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from fastapi import FastAPI, HTTPException |
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from pydantic import BaseModel |
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from huggingface_hub import InferenceClient |
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import os |
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from datasets import load_dataset |
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from huggingface_hub import login |
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app = FastAPI() |
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hf_token = os.environ.get("HF_TOKEN") |
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hf_token) |
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if dataset: |
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texts = [] |
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for file in dataset["concise"]: |
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cleaned_text = file['text'].replace('\n', ' ') |
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texts.append(cleaned_text) |
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class ChatRequest(BaseModel): |
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message: str |
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history: list[tuple[str, str]] = [] |
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system_message: str = "You are a friendly Chatbot." |
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max_tokens: int = 512 |
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temperature: float = 0.7 |
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top_p: float = 0.95 |
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class ChatResponse(BaseModel): |
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response: str |
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@app.post("/chat", response_model=ChatResponse) |
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async def chat(request: ChatRequest): |
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try: |
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messages = [{"role": "system", "content": request.system_message}] |
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for val in request.history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": request.message}) |
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response = "" |
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for message in client.chat_completion( |
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messages, |
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max_tokens=request.max_tokens, |
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stream=True, |
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temperature=request.temperature, |
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top_p=request.top_p, |
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): |
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token = message.choices[0].delta.content |
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response += token |
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return {"assistant_response": response, "dataset_sample": "sample"} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |