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