Add all files including PDFs with Git LFS

.env

@@ -0,0 +1,2 @@
+PINECONE_API_KEY = "pcsk_3wGqdG_Srx49fCB4fS64N23j8a32jVeVYhW99ABaHWubzrhMWAbntQizXRXrDZpNFerMaP"
+EURON_API_KEY = "euri-bfbd93d283a5b6c3e5f62b04d5ae9242f98c07aef2f2b11971a9ed4a8ff603d3"

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+*.pdf filter=lfs diff=lfs merge=lfs -text

data/Medical_book.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:753cd53b7a3020bbd91f05629b0e3ddcfb6a114d7bbedb22c2298b66f5dd00cc
+size 16127037

data/amdnet23.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3d039d4b2fa76b225171a4d9bc86447e1e56fe9a22f4fed9ecc0a16a3e9224b
+size 1507090

execution/euron_html.py

@@ -0,0 +1,70 @@
+from flask import Flask, render_template, request
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from src.prompt import *
+from src.euron_chat import EuronChatModel
+from dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file (for local dev)
+load_dotenv()
+
+# Verify API keys (optional: remove in full production if confident)
+pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+euron_api_key = os.environ.get("EURON_API_KEY")
+if not pinecone_api_key or not euron_api_key:
+    raise ValueError("Missing PINECONE_API_KEY or EURON_API_KEY in environment variables.")
+
+app = Flask(__name__)
+
+# -----------------------------
+# Load embeddings and Pinecone index
+# -----------------------------
+embeddings = download_hugging_face_embeddings()
+index_name = "portfolio"
+
+docsearch = PineconeVectorStore.from_existing_index(
+    index_name=index_name,
+    embedding=embeddings
+)
+
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+# -----------------------------
+# Initialize EuronChatModel & RAG chain
+# -----------------------------
+chatModel = EuronChatModel()
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+# -----------------------------
+# Flask routes
+# -----------------------------
+@app.route("/")
+def index():
+    return render_template('index.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg = request.form["msg"]
+    print("User Input:", msg)
+
+    response = rag_chain.invoke({"input": msg})
+    print("Response:", response["answer"])
+
+    return str(response["answer"])
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 2025))  # Use Render's PORT or default to 8080 locally
+    app.run(host="0.0.0.0", port=port, debug=False)  # Disable debug for production

execution/euron_streamlit.py

@@ -0,0 +1,294 @@
+import execution.euron_streamlit as st
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from src.prompt import *
+from src.euron_chat import EuronChatModel
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+pinecone_api_key = os.getenv("PINECONE_API_KEY")
+euron_api_key = os.getenv("EURON_API_KEY")
+
+if not pinecone_api_key or not euron_api_key:
+    st.error("Missing keys in environment")
+    st.stop()
+
+# ✅ Minimal CSS – Only apply Fira Code font globally + Sidebar styling
+st.markdown("""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Fira+Code:wght@300;400;500;600;700&display=swap');
+
+/* Global font */
+* {
+    font-family: 'Fira Code', monospace !important;
+}
+
+/* Sidebar background & padding */
+[data-testid="stSidebar"] {
+    background-color: #f0f2f6 !important; /* Light ash */
+    border-radius: 0 20px 20px 0;
+    padding: 25px 15px;
+}
+
+/* Sidebar title */
+[data-testid="stSidebar"] .css-1d391kg {
+    color: #1f2937 !important;
+    font-size: 2rem;
+    font-weight: 700;
+    text-align: center;
+    margin-bottom: 25px;
+}
+
+/* Sidebar radio buttons as styled buttons */
+[data-testid="stSidebar"] .stRadio > div {
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+}
+[data-testid="stSidebar"] .stRadio label {
+    background-color: rgba(31,41,55,0.1) !important; /* subtle gray */
+    color: #1f2937 !important;
+    padding: 12px 18px;
+    border-radius: 12px;
+    transition: all 0.3s ease;
+    cursor: pointer;
+    font-size: 1.1rem;
+    font-weight: 500;
+}
+[data-testid="stSidebar"] .stRadio label:hover {
+    background-color: rgba(31,41,55,0.2) !important;
+    transform: translateX(5px);
+    box-shadow: 0 4px 8px rgba(0,0,0,0.2);
+}
+[data-testid="stSidebar"] .stRadio input:checked + label {
+    background-color: #4B5563 !important; /* Darker for selected */
+    color: #ffffff !important;
+}
+
+/* Sidebar image */
+[data-testid="stSidebar"] img {
+    border-radius: 15px;
+    margin-bottom: 20px;
+}
+
+/* Sidebar divider */
+[data-testid="stSidebar"] hr {
+    border: 1px solid rgba(0,0,0,0.2) !important;
+    margin: 20px 0;
+}
+
+/* Sidebar caption */
+[data-testid="stSidebar"] .css-1x8i1po {
+    text-align: center;
+    font-size: 0.9rem;
+    color: #4b5563 !important;
+    margin-top: 15px;
+}
+</style>
+""", unsafe_allow_html=True)
+
+# Sidebar Menu
+with st.sidebar:
+    # st.title("🌟 Navigation")
+    st.image("static/icon.png", width=120)  # Image in sidebar
+    st.markdown("---")
+    menu = st.radio("Go to", ["About Me", "Chatbot"], label_visibility="hidden")
+    st.markdown("---")
+    st.caption("© 2025 Shanin Hossain")
+    # st.markdown("""
+    # <div style="text-align:center; margin-top: 15px;">
+    #     <a href="https://www.linkedin.com/in/shanin-hossain" target="_blank">
+    #         <img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/linkedin.svg" width="25" style="margin:0;">
+    #     </a>
+    #     <a href="https://github.com/shaninhossain" target="_blank">
+    #         <img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/github.svg" width="25" style="margin:0;">
+    #     </a>
+    #     <a href="https://www.facebook.com/shaninhossain" target="_blank">
+    #         <img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/facebook.svg" width="25" style="margin:0;">
+    #     </a>
+    # </div>
+    # """, unsafe_allow_html=True)
+
+
+
+# Initialize session state
+if "rag_chain" not in st.session_state:
+    st.session_state.rag_chain = None
+    st.session_state.embeddings = None
+    st.session_state.retriever = None
+if "messages" not in st.session_state:
+    st.session_state.messages = []  # Chat history
+
+def initialize_rag():
+    try:
+        if st.session_state.rag_chain is None:
+            st.session_state.embeddings = download_hugging_face_embeddings()
+            index_name = "portfolio"
+            docsearch = PineconeVectorStore.from_existing_index(
+                index_name=index_name,
+                embedding=st.session_state.embeddings
+            )
+            st.session_state.retriever = docsearch.as_retriever(
+                search_type="similarity", search_kwargs={"k": 3}
+            )
+            chatModel = EuronChatModel()
+            prompt = ChatPromptTemplate.from_messages(
+                [
+                    ("system", system_prompt),
+                    ("human", "{input}"),
+                ]
+            )
+            question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+            st.session_state.rag_chain = create_retrieval_chain(
+                st.session_state.retriever, question_answer_chain
+            )
+    except Exception as e:
+        st.error(f"Error initializing RAG: {str(e)}")
+        st.stop()
+
+# About Me Section
+# About Me Section
+if menu == "About Me":
+    st.write("Hey there 👋")
+    st.title("👨 I'm Shanin Hossain")
+    st.markdown("""
+    I'm an AI Engineer & Research Assistant passionate about:
+    - Machine Learning, Deep Learning, and Generative AI  
+    - Computer Vision & Natural Language Processing  
+    - Healthcare Informatics and Medical Imaging  
+    
+    📌 I have worked on multiple AI projects, including OCR, Retrieval-Augmented Generation (RAG), and hybrid graph networks.  
+    """)
+    st.success("👉 Navigate to **Chatbot** in the sidebar to chat with me!")
+    st.header("My Projects", divider="gray")
+
+    # --- Project Data ---
+    projects = [
+        {
+            "name": "Brain Glioma Grading System",
+            "description": "Developed a hybrid graph neural network to grade glioma tumors from medical imaging data.",
+            "tech_stack": ["PyTorch Geometric", "Graph Neural Networks", "Medical Imaging", "Python"]
+        },
+        {
+            "name": "OCR Automation",
+            "description": "Built an OCR pipeline for document image understanding and text extraction.",
+            "tech_stack": ["YOLOv8", "OpenCV", "Tesseract OCR", "FastAPI"]
+        },
+        {
+            "name": "Portfolio Chatbot",
+            "description": "Created a RAG-powered chatbot integrated with Pinecone & custom embeddings for Q&A over portfolio data.",
+            "tech_stack": ["Streamlit", "LangChain", "Pinecone", "Hugging Face"]
+        },
+    ]
+
+    # --- Render Projects with Badges ---
+    for project in projects:
+        with st.container():
+            st.subheader(project["name"])
+            st.write(project["description"])
+
+            # Create badges for each tech in stack
+            badges_html = " ".join([
+                f"<span class='badge'>{tech}</span>" for tech in project["tech_stack"]
+            ])
+
+            st.markdown(
+                f"<div style=''>{badges_html}</div>",
+                unsafe_allow_html=True
+            )
+
+            st.markdown("---")
+
+    # --- Badge Styling ---
+    st.markdown("""
+    <style>
+    .badge {
+        display: inline-block;
+        padding: 6px 12px;
+        font-size: 0.70rem;
+        font-weight: 600;
+        color: white;
+        border-radius: 12px;
+    }
+
+    /* Randomized color palette */
+    .badge:nth-child(5n+1) { background-color: #2563EB; }  /* Blue */
+    .badge:nth-child(5n+2) { background-color: #059669; }  /* Green */
+    .badge:nth-child(5n+3) { background-color: #D97706; }  /* Orange */
+    .badge:nth-child(5n+4) { background-color: #9333EA; }  /* Purple */
+    .badge:nth-child(5n+5) { background-color: #DC2626; }  /* Red */
+    </style>
+    """, unsafe_allow_html=True)
+
+
+    st.header("Publications", divider="gray")
+
+    # --- Publication Data ---
+    publications = [
+        {
+            "title": "Automated Detection of Age-Related Macular Degeneration (AMD) Using Deep Learning",
+            "venue": "Journal of Medical Imaging & Health Informatics, 2023",
+            "description": "Published a deep learning-based pipeline to detect age-related macular degeneration from retinal images.",
+            "link": "https://doi.org/xxxxxx"
+        },
+        {
+            "title": "Using Hyperdimensional Computing to Extract Features for the Detection of Type 2 Diabetes",
+            "venue": "Conference on Health Informatics, 2024 (Under Review)",
+            "description": "Explored hyperdimensional computing techniques to improve detection of Type 2 Diabetes from clinical data.",
+            "link": ""
+        }
+    ]
+
+    # --- Render Publications ---
+    for pub in publications:
+        with st.container():
+            st.subheader(pub["title"])
+            st.caption(pub["venue"])
+            st.write(pub["description"])
+
+            if pub["link"]:
+                st.markdown(f"[🔗 View Publication]({pub['link']})")
+
+            st.markdown("---")
+
+
+
+# Chatbot Section
+elif menu == "Chatbot":
+    st.title("🤖 Shanin Chatbot")
+    st.write("Ask me anything about my portfolio!")
+
+    # Display previous messages
+    for msg in st.session_state.messages:
+        with st.chat_message(msg["role"]):
+            st.write(msg["content"])
+
+    # Input box
+    if prompt := st.chat_input("Type your question..."):
+        # Add user message
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.write(prompt)
+
+        # Initialize RAG if needed
+        if st.session_state.rag_chain is None:
+            with st.spinner("Initializing RAG pipeline..."):
+                initialize_rag()
+
+        try:
+            with st.spinner("Generating response..."):
+                response = st.session_state.rag_chain.invoke({"input": prompt})
+                answer = response["answer"]
+
+                # Add assistant response
+                st.session_state.messages.append({"role": "assistant", "content": answer})
+                with st.chat_message("assistant"):
+                    st.write(answer)
+
+        except Exception as e:
+            st.error(f"Error processing request: {str(e)}")

execution/gemma_Ollama.py

@@ -0,0 +1,82 @@
+from flask import Flask, render_template, request, jsonify
+from flask_cors import CORS
+from dotenv import load_dotenv
+import os
+from langchain_pinecone import PineconeVectorStore
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.chat_models import ChatOllama
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Download the Embeddings from HuggingFace
+def download_hugging_face_embeddings():
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')  # this model returns 384 dimensions
+    return embeddings
+
+# Define the system prompt
+system_prompt = (
+    "You are an intelligent Personal Portfolio Assistant that answers questions about the user's background, work, and projects. "
+    "Use the retrieved context below to provide accurate and natural responses. "
+    "If the context does not contain the answer, respond with 'I'm not sure about that.' "
+    "Keep your answer concise."
+    "\n\n"
+    "Context:\n{context}"
+)
+
+
+load_dotenv()
+
+pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+if not pinecone_api_key:
+    raise ValueError("Missing PINECONE_API_KEY in environment variables.")
+
+app = Flask(__name__)
+CORS(app)  # ✅ Allow external web app to access this Flask API
+
+# Load embeddings
+embeddings = download_hugging_face_embeddings()
+index_name = "portfolio"
+
+docsearch = PineconeVectorStore.from_existing_index(index_name=index_name, embedding=embeddings)
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+# Model
+chatModel = ChatOllama(model="gemma3:1b", temperature=0.1, max_tokens=512)
+
+# Prompt
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+
+@app.route("/")
+def index():
+    return "✅ RAG server running"
+
+
+@app.route("/get", methods=["POST"])
+def chat():
+    user_msg = request.form.get("msg") or request.json.get("msg")
+
+    if not user_msg:
+        return jsonify({"error": "No message sent"}), 400
+
+    try:
+        response = rag_chain.invoke({"input": user_msg})
+        answer = response.get("answer", "Sorry, I couldn’t find an answer.")
+        return jsonify({"reply": answer})
+    except Exception as e:
+        print("Error:", e)
+        return jsonify({"reply": f"Server Error: {str(e)}"})
+
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 2025))
+    app.run(host="0.0.0.0", port=port, debug=False)

execution/web.py

@@ -0,0 +1,72 @@
+       
+        
+from flask import Flask, render_template, request
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from src.prompt import *
+from src.euron_chat import EuronChatModel
+from dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file (for local dev)
+load_dotenv()
+
+# Verify API keys (optional: remove in full production if confident)
+pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+euron_api_key = os.environ.get("EURON_API_KEY")
+if not pinecone_api_key or not euron_api_key:
+    raise ValueError("Missing PINECONE_API_KEY or EURON_API_KEY in environment variables.")
+
+app = Flask(__name__)
+
+# -----------------------------
+# Load embeddings and Pinecone index
+# -----------------------------
+embeddings = download_hugging_face_embeddings()
+index_name = "portfolio"
+
+docsearch = PineconeVectorStore.from_existing_index(
+    index_name=index_name,
+    embedding=embeddings
+)
+
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+# -----------------------------
+# Initialize EuronChatModel & RAG chain
+# -----------------------------
+chatModel = EuronChatModel()
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+# -----------------------------
+# Flask routes
+# -----------------------------
+@app.route("/")
+def index():
+    return render_template('chat.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg = request.form["msg"]
+    print("User Input:", msg)
+
+    response = rag_chain.invoke({"input": msg})
+    print("Response:", response["answer"])
+
+    return str(response["answer"])
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 8080))  # Use Render's PORT or default to 8080 locally
+    app.run(host="0.0.0.0", port=port, debug=False)  # Disable debug for production

main.py

@@ -0,0 +1,70 @@
+from flask import Flask, render_template, request
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from src.prompt import *
+from src.euron_chat import EuronChatModel
+from dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file (for local dev)
+load_dotenv()
+
+# Verify API keys (optional: remove in full production if confident)
+pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+euron_api_key = os.environ.get("EURON_API_KEY")
+if not pinecone_api_key or not euron_api_key:
+    raise ValueError("Missing PINECONE_API_KEY or EURON_API_KEY in environment variables.")
+
+app = Flask(__name__)
+
+# -----------------------------
+# Load embeddings and Pinecone index
+# -----------------------------
+embeddings = download_hugging_face_embeddings()
+index_name = "medicaldb"
+
+docsearch = PineconeVectorStore.from_existing_index(
+    index_name=index_name,
+    embedding=embeddings
+)
+
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+# -----------------------------
+# Initialize EuronChatModel & RAG chain
+# -----------------------------
+chatModel = EuronChatModel()
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(chatModel, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+# -----------------------------
+# Flask routes
+# -----------------------------
+@app.route("/")
+def index():
+    return render_template('index.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg = request.form["msg"]
+    print("User Input:", msg)
+
+    response = rag_chain.invoke({"input": msg})
+    print("Response:", response["answer"])
+
+    return str(response["answer"])
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 2025))  # Use Render's PORT or default to 8080 locally
+    app.run(host="0.0.0.0", port=port, debug=False)  # Disable debug for production

requirements.txt

@@ -0,0 +1,13 @@
+langchain==0.3.26
+sentence-transformers==4.1.0
+pypdf==5.6.1 
+python-dotenv==1.1.0
+langchain-pinecone==0.2.8 
+langchain-community==0.3.26
+langchain-huggingface==0.1.0  
+requests==2.32.3  
+# streamlit==1.39.0  
+flask==3.1.1 
+# ollama==0.6.0
+
+-e .  # for this setup.py file

setup.py

@@ -0,0 +1,10 @@
+from setuptools import find_packages, setup
+
+setup(
+    name="Medical Chatbot",
+    version="0.1.0",
+    author="Aiyub Ali",
+    author_email="aiyubali15-13456@diu.edu.bd",
+    packages=find_packages(),
+    install_requires=[]
+)

src/euron_chat.py

@@ -0,0 +1,152 @@
+import os
+import requests
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessage
+from langchain_core.outputs import ChatResult, ChatGeneration
+from typing import List, Optional
+
+def generate_completion(messages, model="gpt-4.1-nano", max_tokens=1000, temperature=0.7):
+    """
+    Generate a response from Euron API
+    """
+    euron_api_key = os.getenv("EURON_API_KEY", "").strip()
+    if not euron_api_key:
+        raise ValueError("EURON_API_KEY is missing. Please set it in your environment.")
+
+    url = "https://api.euron.one/api/v1/euri/chat/completions"
+    headers = {
+       "Content-Type": "application/json",
+       "Authorization": f"Bearer {euron_api_key}"
+    }
+
+
+    # Convert LangChain messages to API format
+    api_messages = []
+    for message in messages:
+        if hasattr(message, 'type'):
+            role = message.type
+            if role == "human":
+                role = "user"
+            elif role == "ai":
+                role = "assistant"
+            api_messages.append({"role": role, "content": message.content})
+        else:
+            api_messages.append(message)
+
+    payload = {
+        "messages": api_messages,
+        "model": model,
+        "max_tokens": max_tokens,
+        "temperature": temperature
+    }
+
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code != 200:
+        raise Exception(f"Euron API Error: {response.status_code} {response.text}")
+
+    return response.json()
+
+
+class EuronChatModel(BaseChatModel):
+    """
+    LangChain compatible chat model using Euron API
+    """
+    model_name: str = "gpt-4.1-nano"
+
+    def _generate(self, messages: List, stop: Optional[List[str]] = None) -> ChatResult:
+        response = generate_completion(messages, model=self.model_name)
+
+        # Extract AI message
+        ai_content = response['choices'][0]['message']['content']
+
+        # Wrap in LangChain objects
+        ai_message = AIMessage(content=ai_content)
+        generation = ChatGeneration(message=ai_message)
+
+        return ChatResult(generations=[generation])
+
+    def _llm_type(self) -> str:
+        return "euron-chat"
+
+
+# Optional: simple chat function
+def simple_chat_completion(user_message: str):
+    messages = [{"role": "user", "content": user_message}]
+    response = generate_completion(messages)
+    return response['choices'][0]['message']['content']
+
+
+# import os
+# import requests
+# from langchain_core.language_models import BaseChatModel
+# from langchain_core.messages import AIMessage
+# from langchain_core.outputs import ChatResult, ChatGeneration
+# from typing import List, Optional
+
+# EURON_API_KEY = os.environ.get("EURON_API_KEY")
+
+# def generate_completion(messages, model="gpt-4.1-nano", max_tokens=1000, temperature=0.7):
+#     """
+#     Generate a response from Euron API
+#     """
+#     url = "https://api.euron.one/api/v1/euri/chat/completions"
+#     headers = {
+#        "Content-Type": "application/json",
+#        "Authorization": f"Bearer {EURON_API_KEY}"
+#     }
+
+
+#     # Convert LangChain messages to API format
+#     api_messages = []
+#     for message in messages:
+#         if hasattr(message, 'type'):
+#             role = message.type
+#             if role == "human":
+#                 role = "user"
+#             elif role == "ai":
+#                 role = "assistant"
+#             api_messages.append({"role": role, "content": message.content})
+#         else:
+#             api_messages.append(message)
+
+#     payload = {
+#         "messages": api_messages,
+#         "model": model,
+#         "max_tokens": max_tokens,
+#         "temperature": temperature
+#     }
+
+#     response = requests.post(url, headers=headers, json=payload)
+#     if response.status_code != 200:
+#         raise Exception(f"Euron API Error: {response.status_code} {response.text}")
+
+#     return response.json()
+
+
+# class EuronChatModel(BaseChatModel):
+#     """
+#     LangChain compatible chat model using Euron API
+#     """
+#     model_name: str = "gpt-4.1-nano"
+
+#     def _generate(self, messages: List, stop: Optional[List[str]] = None) -> ChatResult:
+#         response = generate_completion(messages, model=self.model_name)
+
+#         # Extract AI message
+#         ai_content = response['choices'][0]['message']['content']
+
+#         # Wrap in LangChain objects
+#         ai_message = AIMessage(content=ai_content)
+#         generation = ChatGeneration(message=ai_message)
+
+#         return ChatResult(generations=[generation])
+
+#     def _llm_type(self) -> str:
+#         return "euron-chat"
+
+
+# # Optional: simple chat function
+# def simple_chat_completion(user_message: str):
+#     messages = [{"role": "user", "content": user_message}]
+#     response = generate_completion(messages)
+#     return response['choices'][0]['message']['content']

src/helper.py

@@ -0,0 +1,41 @@
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from typing import List
+from langchain.schema import Document
+
+# Extract Data From the PDF File
+def load_pdf_file(data):
+    loader = DirectoryLoader(data,
+                            glob="*.pdf",
+                            loader_cls=PyPDFLoader)
+    documents = loader.load()
+    return documents
+
+# Filter to minimal documents
+def filter_to_minimal_docs(docs: List[Document]) -> List[Document]:
+    """
+    Given a list of Document objects, return a new list of Document objects
+    containing only 'source' in metadata and the original page_content.
+    """
+    minimal_docs: List[Document] = []
+    for doc in docs:
+        src = doc.metadata.get("source")
+        minimal_docs.append(
+            Document(
+                page_content=doc.page_content,
+                metadata={"source": src}
+            )
+        )
+    return minimal_docs
+
+# Split the Data into Text Chunks
+def text_split(extracted_data):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+    text_chunks = text_splitter.split_documents(extracted_data)
+    return text_chunks
+
+# Download the Embeddings from HuggingFace
+def download_hugging_face_embeddings():
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')  # this model returns 384 dimensions
+    return embeddings

src/prompt.py

@@ -0,0 +1,9 @@
+# Define the system prompt
+system_prompt = (
+    "You are an intelligent Personal Portfolio Assistant that answers questions about the user's background, work, and projects. "
+    "Use the retrieved context below to provide accurate and natural responses. "
+    "If the context does not contain the answer, respond with 'I'm not sure about that.' "
+    "Keep your answer concise."
+    "\n\n"
+    "Context:\n{context}"
+)

src/store_index.py

@@ -0,0 +1,35 @@
+from dotenv import load_dotenv
+import os
+from pinecone import Pinecone
+from langchain_pinecone import PineconeVectorStore
+from src.helper import load_pdf_file, filter_to_minimal_docs, text_split, download_hugging_face_embeddings
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Retrieve Pinecone API key from environment variables
+pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+if not pinecone_api_key:
+    raise ValueError("PINECONE_API_KEY not found in environment variables")
+
+# Initialize Pinecone client
+pc = Pinecone(api_key=pinecone_api_key)
+
+# Set up index
+index_name = "portfolio"
+index = pc.Index(index_name)
+
+# Load and process documents
+extracted_data = load_pdf_file(data='data/')
+filter_data = filter_to_minimal_docs(extracted_data)
+text_chunks = text_split(filter_data)
+
+# Download embeddings
+embeddings = download_hugging_face_embeddings()
+
+# Create Pinecone vector store
+docsearch = PineconeVectorStore.from_documents(
+    documents=text_chunks,
+    index_name=index_name,
+    embedding=embeddings,
+)

static/style.css

@@ -0,0 +1,113 @@
+/* Fonts & Body */
+body {
+    margin: 0;
+    font-family: 'Poppins', sans-serif;
+    background: linear-gradient(135deg, #1f2c34, #1b1b2f);
+}
+
+/* Chat container */
+.chat-container {
+    padding: 20px;
+}
+
+/* Chat box */
+.chat-box {
+    width: 450px;
+    max-width: 95%;
+    height: 650px;
+    background-color: #222831;
+    border-radius: 20px;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+/* Chat header */
+.chat-header {
+    background-color: #30475e;
+    color: white;
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+}
+
+.chat-avatar {
+    width: 55px;
+    height: 55px;
+    border-radius: 50%;
+    border: 2px solid #00adb5;
+}
+
+/* Chat body */
+.chat-body {
+    flex: 1;
+    overflow-y: auto;
+    padding: 15px;
+}
+
+/* Chat footer */
+.chat-footer {
+    background-color: #393e46;
+    border-bottom-left-radius: 20px;
+    border-bottom-right-radius: 20px;
+}
+
+/* Messages */
+.msg-text {
+    position: relative;
+    max-width: 80%;
+    word-wrap: break-word;
+    box-shadow: 0 2px 6px rgba(0,0,0,0.2);
+}
+
+.msg-text .time {
+    font-size: 10px;
+    position: absolute;
+    bottom: -15px;
+    right: 8px;
+    color: rgba(255,255,255,0.5);
+}
+
+/* User & Bot message */
+.user-message .msg-text {
+    background-color: #00adb5;
+    color: white;
+}
+
+.bot-message .msg-text {
+    background-color: #393e46;
+    color: #eeeeee;
+}
+
+/* Input & Button */
+input.form-control {
+    border-radius: 25px;
+    padding: 12px 15px;
+    background-color: #222831;
+    border: 1px solid #00adb5;
+    color: #eee;
+}
+
+input.form-control:focus {
+    box-shadow: none;
+    outline: none;
+}
+
+.btn-accent {
+    border-radius: 25px;
+    background-color: #00adb5;
+    color: white;
+    padding: 0 18px;
+}
+
+.btn-accent:hover {
+    background-color: #00b8c4;
+}
+
+/* Responsive */
+@media (max-width: 500px) {
+    .chat-box {
+        height: 80vh;
+        width: 95%;
+    }
+}
+

templates/chat.html

@@ -0,0 +1,72 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Aiyub's Medicalbot</title>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.2/css/bootstrap.min.css" rel="stylesheet">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600&display=swap" rel="stylesheet">
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+
+<div class="chat-container d-flex justify-content-center align-items-center vh-100">
+    <div class="chat-box shadow-lg">
+        <div class="chat-header d-flex align-items-center p-3">
+            <img src="/static/icon.png" class="chat-avatar me-3">
+            <div>
+                <h5 class="mb-0">Aiyub's Medicalbot</h5>
+                <small class="text-light">Ask me anything about me!</small>
+            </div>
+        </div>
+
+        <div id="messageFormeight" class="chat-body p-3">
+            <!-- Messages will appear here -->
+        </div>
+
+        <div class="chat-footer p-3">
+            <form id="messageArea" class="d-flex">
+                <input type="text" id="text" name="msg" class="form-control me-2" placeholder="Type a message..." required>
+                <button type="submit" class="btn btn-accent"><i class="fas fa-paper-plane"></i></button>
+            </form>
+        </div>
+    </div>
+</div>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.2/js/bootstrap.bundle.min.js"></script>
+
+<script>
+$(document).ready(function() {
+    $("#messageArea").on("submit", function(event) {
+        event.preventDefault();
+
+        const date = new Date();
+        const str_time = date.getHours().toString().padStart(2,'0') + ":" + date.getMinutes().toString().padStart(2,'0');
+        const rawText = $("#text").val();
+        $("#text").val("");
+
+        const userHtml = `
+            <div class="message user-message mb-3 d-flex justify-content-end">
+                <div class="msg-text bg-accent text-white p-3 rounded-4">
+                    ${rawText} <span class="time">${str_time}</span>
+                </div>
+            </div>`;
+        $("#messageFormeight").append(userHtml).scrollTop($("#messageFormeight")[0].scrollHeight);
+
+        $.post("/get", { msg: rawText }, function(data) {
+            const botHtml = `
+                <div class="message bot-message mb-3 d-flex justify-content-start">
+                    <div class="msg-text bg-dark-light text-light p-3 rounded-4">
+                        ${data} <span class="time">${str_time}</span>
+                    </div>
+                </div>`;
+            $("#messageFormeight").append(botHtml).scrollTop($("#messageFormeight")[0].scrollHeight);
+        });
+    });
+});
+</script>
+
+</body>
+</html>

templates/index.html

@@ -0,0 +1,87 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Shanin Portfolio</title>
+
+  <!-- Tailwind CSS CDN -->
+  <script src="https://cdn.tailwindcss.com"></script>
+  <!-- Font Awesome -->
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+  <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600&display=swap" rel="stylesheet">
+</head>
+<body class="bg-gradient-to-br from-gray-900 via-slate-900 to-gray-800 text-gray-200 font-poppins">
+
+ <div class="flex justify-center items-center h-screen p-4">
+  <div class="flex flex-col w-full max-w-xl h-[650px] bg-slate-800/60 border border-slate-700 rounded-2xl shadow-xl overflow-hidden">
+
+    <!-- Header -->
+    <div class="flex justify-between items-center px-5 py-4 bg-slate-900/70 border-b border-slate-700">
+      <div class="flex items-center">
+        <img src="/static/icon.png" alt="Avatar" class="w-12 h-12 rounded-full border-2 border-cyan-400 mr-4">
+        <div>
+          <h5 class="text-white text-lg font-semibold">💬 Aiyub's Medical Assistant</h5>
+          <p class="text-gray-400 text-sm">Ask me anything about my work!</p>
+        </div>
+      </div>
+      <button class="text-cyan-400 hover:text-cyan-300 border-0 p-0">
+        <i class="fas fa-arrow-left"></i>
+      </button>
+    </div>
+
+    <!-- Chat Body -->
+    <div id="messageFormeight" class="flex-1 overflow-y-auto p-4 space-y-4 bg-slate-950/50 scroll-smooth"></div>
+
+    <!-- Footer / Input -->
+    <div class="border-t border-slate-700 bg-slate-900/70 p-4">
+      <form id="messageArea" class="flex items-center space-x-3">
+        <input type="text" id="text" name="msg" placeholder="Type a message..." required
+          class="flex-1 px-4 py-3 rounded-full bg-slate-800 border border-cyan-500/40 focus:outline-none focus:ring-2 focus:ring-cyan-400 text-gray-100 placeholder-gray-400">
+        <button type="submit" class="bg-cyan-500 hover:bg-cyan-400 transition rounded-full p-3 text-white">
+          <i class="fas fa-paper-plane"></i>
+        </button>
+      </form>
+    </div>
+
+  </div>
+</div>
+
+
+  <!-- Scripts -->
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js"></script>
+  <script>
+  $(document).ready(function() {
+    $("#messageArea").on("submit", function(event) {
+      event.preventDefault();
+
+      const date = new Date();
+      const str_time = date.getHours().toString().padStart(2,'0') + ":" + date.getMinutes().toString().padStart(2,'0');
+      const rawText = $("#text").val();
+      $("#text").val("");
+
+      const userHtml = `
+        <div class="message user-message mb-3 flex justify-end">
+          <div class="msg-text bg-cyan-500 text-white p-3 rounded-2xl relative shadow-sm max-w-[80%]">
+            ${rawText}
+            <span class="absolute text-[10px] text-white/50 bottom-[-15px] right-2">${str_time}</span>
+          </div>
+        </div>`;
+      $("#messageFormeight").append(userHtml).scrollTop($("#messageFormeight")[0].scrollHeight);
+
+      $.post("/get", { msg: rawText }, function(data) {
+        const botHtml = `
+          <div class="message bot-message mb-3 flex justify-start">
+            <div class="msg-text bg-slate-800/80 text-gray-200 p-3 rounded-2xl relative shadow-sm border border-slate-700 max-w-[80%]">
+              ${data}
+              <span class="absolute text-[10px] text-white/50 bottom-[-15px] right-2">${str_time}</span>
+            </div>
+          </div>`;
+        $("#messageFormeight").append(botHtml).scrollTop($("#messageFormeight")[0].scrollHeight);
+      });
+    });
+  });
+  </script>
+
+</body>
+</html>