Upload app.py

app.py

@@ -3,6 +3,7 @@ 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)
@@ -12,31 +13,44 @@ def predict_batch_from_csv(input_file, output_file):
 
     # 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)
+        text = row['pnc_fac_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)
-        prediction = obj_Facility_Model.inference(prepared_data)
+
+        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, output_data], axis=1)
+    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", accept=".csv")
+output_csv = gr.outputs.File(label="Output CSV", type="file", default="./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")
 
-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()
+# Run the interface
+iface.launch()