import gradio as gr
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

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=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
    title="Hot Dog? Or Not?",
)

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


# from langchain.chat_models import ChatOpenAI
# from langchain.schema import AIMessage, HumanMessage
# import openai
# import os
# import gradio as gr
#
# os.environ["OPENAI_API_KEY"] = "sk-proj-KyHyIzEDLKe94DhrSq6LT3BlbkFJynj2NRPnnRwdDSV3XaZY"  # Replace with your key
#
# llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
#
# def predict(message, history):
#     history_langchain_format = []
#     for human, ai in history:
#         history_langchain_format.append(HumanMessage(content=human))
#         history_langchain_format.append(AIMessage(content=ai))
#     history_langchain_format.append(HumanMessage(content=message))
#     gpt_response = llm(history_langchain_format)
#     return gpt_response.content
#
# gr.ChatInterface(predict).launch()