created the app

app.py

@@ -0,0 +1,42 @@
+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()