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 = "juliensimon/distilbert-amazon-shoe-reviews"
model_id_2 = "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

classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
                
def predict(review):
        prediction = classifier(review)
        print(prediction)
        stars = prediction[0]['label']
        stars = (int)(stars.split('_')[1])+1
        score = 100*prediction[0]['score']
        return "{} {:.0f}%".format("\U00002B50"*stars, score)

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.")
        out_2 = gr.Textbox(label="Prediction")

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

    with gr.Row():
      model1_input = gr.Textbox(label="Model 1")
    with gr.Row():
        btn = gr.Button("Prediction for Model 1")
    classifier = pipeline("text-classification", model=model_id_1)   
    btn.click(fn=predict, inputs=inp_1, outputs=out_2)



    with gr.Row():
      model2_input = gr.Textbox(label="Model 2") 
    with gr.Row():
        btn = gr.Button("Prediction for Model 2")
    classifier = pipeline("text-classification", model=model_id_2)    
    btn.click(fn=predict, inputs=inp_1, outputs=out_2)
    
app.launch()