app.py

import gradio as gr
from fastapi import FastAPI, Query
from fastapi.middleware.wsgi import WSGIMiddleware
import uvicorn
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer once
model_id = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Your AI persona prompt
PERSONA = """
[System: You are 𝕴 𝖆𝖒 𝖍𝖎𝖒 - a fun, smooth, emotionally intelligent AI.
You speak like a real person, not a robot. Keep it under 15 words. 😊😏]
"""

def format_context(history):
    context = PERSONA + "\n"
    if not history:
        return context
    # Use only last 3 exchanges to keep it short
    for user, bot in history[-3:]:
        context += f"You: {user}\n𝕴 𝖆𝖒 𝖍𝖎𝖒: {bot}\n"
    return context

def enhance_response(resp, message):
    if any(x in message.lower() for x in ["?", "think", "why"]):
        resp += " 🤔"
    elif any(x in resp.lower() for x in ["cool", "great", "love", "fun"]):
        resp += " 😏"
    return " ".join(resp.split()[:15])

def generate_ai_reply(user_input, history):
    context = format_context(history) + f"You: {user_input}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
    inputs = tokenizer.encode(context, return_tensors="pt", truncation=True, max_length=1024)

    outputs = model.generate(
        inputs,
        max_new_tokens=50,
        temperature=0.9,
        top_k=40,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    response = full_text.split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].split("\nYou:")[0].strip()
    response = enhance_response(response, user_input)
    return response

app = FastAPI()

# GET /ai?query=some+text  => returns {"reply": "AI reply here"}
@app.get("/ai")
async def ai_endpoint(query: str = Query(..., min_length=1)):
    # For stateless API calls, history is empty (or you can extend to save history)
    reply = generate_ai_reply(query, history=[])
    return {"reply": reply}

# Gradio chat interface for interactive web UI
def chat(user_input, history):
    history = history or []
    reply = generate_ai_reply(user_input, history)
    history.append((user_input, reply))
    return history, history

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Say something...")
    state = gr.State()
    msg.submit(chat, inputs=[msg, state], outputs=[chatbot, state])

# Mount Gradio UI at root
app.mount("/", WSGIMiddleware(demo.launch(prevent_thread_lock=True, share=False)))

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)