initial commit

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+.env

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+ENV PORT=7860
+
+EXPOSE 7860
+
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

Procfile

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+web: gunicorn app:app

README.md

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----
-title: Legal Docs AI
-emoji: 👀
-colorFrom: gray
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 RAG-based Research Paper Chatbot
+
+After multiple failed prototypes and experiments with different LLMs, I’m excited to share a working version of my PDF chatbot web app — where users can upload a research paper or any PDF and chat with an AI model that gives contextually relevant answers based on the document contents!
+
+### 💡 What it does:
+Upload any research paper (or PDF), and the app will let you chat with it intelligently. It breaks down the PDF into chunks, creates embeddings, indexes them, and uses a language model to generate answers from the document content. Perfect for students, researchers, or curious minds who want to understand dense PDFs faster!
+
+## 🚀 Live Demo
+[Click here](https://huggingface.co/spaces/ommore86/research-paper-chatbot) to get the live demo of the webpage !!!
+
+## 📸 Screenshots
+![Homepage](homepage.png)
+![ChatPage-1](chatpage.png)
+![ChatPage-1](chatpage2.png)
+![TechStack](tech_stack.png)
+
+## 🛠️ Tech Stack & Tools:
+- <b>Frontend:</b> TailwindCSS
+- <b>Backend:</b> Python + Flask
+- <b>PDF Processing:</b> LangChain
+- <b>Embeddings:</b> all-MiniLM-L6-v2 (HuggingFace)
+- <b>Vector Store:</b> FAISS (for fast similarity search)
+- <b>LLM:</b> llama-3.1-8b-instant — "after trying with other similar models like Mixtral and llama-3.3-70b-versatile"
+- <b>Deployment:</b> Hugging Face Spaces (Earlier I used Vercel → then Render → but RAM limits made me shift to HF Spaces, and it’s been a game changer!)
+
+## Publication
+I have made the publication of this project on readytensor.ai. [Click here](https://app.readytensor.ai/publications/rag-based-research-paper-chatbot-D2mnoaeo4HhR) and do check it out!
+
+## 🤝 Let's Connect!
+If you’d like to collaborate, give feedback, or just say hi — don’t hesitate to reach out or connect with me on [LinkedIn](https://www.linkedin.com/in/om-more-b802b2281/).

app.py

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+import os, uuid, gc
+from functools import lru_cache
+
+from flask import Flask, render_template, request, jsonify
+from werkzeug.utils import secure_filename
+from dotenv import load_dotenv
+import PyPDF2
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+# from langchain_huggingface import (
+#     HuggingFaceEmbeddings,
+#     HuggingFaceEndpoint,
+#     ChatHuggingFace,
+# )
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+
+load_dotenv()
+UPLOAD_FOLDER = os.path.join("/tmp", "uploads")
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+os.environ["HF_HOME"] = "/tmp/huggingface"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface/transformers"
+os.environ["HF_DATASETS_CACHE"] = "/tmp/huggingface/datasets"
+
+
+app = Flask(__name__)
+app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
+qa_chains: dict[str, RetrievalQA] = {}
+
+@lru_cache(maxsize=1)
+def get_embedder():
+    return HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+
+@lru_cache(maxsize=1)
+def get_llm():
+    # return ChatHuggingFace(
+    #     llm=HuggingFaceEndpoint(
+    #         repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    #         temperature=0.3,
+    #         max_new_tokens=512,
+    #     )
+    # )
+    return ChatGroq(
+        model_name="llama-3.1-8b-instant", # other options - llama-3.3-70b-versatile(very accurate), mistral-saba-24b(unlimited)
+        temperature=0.3,
+        max_tokens=512,
+    )
+
+def extract_text_from_pdf(path: str) -> str:
+    with open(path, "rb") as f:
+        reader = PyPDF2.PdfReader(f)
+        return "".join(p.extract_text() or "" for p in reader.pages)
+
+def build_qa_for_pdf(path: str) -> RetrievalQA:
+    text = extract_text_from_pdf(path)
+    chunks = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\
+             .split_text(text)
+
+    store = FAISS.from_texts(chunks, get_embedder())
+    retriever = store.as_retriever(search_type="mmr", search_kwargs={"k": 5})
+    return RetrievalQA.from_chain_type(llm=get_llm(), retriever=retriever)
+
+@app.route("/healthz")
+def healthz():
+    return "ok", 200
+
+@app.route("/", methods=["GET"])
+def index():
+    return render_template("index.html")
+
+@app.post("/upload")
+def upload_file():
+    file = request.files.get("file")
+    if not file or not file.filename.lower().endswith(".pdf"):
+        return jsonify({"error": "Please upload a valid PDF."}), 400
+
+    filename = f"{uuid.uuid4()}_{secure_filename(file.filename)}"
+    path = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+    file.save(path)
+
+    try:
+        qa_chains[filename] = build_qa_for_pdf(path)
+        return jsonify({"file_id": filename}), 200
+    except Exception as e:
+        return jsonify({"error": f"Error building chain: {e}"}), 500
+
+@app.post("/ask")
+def ask_question():
+    data = request.get_json(force=True)
+    file_id  = data.get("file_id")
+    question = (data.get("question") or "").strip()
+
+    if not file_id or not question:
+        return jsonify({"error": "Missing file_id or question."}), 400
+
+    qa_chain = qa_chains.get(file_id)
+    if not qa_chain:
+        return jsonify({"error": "File not found or expired."}), 404
+
+    try:
+        result = qa_chain.invoke({"query": question})
+        answer = result["result"] if isinstance(result, dict) else result
+        gc.collect()
+        return jsonify({"answer": answer}), 200
+    except Exception as e:
+        return jsonify({"error": f"Error: {e}"}), 500
+
+if __name__ == "__main__":
+    app.run(debug=True)

pdf_extractor.py

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+import os
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+import PyPDF2
+
+# Set your Hugging Face API token
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
+
+# Load and split PDF
+def extract_text_from_pdf(file_path):
+    with open(file_path, 'rb') as file:
+        reader = PyPDF2.PdfReader(file)
+        text = ''
+        for page in reader.pages:
+            text += page.extract_text()
+        return text
+
+pdf_text = extract_text_from_pdf("IEEEpaper.pdf")
+
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+chunks = splitter.split_text(pdf_text)
+
+from langchain_huggingface import HuggingFaceEmbeddings
+embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+vectorstore = FAISS.from_texts(chunks, embedding)
+
+# ✅ Use HuggingFaceEndpoint correctly
+hf_llm = HuggingFaceEndpoint(
+    repo_id="HuggingFaceH4/zephyr-7b-alpha",
+    temperature=0.5,
+    max_new_tokens=512,
+    huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"]
+)
+
+llm = ChatHuggingFace(llm=hf_llm)
+
+qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
+response = qa.run("Who is the author?")
+
+# print("Total Chunks:", len(chunks))
+# print("First Chunk:", chunks[0] if chunks else "No chunks extracted")

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>PDF Chatbot</title>
+  <script src="https://cdn.tailwindcss.com"></script>
+  <link rel="icon" href="favicon.ico" type="image/x-icon">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.7.2/css/all.min.css"
+    referrerpolicy="no-referrer">
+</head>
+
+<body class="m-0 bg-white">
+  <div class="bg-gradient-to-b from-indigo-200 via-white to-indigo-100">
+  <div class="bg-gradient-to-b from-indigo-50 via-white to-indigo-100">
+
+    <!-- Header -->
+    <header class="fixed inset-x-0 top-0 z-50 bg-white/70 backdrop-blur-md shadow-sm">
+      <div class="max-w-7xl mx-auto px-6 py-3 flex items-center justify-between">
+
+        <a href="/" class="text-xl sm:text-2xl font-bold tracking-tight text-indigo-600">
+          AskBot.AI
+        </a>
+
+        <nav class="hidden sm:flex items-center space-x-6 text-sm font-medium">
+          <a href="#chat" class="text-gray-600 hover:text-indigo-600 transition">Chat</a>
+          <a href="#features" class="text-gray-600 hover:text-indigo-600 transition">Features</a>
+          <a href="#tech" class="text-gray-600 hover:text-indigo-600 transition">Tech</a>
+          <a href="#connect" class="text-gray-600 hover:text-indigo-600 transition">Connect</a>
+        </nav>
+
+        <button class="sm:hidden inline-flex items-center justify-center h-8 w-8 text-gray-600 hover:text-indigo-600">
+          <svg fill="none" viewBox="0 0 24 24" stroke="currentColor">
+            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 6h16M4 12h16M4 18h16" />
+          </svg>
+        </button>
+      </div>
+    </header>
+    <!-- /Header -->
+
+
+    <!-- <main class=""> -->
+      <section>
+        <div class="bg-white">
+          <!-- <div class="relative isolate overflow-hidden bg-gradient-to-b from-gray-200/50"> -->
+          <div class="mx-auto max-w-7xl pb-16 pt-4 sm:pb-24 lg:grid lg:grid-cols-2 lg:gap-x-8 lg:px-8 lg:py-10">
+            <div class="px-6 lg:px-0 lg:pt-4">
+              <div class="mx-auto max-w-2xl">
+                <div class="max-w-lg">
+                  <h1 class="mt-10 text-4xl font-bold tracking-tight text-gray-900 sm:text-5xl">Upload PDFs, <br>
+                    Research Papers & <br> Ask me Anything!!!</h1>
+                  <p class="mt-6 text-lg leading-8 text-gray-600">My chatbot reads your research paper
+                    or any PDFs and answers questions using AI model.</p>
+                </div>
+              </div>
+            </div>
+
+            <div class="mt-20 lg:mt-0 flex justify-center items-center">
+              <img
+                src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751450446/giphy_sab0e8.webp"
+                class="drop-shadow-2xl" height="350" width="550" alt="chatbot image">
+            </div>
+          </div>
+        </div>
+  </div>
+  </section>
+
+  <!-- Chat Section -->
+  <section id="chat" class="py-20">
+    <div class="max-w-6xl mx-auto px-6">
+
+      <div class="text-center mb-10">
+        <h2 class="text-3xl font-bold text-gray-800">Chat With Your PDF</h2>
+        <p class="mt-2 text-gray-600 text-sm">Upload a research paper and ask any question. Powered by AI.</p>
+      </div>
+
+      <div id="uploadChatCard" class="bg-white shadow-xl border border-gray-200 rounded-xl p-6 space-y-6">
+        <div id="uploader" class="space-y-4 text-center">
+          <div class="flex items-center justify-center gap-4 flex-col sm:flex-row">
+            <label for="pdfFile"
+              class="cursor-pointer bg-indigo-500 hover:bg-indigo-600 text-white px-6 py-2 rounded shadow text-sm transition">
+              Choose PDF
+            </label>
+            <span id="fileNameDisplayText" class="text-sm text-gray-600 italic">No file chosen</span>
+            <input id="pdfFile" type="file" accept=".pdf" class="hidden">
+          </div>
+
+          <button id="btnUpload" disabled class="bg-indigo-500 hover:bg-indigo-600 text-white px-6 py-2 rounded">
+            Upload PDF
+          </button>
+        </div>
+
+        <!-- Chat Interface -->
+        <div id="chatInterface" class="hidden space-y-4">
+          <div class="flex justify-between items-center max-w-3xl mx-auto">
+            <div id="fileNameDisplay" class="text-sm text-gray-600 font-medium italic"></div>
+            <button id="reuploadBtn" class="text-sm text-indigo-600 hover:underline">
+              Upload Another PDF
+            </button>
+          </div>
+
+          <!-- Chat Box -->
+          <div id="chatBox"
+            class="space-y-3 max-w-3xl max-h-96 overflow-y-auto px-4 py-4 border rounded-lg bg-white shadow-inner mx-auto">
+          </div>
+
+          <form id="chatForm" class="flex max-w-3xl mx-auto">
+            <input id="msgInput" autocomplete="off" placeholder="Ask a question..."
+              class="flex-1 rounded-l-lg border-gray-300 px-4 py-3 shadow" />
+            <button class="bg-indigo-500 hover:bg-indigo-600 text-white px-6 rounded-r-lg">
+              Send
+            </button>
+          </form>
+        </div>
+      </div>
+
+      <!-- Result -->
+      {% if results %}
+      <div class="mt-6 bg-white p-6 rounded-lg shadow border border-gray-200">
+        <h2 class="text-lg font-semibold text-gray-800 mb-2">Answer:</h2>
+        <p class="text-gray-700 whitespace-pre-wrap">{{ results }}</p>
+      </div>
+      {% endif %}
+
+    </div>
+  </section>
+
+  <!-- Features Section -->
+  <section id="features" class="py-20">
+    <div class="max-w-7xl mx-auto px-6">
+
+      <div class="text-center mb-12">
+        <h2 class="text-3xl font-bold text-gray-800">Features</h2>
+        <p class="mt-2 text-gray-600 text-sm">What makes our PDF Chatbot stand out</p>
+      </div>
+
+      <div class="grid grid-cols-1 md:grid-cols-3 gap-8">
+
+        <!-- Instant PDF Reading -->
+        <div
+          class="bg-white bg-opacity-80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751357240/feature_img_1_nhlopv.jpg"
+            alt="Feature 1" class="mx-auto h-28 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">Instant PDF Understanding</h3>
+          <p class="text-sm text-gray-600">Ask any question and get accurate responses
+            based on the uploaded PDF content in seconds.</p>
+        </div>
+
+        <!-- Multiple File-Type Support -->
+        <div
+          class="bg-white bg-opacity-80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751357240/feature_img_2_b8feaa.jpg"
+            alt="Feature 2" class="mx-auto h-28 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">Multiple File-Type Support</h3>
+          <p class="text-sm text-gray-600">Upload research papers, eBooks, or reports — 
+            the chatbot can handle various types of academic PDFs.</p>
+        </div>
+
+        <!-- AI-Powered Accuracy -->
+        <div
+          class="bg-white bg-opacity-80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751357580/feature_img_3_qeecda.jpg"
+            alt="Feature 3" class="mx-auto h-28 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">AI-Powered Accuracy</h3>
+          <p class="text-sm text-gray-600">Built with powerful AI models to understand complex contexts, summaries,
+            and
+            in-depth queries.</p>
+        </div>
+
+      </div>
+    </div>
+  </section>
+
+
+  <!-- ========== Tech Stack & AI Models ========== -->
+  <section id="tech" class="py-20">
+    <div class="max-w-7xl mx-auto px-6">
+      <div class="text-center mb-12">
+        <h2 class="text-3xl font-bold text-gray-800">Tech Stack &amp; AI Models</h2>
+        <p class="mt-2 text-gray-600 text-sm">
+          The key Technologies and AI models that power this chatbot
+        </p>
+      </div>
+
+      <!-- Python and Flask -->
+      <div class="grid grid-cols-1 md:grid-cols-3 gap-8">
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://cdn-icons-png.flaticon.com/512/5968/5968350.png" alt="Python Flask"
+            class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">Python & Flask</h3>
+          <p class="text-sm text-gray-600">Handles backend, file uploads & AI Responses.</p>
+        </div>
+
+        <!-- LangChain -->
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751439498/download_wabh02.png" alt="LangChain"
+            class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">LangChain</h3>
+          <p class="text-sm text-gray-600">LangChain manages PDF processing & LLM calls.</p>
+        </div>
+
+        <!-- Embeddings -->
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="MiniLM Embeddings"
+            class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">Embedding Model: all-MiniLM-L6-v2 </h3>
+          <p class="text-sm text-gray-600">Converts Text into Numerical Vectors.</p>
+        </div>
+        
+        <!-- FAISS -->
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751439632/download_so6va6.png" alt="FAISS"
+            class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">FAISS Vector Store</h3>
+          <p class="text-sm text-gray-600">Enables Fast Similarity Search of vectors in PDFs.</p>
+        </div>
+
+
+        <!-- LLM -->
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751520819/download_cbh1yx.jpg" alt="Mixtral LLM"
+            class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">LLM: llama-3.1-8b-instant</h3>
+          <p class="text-sm text-gray-600">Generates correct answers from extracted PDF chunks.</p>
+        </div>
+
+        <!-- Tailwind CSS -->
+        <div
+          class="bg-white/80 backdrop-blur-md border border-gray-200 rounded-xl p-6 shadow-lg text-center transition hover:shadow-xl">
+          <img src="https://res.cloudinary.com/dekujzz4s/image/upload/v1751439818/download_qgtwpn.png"
+            alt="Tailwind CSS" class="mx-auto h-20 mb-4 object-contain" />
+          <h3 class="text-xl font-semibold text-gray-800 mb-2">Tailwind CSS</h3>
+          <p class="text-sm text-gray-600">Stylish and fully responsive frontend.</p>
+        </div>
+      </div>
+    </div>
+  </section>
+  <!-- </main> -->
+  </div>
+
+  <!-- ========== Connect / Footer Section ========== -->
+  <footer id="connect" class="w-full bg-gray-900 text-gray-300">
+    <div class="max-w-7xl mx-auto px-6 py-8 text-center">
+
+      <h2 class="text-3xl font-bold text-white mb-2">Connect With Me</h2>
+      <p class="text-gray-400 mb-8">Feel free to reach out on any platform!</p>
+
+      <div class="flex justify-center space-x-8 text-2xl">
+        <!-- LinkedIn -->
+        <a href="https://www.linkedin.com/in/om-more-b802b2281/" target="_blank" rel="noopener noreferrer"
+          class="hover:text-blue-400 transition">
+          <i class="fa-brands fa-linkedin"></i>
+        </a>
+
+        <!-- GitHub -->
+        <a href="https://github.com/ommore86/" target="_blank" rel="noopener noreferrer"
+          class="hover:text-white transition">
+          <i class="fa-brands fa-github"></i>
+        </a>
+
+        <!-- Email -->
+        <a href="mailto:omgmore2005@gmail.com" class="hover:text-red-400 transition">
+          <i class="fa-solid fa-envelope"></i>
+        </a>
+      </div>
+
+      <div class="mt-10 border-t border-gray-700 pt-6 text-sm text-gray-500">
+        &copy; 2025 Om More | All rights reserved
+      </div>
+  </footer>
+
+  <script>
+    let fileId = null;
+
+    const pdfInput = document.getElementById("pdfFile");
+    const btnUpload = document.getElementById("btnUpload");
+    pdfInput.onchange = () => {
+      const file = pdfInput.files[0];
+      if (file) {
+        document.getElementById("fileNameDisplayText").textContent = file.name;
+        btnUpload.disabled = false;
+      } else {
+        document.getElementById("fileNameDisplayText").textContent = "No file chosen";
+        btnUpload.disabled = true;
+      }
+    };
+
+    btnUpload.onclick = async () => {
+      const file = pdfInput.files[0];
+      const formData = new FormData();
+      formData.append("file", file);
+      btnUpload.disabled = true;
+      btnUpload.textContent = "Uploading…";
+
+      const res = await fetch("/upload", { method: "POST", body: formData });
+      const data = await res.json();
+      if (res.ok) {
+        fileId = data.file_id;
+        document.getElementById("fileNameDisplay").textContent = pdfInput.files[0].name;
+
+        document.getElementById("uploader").classList.add("hidden");
+        document.getElementById("chatInterface").classList.remove("hidden");
+        document.getElementById("fileNameDisplay").textContent = `📄 ${file.name}`;
+      } else {
+        alert(data.error || "Upload failed");
+        btnUpload.disabled = false;
+        btnUpload.textContent = "Upload PDF";
+      }
+    };
+
+
+    // Chat logic
+    const chatBox = document.getElementById("chatBox");
+    const chatForm = document.getElementById("chatForm");
+    const msgInput = document.getElementById("msgInput");
+
+    chatForm.addEventListener("submit", async e => {
+      e.preventDefault();
+      const q = msgInput.value.trim();
+      if (!q) return;
+      appendBubble(q, "user");
+      msgInput.value = "";
+
+      // Showing bubble while waiting
+      const thinkingBubble = addThinkingBubble();
+
+      let answerText = "";
+      try {
+        const res = await fetch("/ask", {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify({ question: q, file_id: fileId }),
+        });
+        const data = await res.json();
+        answerText = data.answer || data.error;
+      } catch (err) {
+        answerText = "Error fetching answer.";
+      }
+
+      // Replacing placeholder text
+      thinkingBubble.textContent = answerText;
+    });
+
+    function appendBubble(text, who) {
+      const bubble = document.createElement("div");
+      bubble.className = `w-fit rounded-2xl px-5 py-3 shadow break-words
+                      ${who === "user"
+          ? "ml-auto bg-indigo-500 text-white"
+          : "mr-auto bg-gray-200 text-gray-900"}`;
+      bubble.textContent = text;
+      chatBox.appendChild(bubble);
+      chatBox.scrollTop = chatBox.scrollHeight;
+      return bubble;
+    }
+
+    function addThinkingBubble() {
+      const bubble = document.createElement("div");
+      bubble.className = "w-fit rounded-2xl px-5 py-3 shadow mr-auto bg-gray-200 text-gray-900";
+      bubble.innerHTML = `<span class="animate-pulse">Thinking…</span>`;
+      chatBox.appendChild(bubble);
+      chatBox.scrollTop = chatBox.scrollHeight;
+      return bubble;
+    }
+
+    document.getElementById("reuploadBtn").onclick = () => {
+      fileId = null;
+      pdfInput.value = "";
+
+      document.getElementById("uploader").classList.remove("hidden");
+      document.getElementById("chatInterface").classList.add("hidden");
+
+      document.getElementById("fileNameDisplay").textContent = "";
+      document.getElementById("chatBox").innerHTML = "";
+
+      btnUpload.disabled = true;
+      btnUpload.textContent = "Upload PDF";
+    };
+  </script>
+
+</body>
+
+</html>

templates/site.webmanifest

@@ -0,0 +1 @@
+{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"}

uploads/.huggingface.yml

@@ -0,0 +1 @@
+sdk: "docker"