app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import urllib.parse

# Load model
model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Memory for users
chat_history = {}

# Format history
def format_context(history):
    return "".join([f"You: {u}\n𝕴 𝖆𝖒 𝖍𝖎𝖒: {b}\n" for u, b in history[-3:]])

# FastAPI app
app = FastAPI()

@app.get("/ai")
async def ai_chat(request: Request):
    query_params = dict(request.query_params)
    user_input = query_params.get("query", "")
    user_id = query_params.get("user_id", "default")

    # Get user history
    history = chat_history.get(user_id, [])
    prompt = format_context(history) + f"You: {user_input}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"

    # Tokenize & run model
    inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True)
    outputs = model.generate(**inputs, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
    reply = tokenizer.decode(outputs[0], skip_special_tokens=True).split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].strip()

    # Save memory
    history.append((user_input, reply))
    chat_history[user_id] = history[-10:]

    return JSONResponse({"reply": reply})

# Wrap with Gradio to serve
app = gr.mount_gradio_app(app, gr.Interface(lambda x: x, "textbox", "textbox"))

# Launch it
gradio_app = gr.FastAPI(app)