app.py

import streamlit as st
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

# Load resume data
resume_data = {
    "name": "Pradeep Singh Sengar",
    "linkedin": "www.linkedin.com/in/ipradeepsengarr",
    "email": "[email protected]",
    "github": "github.com/pradeepsengar",
    "mobile": "+91-7898367211",
    "education": "Bachelor of Engineering (Hons.) - Information Technology; CGPA: 8.31 (Oriental College Of Technology, Bhopal, 2019-2023)",
    "skills": "Python, HTML/CSS, Django, Reactjs, Node.js, Git, Web Scraping, Generative AI, Machine Learning (LLM)",
    "experience": "Graduate Engineer Trainee at Jio Platform Limited (Dec. 2023 - Present). Implemented chatbots with Docker, used Git/GitHub, worked with LLM concepts and Hugging Face.",
    "projects": "Room Rental System, Text to Image Generator, Fitness Tracker, Movie Recommendation System",
    "honors_awards": "Qualified for Round 1B of SnackDown (CodeChef), Startup Challenge (Top 10 teams)",
    "certifications": "Web Development (Internshala), The Complete Python Pro Bootcamp (Udemy), Data Science (LinkedIn Learning), Web Scraping (LinkedIn Learning)"
}

# Convert data to list of sentences for retrieval
resume_values = list(resume_data.values())

# Load embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(resume_values)

# Store embeddings in FAISS index
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(np.array(embeddings))

def get_response(query):
    query_embedding = model.encode([query])
    D, I = index.search(query_embedding, 1)
    return resume_values[I[0][0]]

# Streamlit UI
st.title("📝 Resume Chatbot")
st.write("Ask anything about Pradeep's resume!")

user_input = st.text_input("Your question:")
if user_input:
    response = get_response(user_input)
    st.success(f"**Answer:** {response}")