feedback1

app.py

@@ -1,5 +1,39 @@
 import gradio as gr
 import os
+import csv
+import time
+
 token = os.getenv('HF_TOKEN')
 
-gr.load("models/ressolve-ai/sentiment_cobranzas_BAL4", hf_token = token).launch()
+def save_feedback(text, original_output, corrected_label):  
+    with open('/data/feedback_data.csv', 'a', newline='') as f:
+        predicted_label = max(original_output, key=original_output.get)
+        writer = csv.writer(f)
+        writer.writerow([text, original_output, predicted_label, corrected_label])
+    return "Feedback saved successfully!"
+
+def clear_message():
+    time.sleep(1)  # Wait for 1 seconds
+    return gr.update(value="")
+
+with gr.Blocks() as demo:
+    # Load the classification app
+    classification_app = gr.load("models/ressolve-ai/sentiment_cobranzas_BAL4", hf_token=token)
+
+    # Get the input and output components from the loaded app
+    text_input = classification_app.input_components[0]
+    output_label = classification_app.output_components[0]
+
+    # Add a subtitle before the feedback section
+    gr.Markdown("## Feedback the sentiment")
+
+    # Add feedback buttons below the classification app
+    with gr.Row():
+        feedback_label = gr.Radio(["Positivo", "Negativo", "Neutro"], label="Correct Label") 
+        feedback_btn = gr.Button("If applicable, press this button to correct the label")
+        feedback_message = gr.Textbox(label="Feedback Status", interactive=False)
+
+    feedback_btn.click(save_feedback, inputs=[text_input, output_label, feedback_label], outputs=feedback_message)
+    feedback_btn.click(clear_message, inputs=None, outputs=feedback_message)
+
+demo.launch()