app.py

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

        if pd.isnull(text):
            cleaned_text = ""
        else:
            cleaned_text = processor.clean_text(text)

        prepared_data = processor.process_tokenizer(cleaned_text)

        if cleaned_text == "":
            prediction = ""  # Set prediction as empty string
        else:
            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.reset_index(drop=True), output_data], axis=1)

    # Save predictions to CSV
    pred_output_df.to_csv(output_file, index=False)
    return "Prediction completed. Results saved to " + output_file

# Define the Gradio interface
input_csv = gr.inputs.File(label="Input CSV", type="file")
output_csv = gr.outputs.File(label="Output CSV")

# Define the prediction function for the Gradio interface
def predict_interface(input_file):
    output_file = "./output.csv"
    predict_batch_from_csv(input_file.name, output_file)
    return output_file

# Connect the interface with the prediction function
iface = gr.Interface(fn=predict_interface, inputs=input_csv, outputs=output_csv, title="CSV Batch Prediction")

# Run the interface
iface.launch()