Create app.py

app.py

@@ -0,0 +1,125 @@
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import plotly.express as px
+
+# ------------------------------
+# Load pretrained models
+# ------------------------------
+text_classifier = pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    return_all_scores=True
+)
+
+audio_classifier = pipeline(
+    "audio-classification",
+    model="superb/wav2vec2-base-superb-er"
+)
+
+# ------------------------------
+# Map emotion to emoji
+# ------------------------------
+EMOJI_MAP = {
+    "anger": "😡",
+    "disgust": "🤢",
+    "fear": "😨",
+    "joy": "😄",
+    "neutral": "😐",
+    "sadness": "😢",
+    "surprise": "😲",
+    "hap": "😄",  # for audio model
+    "neu": "😐",
+    "sad": "😢",
+    "ang": "😡"
+}
+
+# ------------------------------
+# Fusion function
+# ------------------------------
+def fuse_predictions(text_preds=None, audio_preds=None, w_text=0.5, w_audio=0.5):
+    labels = set()
+    if text_preds:
+        labels |= {p['label'] for p in text_preds}
+    if audio_preds:
+        labels |= {p['label'] for p in audio_preds}
+    scores = {l: 0.0 for l in labels}
+
+    def normalize(preds):
+        s = sum(p['score'] for p in preds)
+        return {p['label']: p['score']/s for p in preds}
+
+    if text_preds:
+        t_norm = normalize(text_preds)
+        for l in labels:
+            scores[l] += w_text * t_norm.get(l, 0)
+    if audio_preds:
+        a_norm = normalize(audio_preds)
+        for l in labels:
+            scores[l] += w_audio * a_norm.get(l, 0)
+
+    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
+# ------------------------------
+def predict(text, audio, w_text, w_audio):
+    text_preds, audio_preds = None, None
+    if text:
+        text_preds = text_classifier(text)[0]
+    if audio:
+        audio_preds = audio_classifier(audio)
+    fused = fuse_predictions(text_preds, audio_preds, w_text, w_audio)
+
+    # Display final predicted emotion with emoji
+    label = fused['fused_label']
+    emoji = EMOJI_MAP.get(label, "")
+    final_emotion = f"### Final Predicted Emotion: {label.upper()} {emoji} (score: {fused['fused_score']})"
+
+    # Bar charts
+    charts = []
+    if text_preds:
+        charts.append(make_bar_chart({p['label']: p['score'] for p in text_preds}, "Text Emotion Scores"))
+    if audio_preds:
+        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, charts
+
+# ------------------------------
+# Build Gradio interface
+# ------------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎭 Multimodal Emotion Classification (Text + Speech)")
+
+    with gr.Row():
+        with gr.Column():
+            txt = gr.Textbox(label="Text input", placeholder="Type something emotional...")
+            aud = gr.Audio(type="filepath", label="Upload speech (wav/mp3)")
+            w1 = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Text weight (w_text)")
+            w2 = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Audio weight (w_audio)")
+            btn = gr.Button("Predict")
+        with gr.Column():
+            final_label = gr.Markdown(label="Predicted Emotion")
+            chart_output = gr.Plot(label="Emotion Scores")
+
+    # Button click triggers prediction
+    btn.click(fn=predict, inputs=[txt, aud, w1, w2], outputs=[final_label, chart_output])
+
+demo.launch()