app.py

import gradio as gr
from transformers import pipeline

# Load the sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")

# Function to analyze sentiment
def sentiment_analysis(message, history):
    result = sentiment_pipeline(message)[0]
    label = result["label"]
    score = result["score"]
    response = f"Sentiment: {label}, Confidence: {score:.2f}"
    return response

# Function to record feedback
feedback_store = []  # A list to store feedback

def record_feedback(response, feedback):
    feedback_store.append({"response": response, "feedback": feedback})
    return f"Thank you for your feedback! ({len(feedback_store)} recorded)"

# Gradio Interface
with gr.Blocks() as demo:
    chat = gr.ChatInterface(fn=sentiment_analysis, type="messages")
    
    # Additional components for feedback
    with gr.Row():
        feedback_input = gr.Textbox(placeholder="Enter your feedback here", label="Feedback")
        record_button = gr.Button("Submit Feedback")
    
    # Feedback submission functionality
    feedback_status = gr.Textbox(interactive=False, label="Feedback Status")
    record_button.click(
        fn=record_feedback,
        inputs=[chat.output_component, feedback_input],
        outputs=feedback_status,
    )

demo.launch()