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

# work around for error, not happy really
# import os
# os.environ['KMP_DUPLICATE_LIB_OK']='True'

app = gr.Blocks()

model_1 = "juliensimon/distilbert-amazon-shoe-reviews"
model_2 = "juliensimon/distilbert-amazon-shoe-reviews"

def load_agent(model_id_1, model_id_2):
    """
    This function load the agent's results
    """
    # Load the metrics
    metadata_1 = get_metadata(model_id_1)

    # get predictions
    predictions_1 = predict(model_id_1)
    
    # Get the accuracy
    # results_1 = parse_metrics_accuracy(metadata_1)
    
    # Load the metrics
    metadata_2 = get_metadata(model_id_2)

    # get predictions
    predictions_2 = predict(model_id_2)
    # Get the accuracy
    # results_2 = parse_metrics_accuracy(metadata_2)

    return model_id_1, predictions_1, model_id_2, predictions_2

# def parse_metrics_accuracy(meta):
#     if "model-index" not in meta:
#         return None
#     result = meta["model-index"][0]["results"]
#     metrics = result[0]["metrics"]
#     accuracy = metrics[0]["value"]
#     return accuracy

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, model_id):
        classifier = pipeline("text-classification", model=model_id)
        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")
    btn.click(fn=predict(model_1), 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")
    btn.click(fn=predict(model_2), inputs=inp_1, outputs=out_2)


    app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
    
    # examples = gr.Examples(examples=[["juliensimon/distilbert-amazon-shoe-reviews","juliensimon/distilbert-amazon-shoe-reviews"]],
    #                        inputs=[model1_input, model2_input])

    
app.launch()