import gradio as gr
from transformers import pipeline
# from huggingface_hub import InferenceClient
# from transformers import pipeline

# modelName = "chenluuli/test-text-vis"
# pipeline = pipeline(task="image-classification", model="chenluuli/test-text-vis")

# def predict(input_img):
#     predictions = pipeline(input_img)
#     return input_img, {p["label"]: p["score"] for p in predictions} 

# gradio_app = gr.Interface(
#     predict,
#     inputs="text",
#     outputs="text",
#     title="demo",
# )

# if __name__ == "__main__":
#     gradio_app.launch()


token = "" # todo 支持外部传入

def initClient():
    # Initialize client for a specific model
    client = InferenceClient(
        model="prompthero/openjourney-v4",
        #base_url=...,
        #api_key=...,
    )
    return client

def greet(input):
    modelName = "chenluuli/test-text-vis"
    text2text_generator = pipeline("text-generation", model="Qwen/Qwen2.5-0.5B-Instruct", torch_dtype="auto", device_map="auto")
    prompt = "##你是一个可视化专家，通过我提供的信息，推荐合理的图表配置##请根据这些信息，返回合理的图表类型 >>我输入的数据如下:"
    messages = [{
        "role": "user", 
        "content": prompt+input,
    }]
    response = text2text_generator(
        messages,
        max_length=512
    )
    print(response, response[0]['generated_text'])
    return response[0]['generated_text']

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
# title = "demo"
# description = "Gradio Demo for custom demo"
# # examples = [
# #     ["The tower is 324 metres (1,063 ft) tall,"],
# #     ["The Moon's orbit around Earth has"],
# #     ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
# # ]
# gr.Interface.load(
#     "huggingface/chenluuli/test-text-vis",
#     inputs=gr.Textbox(lines=5, label="Input Text"),
#     outputs="text",
#     #title=title,
#     #description=description,
#     # article=article,
#     # examples=examples,
#     #enable_queue=True,
# ).launch()