Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load sentiment analysis model
+sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+
+# Custom label mapping for multi-level output
+label_map = {
+    "LABEL_0": "Very Negative",
+    "LABEL_1": "Negative",
+    "LABEL_2": "Neutral",
+    "LABEL_3": "Positive",
+    "LABEL_4": "Very Positive"
+}
+
+def advanced_sentiment_analysis(text):
+    # Predict sentiment
+    result = sentiment_pipeline(text, top_k=None)[0]
+    
+    # Sum total scores for normalization (if needed)
+    total_score = sum([entry['score'] for entry in result])
+    
+    # Build formatted output
+    formatted_output = ""
+    for entry in result:
+        label = label_map.get(entry['label'], entry['label'])
+        percentage = (entry['score'] / total_score) * 100
+        formatted_output += f"{label}: {percentage:.2f}%\n"
+    
+    return formatted_output.strip()
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("### Welcome, please enter a sample of what you may respond or tell a customer,let's tell you how cool it is")
+    with gr.Row():
+        text_input = gr.Textbox(lines=4, placeholder="Type your message here...", label="Customer Message")
+    output = gr.Textbox(label="Sentiment Analysis Result")
+    analyze_button = gr.Button("Analyze Sentiment")
+    
+    analyze_button.click(fn=advanced_sentiment_analysis, inputs=text_input, outputs=output)
+
+demo.launch()