutil
+import uuid
+import gradio as gr
+import requests
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from threading import Thread
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+# === CONFIG ===
+STORAGE_DIR = "storage"
+CLEANUP_INTERVAL = 600  # 10 min
+SESSION_TTL = 1800      # 30 min
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+OPENROUTER_MODEL = "z-ai/glm-4.5-air:free"
+
+if not os.path.exists(STORAGE_DIR):
+    os.makedirs(STORAGE_DIR)
+
+# === CLEANUP THREAD ===
+def cleanup_old_sessions():
+    while True:
+        now = time.time()
+        for folder in os.listdir(STORAGE_DIR):
+            path = os.path.join(STORAGE_DIR, folder)
+            if os.path.isdir(path) and now - os.path.getmtime(path) > SESSION_TTL:
+                shutil.rmtree(path)
+        time.sleep(CLEANUP_INTERVAL)
+
+Thread(target=cleanup_old_sessions, daemon=True).start()
+
+# === PDF PROCESSING ===
+def process_pdf(pdf_file):
+    if pdf_file is None:
+        return "No file uploaded.", "", []
+    session_id = str(uuid.uuid4())
+    reader = PdfReader(pdf_file.name)
+    text = "".join([page.extract_text() for page in reader.pages if page.extract_text()])
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = splitter.split_text(text)
+
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    session_path = os.path.join(STORAGE_DIR, session_id)
+    os.makedirs(session_path, exist_ok=True)
+
+    db = FAISS.from_texts(chunks, embeddings)
+    db.save_local(session_path)
+
+    chat_history = [("System", "Paper uploaded and processed. You can now ask questions.")]
+    return f"Paper uploaded successfully. Session ID: {session_id}", session_id, chat_history
+
+# === QUERY FUNCTION ===
+def query_paper(session_id, user_message, chat_history):
+    if not session_id or not os.path.exists(os.path.join(STORAGE_DIR, session_id)):
+        chat_history = chat_history or []
+        chat_history.append(("System", "Session expired or not found. Upload the paper again."))
+        return chat_history, ""
+
+    if not user_message.strip():
+        return chat_history, ""
+
+    session_path = os.path.join(STORAGE_DIR, session_id)
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    db = FAISS.load_local(session_path, embeddings, allow_dangerous_deserialization=True)
+    retriever = db.as_retriever(search_kwargs={"k": 3})
+
+    # Use invoke() method
+    docs = retriever.invoke(user_message)
+    context = "\n\n".join([d.page_content for d in docs])
+
+    prompt = f"""
+You are an AI assistant. Explain the following research paper content in simple terms and answer the question.
+Context from paper:
+{context}
+
+Question: {user_message}
+Answer:
+"""
+
+    headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}", "Content-Type": "application/json"}
+    payload = {
+        "model": OPENROUTER_MODEL,
+        "messages": [
+            {"role": "system", "content": "You are a helpful research explainer."},
+            {"role": "user", "content": prompt}
+        ]
+    }
+
+    try:
+        response = requests.post("https://openrouter.ai/api/v1/chat/completions",
+                                 headers=headers, json=payload)
+
+        if response.status_code == 200:
+            answer = response.json()["choices"][0]["message"]["content"].strip()
+        else:
+            answer = f"Error: {response.status_code} - {response.text}"
+    except Exception as e:
+        answer = f"Error: {str(e)}"
+
+    # Update chat history
+    chat_history = chat_history or []
+    chat_history.append((user_message, answer))
+    
+    return chat_history, ""
+
+# === GRADIO UI ===
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 Research Paper Chatbot (RAG + OpenRouter)")
+
+    with gr.Row():
+        pdf_input = gr.File(label="Upload Research Paper (PDF)", file_types=[".pdf"])
+        session_box = gr.Textbox(label="Session ID", interactive=False)
+
+    chatbot = gr.Chatbot(label="Chat about your paper", height=400)
+    user_message = gr.Textbox(label="Ask a question", placeholder="What is this paper about?")
+
+    with gr.Row():
+        upload_btn = gr.Button("Upload Paper", variant="primary")
+        ask_btn = gr.Button("Send Question")
+        clear_btn = gr.Button("Clear Chat")
+
+    # Store chat history and session
+    state_chat = gr.State([])
+    state_session = gr.State("")
+
+    # Upload button functionality
+    def handle_upload(pdf_file):
+        status, session_id, chat_history = process_pdf(pdf_file)
+        return status, session_id, chat_history
+
+    upload_btn.click(
+        fn=handle_upload,
+        inputs=[pdf_input],
+        outputs=[session_box, state_session, state_chat]
+    )
+
+    # Ask button functionality
+    def handle_question(session_id, message, chat_history):
+        updated_chat, _ = query_paper(session_id, message, chat_history)
+        return updated_chat, ""
+
+    ask_btn.click(
+        fn=handle_question,
+        inputs=[state_session, user_message, state_chat],
+        outputs=[chatbot, user_message]
+    ).then(
+        lambda chat: chat,
+        inputs=[chatbot],
+        outputs=[state_chat]
+    )
+
+    # Submit on enter
+    user_message.submit(
+        fn=handle_question,
+        inputs=[state_session, user_message, state_chat],
+        outputs=[chatbot, user_message]
+    ).then(
+        lambda chat: chat,
+        inputs=[chatbot],
+        outputs=[state_chat]
+    )
+
+    # Clear chat
+    def clear_chat():
+        return [], []
+
+    clear_btn.click(
+        fn=clear_chat,
+        outputs=[chatbot, state_chat]
+    )
+
+    # Update chatbot display when chat history changes
+    state_chat.change(
+        lambda chat: chat,
+        inputs=[state_chat],
+        outputs=[chatbot]
+    )
+
+demo.launch(debug=True)