Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 from transformers import pipeline
+import pandas as pd
+import plotly.express as px
 
 # ------------------------------
 # Load pretrained models (CPU)
@@ -13,7 +15,7 @@ text_classifier = pipeline(
 
 audio_classifier = pipeline(
     "audio-classification",
-    model="m3hrdadfi/wav2vec2-mini-xlsr-emotion",
+    model="mrm8488/wav2vec2-small-xlsr-53-english-emotion",
     device=-1  # CPU
 )
 
@@ -61,6 +63,21 @@ def fuse_predictions(text_preds=None, audio_preds=None, w_text=0.5, w_audio=0.5)
     best = max(scores.items(), key=lambda x: x[1]) if scores else ("none", 0)
     return {"fused_label": best[0], "fused_score": round(best[1], 3), "all_scores": scores}
 
+# ------------------------------
+# Create bar chart
+# ------------------------------
+def make_bar_chart(scores_dict, title="Emotion Scores"):
+    df = pd.DataFrame({
+        "Emotion": list(scores_dict.keys()),
+        "Score": list(scores_dict.values())
+    })
+    fig = px.bar(df, x="Emotion", y="Score", text="Score",
+                 title=title, range_y=[0,1],
+                 color="Emotion", color_discrete_sequence=px.colors.qualitative.Bold)
+    fig.update_traces(texttemplate='%{text:.2f}', textposition='outside')
+    fig.update_layout(yaxis_title="Probability", xaxis_title="Emotion", showlegend=False)
+    return fig
+
 # ------------------------------
 # Prediction function
 # ------------------------------
@@ -77,15 +94,15 @@ def predict(text, audio, w_text, w_audio):
     emoji = EMOJI_MAP.get(label, "")
     final_emotion = f"### Final Predicted Emotion: {label.upper()} {emoji} (score: {fused['fused_score']})"
 
-    # Simple text-based bar chart
-    chart_text = "\n"
+    # Bar charts
+    charts = []
     if text_preds:
-        chart_text += "Text scores:\n" + "\n".join([f"{p['label']}: {p['score']:.2f}" for p in text_preds]) + "\n"
+        charts.append(make_bar_chart({p['label']: p['score'] for p in text_preds}, "Text Emotion Scores"))
     if audio_preds:
-        chart_text += "Audio scores:\n" + "\n".join([f"{p['label']}: {p['score']:.2f}" for p in audio_preds]) + "\n"
-    chart_text += "Fused scores:\n" + "\n".join([f"{k}: {v:.2f}" for k,v in fused['all_scores'].items()])
+        charts.append(make_bar_chart({p['label']: p['score'] for p in audio_preds}, "Audio Emotion Scores"))
+    charts.append(make_bar_chart(fused['all_scores'], "Fused Emotion Scores"))
 
-    return final_emotion, chart_text
+    return final_emotion, charts
 
 # ------------------------------
 # Build Gradio interface
@@ -102,7 +119,7 @@ with gr.Blocks() as demo:
             btn = gr.Button("Predict")
         with gr.Column():
             final_label = gr.Markdown(label="Predicted Emotion")
-            chart_output = gr.Textbox(label="Emotion Scores", interactive=False)
+            chart_output = gr.Plot(label="Emotion Scores")
 
     btn.click(fn=predict, inputs=[txt, aud, w1, w2], outputs=[final_label, chart_output])