# from fastapi import FastAPI
# from fastapi.staticfiles import StaticFiles
# from fastapi.responses import FileResponse

# from transformers import pipeline

# app = FastAPI()

# pipe_flan = pipeline("text2text-generation", model="FreedomIntelligence/Apollo-1.8B",trust_remote_code=True)

# @app.get("/infer_t5")
# def t5(input):
#     output = pipe_flan(input)
#     return {"output": output[0]["generated_text"]}

# app.mount("/", StaticFiles(directory="static", html=True), name="static")

# @app.get("/")
# def index() -> FileResponse:
#     return FileResponse(path="/app/static/index.html", media_type="text/html")

from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse

from transformers import pipeline

app = FastAPI()

# 更改pipeline类型到text-generation，并使用一个示例模型（如GPT-2）
pipe_gpt = pipeline("text-generation", model="FreedomIntelligence/Apollo-1.8B",trust_remote_code=True)

@app.get("/infer_t5")
def infer_gpt(input: str):
    output = pipe_gpt(input, max_length=50)  # 你可以调整max_length来控制生成文本的长度
    return {"output": output[0]["generated_text"]}

app.mount("/", StaticFiles(directory="static", html=True), name="static")

@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/static/index.html", media_type="text/html")