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from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
app = FastAPI()
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
# In-memory history per user
chat_history = {}
@app.get("/")
async def root():
return {"message": "🟢 API is running. Use /ai?query=Hello&user_id=yourname"}
@app.get("/ai")
async def chat(request: Request):
query_params = dict(request.query_params)
user_input = query_params.get("query", "")
user_id = query_params.get("user_id", "default")
if not user_input:
return JSONResponse({"error": "Missing 'query' parameter"}, status_code=400)
new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
user_history = chat_history.get(user_id, [])
bot_input_ids = torch.cat(user_history + [new_input_ids], dim=-1) if user_history else new_input_ids
output_ids = model.generate(bot_input_ids, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(output_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
chat_history[user_id] = [bot_input_ids, output_ids]
return JSONResponse({"reply": response})
# Only needed if running locally, not in Hugging Face Space
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |