Update app.py

app.py

@@ -1,39 +1,49 @@
 import gradio as gr
+import plotly.express as px
+import pandas as pd
 from transformers import pipeline
 
-# Load your model
+# Load model
 classifier = pipeline(task="text-classification", model="AR04/Senti", top_k=None)
 
-# Function to classify emotions
-def classify_emotion(user_input, history):
-    if not user_input.strip():
-        return history
-
-    # Run classifier
-    outputs = classifier([user_input])[0]
-
-    # Format as "emotion (score)"
-    emotions = [f"{o['label']} ({o['score']:.2f})" for o in outputs[:5]]  # top 5
-    result_text = ", ".join(emotions)
+id2label = {
+    0: "admiration", 1: "amusement", 2: "anger", 3: "annoyance", 4: "approval",
+    5: "caring", 6: "confusion", 7: "curiosity", 8: "desire", 9: "disappointment",
+    10: "disapproval", 11: "disgust", 12: "embarrassment", 13: "excitement",
+    14: "fear", 15: "gratitude", 16: "grief", 17: "joy", 18: "love", 19: "nervousness",
+    20: "optimism", 21: "pride", 22: "realization", 23: "relief", 24: "remorse",
+    25: "sadness", 26: "surprise", 27: "neutral"
+}
+
+def classify_and_visualize(text):
+    outputs = classifier([text])[0]  # list of dicts
+    # Convert to DataFrame
+    df = pd.DataFrame(outputs)
+    df = df.sort_values("score", ascending=False)
+
+    # Bar chart with plotly
+    fig = px.bar(
+        df,
+        x="label",
+        y="score",
+        title="Emotion Scores",
+        labels={"label": "Emotion", "score": "Probability"},
+    )
+    fig.update_layout(xaxis_tickangle=-45)
 
-    # Append to chat history
-    history.append(("🗨️ " + user_input, "🎭 " + result_text))
-    return history
+    return df.to_dict("records"), fig
 
-# Gradio Chat UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎭 SentiChat — Emotion Classifier Demo\nType a message and see what emotions the model detects!")
+    gr.Markdown("## 🎭 Emotion Radar Chat App")
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(placeholder="Type a message...", lines=2)
+            submit_btn = gr.Button("Analyze")
+        with gr.Column():
+            result_json = gr.JSON()
+            result_plot = gr.Plot()
 
-    chatbot = gr.Chatbot(height=400)
-    msg = gr.Textbox(placeholder="Type your message here...")
-
-    clear = gr.Button("Clear Chat")
-
-    msg.submit(classify_emotion, [msg, chatbot], chatbot).then(
-        lambda: "", None, msg
-    )
-    clear.click(lambda: [], None, chatbot)
+    submit_btn.click(fn=classify_and_visualize, inputs=text_input, outputs=[result_json, result_plot])
 
-# Launch app
 if __name__ == "__main__":
     demo.launch()