Create app.py

app.py

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+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}")