app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

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

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"
    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 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 chat(user_input, history):
    if history is None:
        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)

    history.append((user_input, response))
    return history

# Create the Gradio Interface
demo = gr.Interface(fn=chat, inputs=["text", "state"], outputs="state")

# Launch the app
demo.launch()