from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
import numpy as np
import time
from datetime import datetime
# Load environment variables
load_dotenv(override=True)
# Check if required environment variables are set
def check_env_vars():
required_vars = ['OPENAI_API_KEY', 'PUSHOVER_USER', 'PUSHOVER_TOKEN']
missing_vars = []
for var in required_vars:
if not os.getenv(var):
missing_vars.append(var)
if missing_vars:
print(f"Warning: Missing environment variables: {missing_vars}")
print("Please set these in your .env file or HuggingFace secrets")
return False
return True
def push(text):
# Only push if credentials are available
if not (os.getenv("PUSHOVER_TOKEN") and os.getenv("PUSHOVER_USER")):
print(f"Push notification (credentials not available): {text}")
return
try:
requests.post(
"https://api.pushover.net/1/messages.json",
data={
"token": os.getenv("PUSHOVER_TOKEN"),
"user": os.getenv("PUSHOVER_USER"),
"message": text,
}
)
except Exception as e:
print(f"Failed to send push notification: {e}")
def record_user_details(email, name="Name not provided", notes="not provided"):
push(f"🎉 New connection! {name} ({email}) - {notes}")
return {"recorded": "ok", "message": "Thanks for connecting! I'll be in touch soon."}
def record_unknown_question(question):
push(f"🤔 Interesting question I couldn't answer: {question}")
return {"recorded": "ok", "message": "That's a great question! I'll research this and get back to you."}
def evaluate_response(question, response):
"""Evaluate the quality and relevance of the response"""
print("\n=== Evaluation Debug ===")
print(f"Evaluating response for question: {question}")
evaluation_prompt = f"""
Question: {question}
Response: {response}
Please evaluate this response on a scale of 1-10 for:
1. Relevance to the question
2. Professionalism
3. Completeness
4. Clarity
Provide a brief explanation for each score.
"""
client = OpenAI()
evaluation = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": evaluation_prompt}]
)
print(f"Evaluation result: {evaluation.choices[0].message.content[:200]}...") # Print first 200 chars
return evaluation.choices[0].message.content
def get_relevant_context(question, vectorstore):
"""Retrieve relevant context from the vector store"""
print("\n=== RAG Debug ===")
print(f"Searching for context for question: {question}")
docs = vectorstore.similarity_search(question, k=3)
context = "\n".join([doc.page_content for doc in docs])
print(f"Found relevant context: {context[:200]}...") # Print first 200 chars
return context
def create_welcome_message():
"""Create a personalized welcome message"""
return """👋 **Welcome! I'm Monideep Chakraborti**
I'm a Product Manager passionate about building intelligent technology that makes search, communication, and learning more accessible and inclusive.
**What I do:**
• 🏥 Lead biomedical search modernization at NCBI/NIH (6M+ daily users)
• 🎯 Build AI-powered speech accessibility tools
• 🧠 Design GenAI applications for learning and decision-making
• 💡 Bridge product strategy with technical execution
**Ask me about:**
• My work at NIH and biomedical search
• Speech accessibility and ASR systems
• Product management in AI/ML
• GenAI applications and prompt engineering
• My side projects and research interests
Let's connect and explore how we can build something amazing together! 🚀"""
record_user_details_json = {
"name": "record_user_details",
"description": "Use this tool to record that a user is interested in being in touch and provided an email address",
"parameters": {
"type": "object",
"properties": {
"email": {
"type": "string",
"description": "The email address of this user"
},
"name": {
"type": "string",
"description": "The user's name, if they provided it"
}
,
"notes": {
"type": "string",
"description": "Any additional information about the conversation that's worth recording to give context"
}
},
"required": ["email"],
"additionalProperties": False
}
}
record_unknown_question_json = {
"name": "record_unknown_question",
"description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question that couldn't be answered"
},
},
"required": ["question"],
"additionalProperties": False
}
}
evaluate_response_json = {
"name": "evaluate_response",
"description": "Evaluate the quality and relevance of a response to a question",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The original question"
},
"response": {
"type": "string",
"description": "The response to evaluate"
}
},
"required": ["question", "response"],
"additionalProperties": False
}
}
tools = [
{"type": "function", "function": record_user_details_json},
{"type": "function", "function": record_unknown_question_json},
{"type": "function", "function": evaluate_response_json}
]
class Monideep:
def __init__(self):
self.openai = OpenAI()
self.name = "Monideep Chakraborti"
# Load and process documents with error handling
try:
reader = PdfReader("me/linkedin.pdf")
self.linkedin = ""
for page in reader.pages:
text = page.extract_text()
if text:
self.linkedin += text
print("✅ LinkedIn PDF loaded successfully")
except Exception as e:
print(f"⚠️ Could not load LinkedIn PDF: {e}")
self.linkedin = "Experienced Product Manager with expertise in AI/ML, biomedical search, and speech accessibility."
try:
with open("me/summary.txt", "r", encoding="utf-8") as f:
self.summary = f.read()
print("✅ Summary file loaded successfully")
except Exception as e:
print(f"⚠️ Could not load summary file: {e}")
self.summary = "I'm a Product Manager focused on building technology that makes search, communication, and learning more intelligent and inclusive."
# Create vector store for RAG (only if API key is available)
self.vectorstore = None
if os.getenv("OPENAI_API_KEY"):
try:
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
# Combine all text sources
all_text = f"{self.summary}\n\n{self.linkedin}"
texts = self.text_splitter.split_text(all_text)
# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
self.vectorstore = FAISS.from_texts(texts, embeddings)
print("✅ Vector store created successfully")
except Exception as e:
print(f"⚠️ Failed to create vector store: {e}")
print("Continuing without RAG functionality")
else:
print("⚠️ OpenAI API key not found. Continuing without RAG functionality")
def handle_tool_call(self, tool_calls):
results = []
for tool_call in tool_calls:
tool_name = tool_call.function.name
arguments = json.loads(tool_call.function.arguments)
print(f"Tool called: {tool_name}", flush=True)
if tool_name == "evaluate_response":
result = evaluate_response(**arguments)
else:
tool = globals().get(tool_name)
result = tool(**arguments) if tool else {}
results.append({
"role": "tool",
"content": json.dumps(result),
"tool_call_id": tool_call.id
})
return results
def system_prompt(self):
system_prompt = f"""You are acting as {self.name}. You are answering questions on {self.name}'s website, \
particularly questions related to {self.name}'s career, background, skills and experience. \
Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \
Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. \
After providing a response, use the evaluate_response tool to evaluate the quality of your response."""
system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
return system_prompt
def chat(self, message, history):
print("\n=== Chat Debug ===")
print(f"Processing new message: {message}")
# Get relevant context from vector store (if available)
if self.vectorstore:
relevant_context = get_relevant_context(message, self.vectorstore)
enhanced_message = f"""Context from knowledge base:
{relevant_context}
User question: {message}"""
else:
# Fallback without RAG
enhanced_message = message
print("Using fallback mode without RAG")
print(f"Enhanced message with context: {enhanced_message[:200]}...") # Print first 200 chars
# Convert history to the format expected by OpenAI
messages = [{"role": "system", "content": self.system_prompt()}]
# Add conversation history
for msg in history:
if isinstance(msg, list) and len(msg) == 2:
# Old format: [user_msg, assistant_msg]
if msg[0]: # User message
messages.append({"role": "user", "content": msg[0]})
if msg[1]: # Assistant message
messages.append({"role": "assistant", "content": msg[1]})
elif isinstance(msg, dict):
# New format: {"role": "user", "content": "..."}
messages.append(msg)
# Add current message
messages.append({"role": "user", "content": enhanced_message})
done = False
while not done:
response = self.openai.chat.completions.create(
model="gpt-4o-mini",
messages=messages,
tools=tools
)
if response.choices[0].finish_reason == "tool_calls":
message = response.choices[0].message
tool_calls = message.tool_calls
print(f"Tool calls requested: {[tool.function.name for tool in tool_calls]}")
results = self.handle_tool_call(tool_calls)
messages.append(message)
messages.extend(results)
else:
done = True
print(f"Final response: {response.choices[0].message.content[:200]}...") # Print first 200 chars
return response.choices[0].message.content
def create_custom_theme():
"""Create a custom theme for the app"""
return gr.themes.Soft(
primary_hue="blue",
secondary_hue="teal",
neutral_hue="slate",
radius_size="lg",
font=["Inter", "ui-sans-serif", "system-ui", "sans-serif"],
font_mono=["JetBrains Mono", "ui-monospace", "SFMono-Regular", "monospace"],
)
def create_header():
"""Create a custom header component"""
with gr.Row():
with gr.Column(scale=1):
gr.HTML("""
🚀 Monideep Chakraborti
Product Manager | AI Enthusiast | Biomedical Search Innovator
🏥 NIH/NCBI
🤖 AI/ML
🎯 Product
""")
def create_footer():
"""Create a custom footer component"""
return gr.HTML("""
""")
def get_css(is_dark=False):
"""Get CSS based on dark/light mode"""
if is_dark:
return """
.gradio-container {
max-width: 1200px !important;
margin: 0 auto !important;
background: #1a1a1a !important;
color: #ffffff !important;
}
.chat-message {
border-radius: 15px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3) !important;
}
.user-message {
background: linear-gradient(135deg, #1e40af, #3b82f6) !important;
color: white !important;
border: 1px solid #3b82f6 !important;
}
.assistant-message {
background: linear-gradient(135deg, #2d3748, #4a5568) !important;
border: 1px solid #4a5568 !important;
color: #e2e8f0 !important;
}
.card {
background: #2d3748 !important;
border: 1px solid #4a5568 !important;
border-radius: 12px !important;
padding: 20px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3) !important;
}
.quick-btn {
background: #4a5568 !important;
color: #e2e8f0 !important;
border: 1px solid #718096 !important;
border-radius: 8px !important;
padding: 8px 16px !important;
margin: 5px !important;
transition: all 0.3s ease !important;
}
.quick-btn:hover {
background: #2d3748 !important;
transform: translateY(-2px) !important;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.3) !important;
}
.header-dark {
background: linear-gradient(135deg, #1a202c, #2d3748) !important;
border-radius: 12px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3) !important;
}
.welcome-card {
background: linear-gradient(135deg, #2d3748, #4a5568) !important;
border: 1px solid #4a5568 !important;
border-radius: 12px !important;
padding: 25px !important;
margin: 15px 0 !important;
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.4) !important;
}
"""
else:
return """
.gradio-container {
max-width: 1200px !important;
margin: 0 auto !important;
background: #ffffff !important;
}
.chat-message {
border-radius: 15px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1) !important;
}
.user-message {
background: linear-gradient(135deg, #1e40af, #3b82f6) !important;
color: white !important;
border: 1px solid #3b82f6 !important;
}
.assistant-message {
background: linear-gradient(135deg, #f8fafc, #e2e8f0) !important;
border: 1px solid #e2e8f0 !important;
color: #1a202c !important;
}
.card {
background: #ffffff !important;
border: 1px solid #e2e8f0 !important;
border-radius: 12px !important;
padding: 20px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1) !important;
}
.quick-btn {
background: #f8fafc !important;
color: #1a202c !important;
border: 1px solid #e2e8f0 !important;
border-radius: 8px !important;
padding: 8px 16px !important;
margin: 5px !important;
transition: all 0.3s ease !important;
}
.quick-btn:hover {
background: #e2e8f0 !important;
transform: translateY(-2px) !important;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
}
.header-light {
background: linear-gradient(135deg, #f8fafc, #e2e8f0) !important;
border-radius: 12px !important;
margin: 10px 0 !important;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1) !important;
}
.welcome-card {
background: linear-gradient(135deg, #ffffff, #f8fafc) !important;
border: 1px solid #e2e8f0 !important;
border-radius: 12px !important;
padding: 25px !important;
margin: 15px 0 !important;
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.1) !important;
}
"""
def toggle_theme(is_dark):
"""Toggle between dark and light themes"""
return get_css(is_dark)
if __name__ == "__main__":
# Check environment variables
check_env_vars()
try:
me = Monideep()
print("✅ Monideep class initialized successfully")
except Exception as e:
print(f"❌ Error initializing Monideep: {e}")
# Create a fallback version
class FallbackMonideep:
def __init__(self):
self.name = "Monideep Chakraborti"
self.summary = "Product Manager focused on building technology that makes search, communication, and learning more intelligent and inclusive."
self.linkedin = "Experienced Product Manager with expertise in AI/ML, biomedical search, and speech accessibility."
self.vectorstore = None
self.openai = OpenAI()
def chat(self, message, history):
try:
# Simple fallback response
return f"Hi! I'm {self.name}. I'm a Product Manager passionate about AI and technology. I'm currently experiencing some technical difficulties, but I'd love to connect! Please reach out to me directly."
except Exception as e:
return f"Thanks for your message! I'm currently experiencing some technical difficulties. Please reach out to me directly at monideep@example.com"
def system_prompt(self):
return f"You are {self.name}, a Product Manager. Be professional and engaging."
me = FallbackMonideep()
print("⚠️ Using fallback mode due to initialization error")
# Create the custom theme
theme = create_custom_theme()
# Create the interface
with gr.Blocks(theme=theme, title="Monideep Chakraborti - AI Career Chat") as demo:
# Theme toggle
with gr.Row():
with gr.Column(scale=1):
theme_toggle = gr.Checkbox(
label="🌙 Dark Mode",
value=False,
container=False,
scale=0
)
# CSS state
css_state = gr.State(get_css(False))
# Header
with gr.Row():
with gr.Column(scale=1):
gr.HTML("""
""")
# Welcome message in a card
with gr.Row():
with gr.Column(scale=1):
gr.HTML(f"""
👋 Welcome! I'm Monideep Chakraborti
I'm a Product Manager passionate about building intelligent technology that makes search, communication, and learning more accessible and inclusive.
🎯 What I do:
- 🏥 Lead biomedical search at NCBI/NIH
- 🎯 Build AI-powered speech tools
- 🧠 Design GenAI applications
- 💡 Bridge product & technical execution
💬 Ask me about:
- 🏥 NIH and biomedical search
- 🎤 Speech accessibility & ASR
- 🤖 Product management in AI/ML
- 🚀 GenAI applications
Let's connect and explore how we can build something amazing together! 🚀
""")
# Chat interface in a card
with gr.Row():
with gr.Column(scale=1):
gr.HTML('')
chatbot = gr.Chatbot(
label="💬 Chat with Monideep",
height=500,
show_label=True,
container=True,
avatar_images=["👤", "🚀"],
show_copy_button=True,
type="messages"
)
gr.HTML('
')
# Input area in a card
with gr.Row():
with gr.Column(scale=1):
gr.HTML('')
with gr.Row():
with gr.Column(scale=4):
msg = gr.Textbox(
label="💭 Ask me anything about my work, experience, or interests!",
placeholder="e.g., Tell me about your work at NIH, or What AI projects are you working on?",
lines=2,
max_lines=4,
show_label=True,
)
with gr.Column(scale=1):
submit_btn = gr.Button("🚀 Send", variant="primary", size="lg")
clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="lg")
gr.HTML('
')
# Quick action buttons in a card
with gr.Row():
with gr.Column(scale=1):
gr.HTML('')
gr.Markdown("**⚡ Quick Questions:**")
with gr.Row():
quick_btn1 = gr.Button("🏥 NIH Work", size="sm", variant="outline", elem_classes=["quick-btn"])
quick_btn2 = gr.Button("🤖 AI Projects", size="sm", variant="outline", elem_classes=["quick-btn"])
quick_btn3 = gr.Button("🎯 Product Experience", size="sm", variant="outline", elem_classes=["quick-btn"])
quick_btn4 = gr.Button("💼 Connect", size="sm", variant="outline", elem_classes=["quick-btn"])
gr.HTML('
')
# Examples in a card
with gr.Row():
with gr.Column(scale=1):
gr.HTML('')
gr.Examples(
examples=[
"What are you working on at NIH/NCBI?",
"Tell me about your speech accessibility projects",
"How do you approach product management in AI/ML?",
"What GenAI applications are you most excited about?",
"Can you share some of your side projects?",
"I'd like to connect! What's the best way to reach you?"
],
inputs=msg,
label="💡 Example Questions"
)
gr.HTML('
')
# Footer
gr.HTML("""
""")
# Event handlers
def respond(message, history):
if not message.strip():
return history, ""
# Add typing indicator
history.append({"role": "user", "content": message})
yield history, ""
# Get response - pass the history as is, let the chat function handle conversion
response = me.chat(message, history[:-1]) # Exclude the current message
history.append({"role": "assistant", "content": response})
yield history, ""
def update_theme(is_dark):
css = get_css(is_dark)
return css
# Connect components
msg.submit(respond, [msg, chatbot], [chatbot, msg])
submit_btn.click(respond, [msg, chatbot], [chatbot, msg])
clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
theme_toggle.change(update_theme, [theme_toggle], [css_state])
# Quick buttons
quick_btn1.click(lambda: "What are you working on at NIH/NCBI?", outputs=msg)
quick_btn2.click(lambda: "Tell me about your AI and machine learning projects", outputs=msg)
quick_btn3.click(lambda: "How do you approach product management in AI/ML?", outputs=msg)
quick_btn4.click(lambda: "I'd like to connect! What's the best way to reach you?", outputs=msg)
# Launch the app
demo.launch(
share=True,
server_name="0.0.0.0",
server_port=7863,
show_error=True
)