import gradio as gr
import pandas as pd
from tqdm import tqdm
from facility_predict import Preprocess, Facility_Model, obj_Facility_Model, processor

def predict_batch_from_csv(input_file, output_file):
    # Load batch data from CSV
    batch_data = pd.read_csv(input_file)

    # Initialize predictions list
    predictions = []

    # Iterate over rows with tqdm for progress tracking
    for _, row in tqdm(batch_data.iterrows(), total=len(batch_data)):
        text = row['facility_name']  # Replace 'facility_name' with the actual column name containing the text data
        cleaned_text = processor.clean_text(text)
        prepared_data = processor.process_tokenizer(cleaned_text)
        prediction = obj_Facility_Model.inference(prepared_data)
        predictions.append(prediction)

    # Create DataFrame for predictions
    output_data = pd.DataFrame({'prediction': predictions})
    # Merge with input DataFrame
    pred_output_df = pd.concat([batch_data, output_data], axis=1)
    # Save predictions to CSV
    pred_output_df.to_csv(output_file, index=False)

def predict_batch(input_csv, output_csv):
    predict_batch_from_csv(input_csv, output_csv)
    return "Prediction completed. Results saved to " + output_csv

iface = gr.Interface(
    fn=predict_batch,
    inputs=["file", "text"],
    outputs="text",
    title="Batch Facility Name Prediction",
    description="Upload a CSV file with facility names and get the predictions in a CSV file",
   #examples=[["input.csv", "output.csv"]],
)

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