# import sklearn
from os import O_ACCMODE
import gradio as gr
import joblib
from transformers import pipeline
import requests.exceptions
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load


app = gr.Blocks()

model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
model_id_2 = "microsoft/deberta-base"
model_id_3 = "juliensimon/distilbert-amazon-shoe-reviews"

def load_agent(model_id):
    """
    This function load the agent's results
    """
    # Load the metrics
    metadata = get_metadata(model_id)
    # get predictions
    predictions = predict(model_id)

    return model_id, predictions


def get_metadata(model_id):
    """
    Get the metadata of the model repo
    :param model_id:
    :return: metadata
    """
    try:
        readme_path = hf_hub_download(model_id, filename="README.md")
        metadata = metadata_load(readme_path)
        print(metadata)
        return metadata
    except requests.exceptions.HTTPError:
        return None

def get_prediction(model_id):

    classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
             
    def predict(review):
            prediction = classifier(review)
            print(prediction)
            return prediction
    return predict


with app:
    gr.Markdown(
    """
    # Compare Sentiment Analysis Models 
    
    Type text to predict sentiment.
    """)   
    with gr.Row():
        inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")

    gr.Markdown(
    """
    **Model Predictions**
    """)

    gr.Markdown(
    """
    Model 1 = nlptown/bert-base-multilingual-uncased-sentiment
    """)

    with gr.Row():
        btn1 = gr.Button("Predict for Model 1")
    with gr.Row():
        out_1 = gr.Textbox(label="Prediction for Model 1")
    btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)

    gr.Markdown(
    """
    Model 2 = microsoft/deberta-base
    """)

    with gr.Row():
        btn2 = gr.Button("Predict for Model 2")
    with gr.Row():
        out_2 = gr.Textbox(label="Prediction for Model 2")      
    btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)

    gr.Markdown(
    """
    Model 3 = juliensimon/distilbert-amazon-shoe-reviews
    """)

    with gr.Row():
        btn3 = gr.Button("Predict for Model 3")
    with gr.Row():
        out_3 = gr.Textbox(label="Prediction for Model 3")      
    btn3.click(fn=get_prediction(model_id_3), inputs=inp_1, outputs=out_3)
   
app.launch()