Update app.py

app.py

@@ -3,36 +3,28 @@ import faiss
 import numpy as np
 from sentence_transformers import SentenceTransformer
 
-# Load resume data in structured format
+# Load resume data
 resume_data = {
-    "Name": "Pradeep Singh Sengar",
-    "LinkedIn": "[LinkedIn Profile](https://www.linkedin.com/in/pradeepsengarr)",
-    "Email": "[email protected]",
-    "GitHub": "[GitHub Profile](https://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, React.js, 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",
-    "Certifications": "Salesforce AI Associate, Web Development (Internshala), Data Science (LinkedIn Learning)",
+    "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)"
 }
 
-# Suggested common questions
-suggested_questions = [
-    "What are your key skills?",
-    "Tell me about your work experience?",
-    "What projects have you worked on?",
-    "What certifications do you have?",
-    "How can I contact you?",
-]
-
-# Convert resume data to list for embedding
+# Convert data to list of sentences for retrieval
 resume_keys = list(resume_data.keys())
 resume_values = list(resume_data.values())
 
 # Load embedding model
 model = SentenceTransformer('all-MiniLM-L6-v2')
-embeddings = model.encode(resume_keys)
+embeddings = model.encode(resume_values)
 
 # Store embeddings in FAISS index
 index = faiss.IndexFlatL2(embeddings.shape[1])
@@ -41,28 +33,43 @@ index.add(np.array(embeddings))
 def get_response(query):
     query_embedding = model.encode([query])
     D, I = index.search(query_embedding, 1)
-    score = 1 - (D[0][0] / np.max(D))  # Convert distance to similarity
-    key = resume_keys[I[0][0]]
-    response = resume_data[key]
-    return key, response, round(score * 100, 2)
+    confidence = 1 - D[0][0] / 10  # Normalize confidence score
+    
+    if confidence > 0.5:
+        return f"**{resume_keys[I[0][0]].capitalize()}**: {resume_values[I[0][0]]} \n\n *(Confidence: {confidence:.2f})*"
+    else:
+        return "I'm not sure about this. Please try asking differently or be more specific."
 
 # Streamlit UI
-st.set_page_config(page_title="Resume Chatbot", page_icon="🤖", layout="centered")
-st.title("🤖 Chat with My Resume")
-st.write("Ask me anything about my resume or pick a question below:")
+st.title("📝 Resume Chatbot")
+st.write("Ask anything about Pradeep's resume!")
 
-# Suggested questions UI
-cols = st.columns(len(suggested_questions))
+# Suggested Questions
+suggested_questions = [
+    "What is your name?",
+    "What is your LinkedIn profile?",
+    "What skills do you have?",
+    "Tell me about your experience?",
+    "What are your certifications?",
+    "List your projects."
+]
+
+st.write("### Quick Questions")
+col1, col2 = st.columns(2)
 for i, question in enumerate(suggested_questions):
-    if cols[i].button(question):
-        user_input = question
-    
-# User input box
-user_input = st.text_input("Your question:", key="user_query")
+    if i % 2 == 0:
+        with col1:
+            if st.button(question, key=f"btn_{i}"):
+                st.session_state["user_input"] = question
+                st.rerun()
+    else:
+        with col2:
+            if st.button(question, key=f"btn_{i}"):
+                st.session_state["user_input"] = question
+                st.rerun()
 
+# User Input & Response
+user_input = st.text_input("Your question:", value=st.session_state.get("user_input", ""))
 if user_input:
-    key, response, confidence = get_response(user_input)
-    if confidence > 50:
-        st.success(f"**{key}:** {response} (Confidence: {confidence}%)")
-    else:
-        st.warning("I'm not sure about this. Can you ask in a different way?")
+    response = get_response(user_input)
+    st.success(response)