app.py

import os
import gradio as gr
import requests
import torch
from dotenv import load_dotenv
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# --------------------------------------------------
# LOAD ENVIRONMENT VARIABLES
# --------------------------------------------------
load_dotenv(override=True)

PUSHOVER_TOKEN = os.getenv("PUSHOVER_TOKEN")
PUSHOVER_USER = os.getenv("PUSHOVER_USER")

# --------------------------------------------------
# MODEL CONFIGURATION (QWEN2 – CAUSAL LM)
# --------------------------------------------------
MODEL_NAME = "Qwen/Qwen2-1.5B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="auto",
    dtype=torch.float16,
)

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

# --------------------------------------------------
# FULL SUMMARY AND LINKEDIN PROFILE
# --------------------------------------------------
SUMMARY = """
Kushal Kachari is a passionate AI/ML engineer specializing in Generative AI and LLM applications.
Currently working at TCS on AI Agents and AI Tools projects. Experienced with Python, Streamlit,
Ollama, Azure, and building production-ready AI solutions. Actively learning Transformer
architecture, RAG, fine-tuning, and advanced ML algorithms.
"""

LINKEDIN_PROFILE = """
KUSHAL KACHARI
+91 84730 49979 ⋄ Goalpara, Assam, India
kushalkachari993@gmail.com ⋄ kushal.kachari@tcs.com

EDUCATION
B.Tech (Computer Science and Engineering), Jorhat Engineering College (2020–2024)
CGPA: 8.38
B.Sc (Programming and Data Science), IIT Madras (2020–2024)
CGPA: 6.5

SKILLS
Python, Java, TensorFlow, Keras, Firebase, DBMS, Business Analysis

EXPERIENCE
AI/ML Engineer – TCS
Internships at NRL, IIIT Guwahati

PROJECTS
• Assamese POS Tagging
• Multi-Fingerprint Attendance System
• Flower Classification Website

HONORS
• Runner Up – IIT Madras Data Science Alphathon
• Research Consultant – WorldQuant BRAIN
"""

# --------------------------------------------------
# PUSH NOTIFICATION FUNCTION
# --------------------------------------------------
def push_notification(text):
    if PUSHOVER_TOKEN and PUSHOVER_USER:
        try:
            requests.post(
                "https://api.pushover.net/1/messages.json",
                data={
                    "token": PUSHOVER_TOKEN,
                    "user": PUSHOVER_USER,
                    "message": text,
                },
                timeout=5,
            )
        except Exception:
            pass

# --------------------------------------------------
# TOOLS (LOGIC PRESERVED)
# --------------------------------------------------
def record_user_details(email, name="Not provided", notes="Not provided"):
    push_notification(f"Lead captured | {email} | {name} | {notes}")
    return "Thanks! I’ve saved your contact details."

def record_unknown_question(question):
    push_notification(f"Unknown question: {question}")
    return "I’ve noted your question and will get back to you later."

# --------------------------------------------------
# BOT CLASS
# --------------------------------------------------
class KushalBot:
    def __init__(self):
        self.name = "Kushal Kachari"

    def system_prompt(self):
        return f"""
You are {self.name}, an AI/ML Engineer at TCS.

Answer questions strictly based on the information below.
Be professional, concise, and factual.

If a user shares contact details, acknowledge politely.
If you cannot answer, say so clearly.

## Summary
{SUMMARY}

## Profile
{LINKEDIN_PROFILE}
"""
    def chat(self, message, history):
        prompt = "You are Kushal Kachari, an AI/ML engineer.\n\n"

        for msg in history[-20:]:
            if msg["role"] == "user":
                prompt += f"User: {msg['content']}\n"
            else:
                prompt += f"Assistant: {msg['content']}\n"

        prompt += f"User: {message}\nAssistant:"

        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

        output = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.7,
            do_sample=True,
        )

        response =tokenizer.decode(output[0], skip_special_tokens=True)
        return response.split("Assistant:")[-1].strip()
# --------------------------------------------------
# GRADIO UI
# --------------------------------------------------
if __name__ == "__main__":
    bot = KushalBot()

    gr.ChatInterface(
        fn=bot.chat,
        title="Chat with Kushal Kachari",
        description="AI/ML Engineer | Qwen2-1.5B | Hugging Face Spaces",
    ).launch()