update code

app.py

@@ -10,7 +10,6 @@ import onnxruntime as ort
 import requests
 import os
 from sklearn.preprocessing import StandardScaler
-import joblib
 
 # Download emotion recognition ONNX model
 MODEL_URL = "https://github.com/onnx/models/raw/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-8.onnx"
@@ -26,7 +25,7 @@ if not os.path.exists(MODEL_PATH):
 emotion_session = ort.InferenceSession(MODEL_PATH)
 emotion_labels = ['neutral', 'happy', 'surprise', 'sad', 'angry', 'disgust', 'fear', 'contempt']
 
-# Simple voice emotion classifier (replace with your own trained model if needed)
+# Simple voice emotion classifier
 class VoiceEmotionClassifier:
     def __init__(self):
         self.scaler = StandardScaler()
@@ -54,7 +53,6 @@ class VoiceEmotionClassifier:
             features = self.scaler.transform(features)
             
             # Simple rule-based classifier (replace with actual trained model)
-            # This is just a placeholder - you should train a proper model
             if features[0, 0] > 0.5:
                 return "happy", [{"label": "happy", "score": 0.8}]
             elif features[0, 0] < -0.5:
@@ -191,4 +189,71 @@ def process_input(video, audio):
             voice_emotion, voice_details = "neutral", {}
         
         # Update history and get outputs
-        update_em
+        update_emotion_history(face_emotion, voice_emotion)
+        timeline_df = get_emotion_timeline()
+        advice = get_practitioner_advice(face_emotion, voice_emotion)
+        
+        # Prepare outputs
+        outputs = {
+            "current_face": face_emotion,
+            "current_voice": voice_emotion,
+            "timeline": timeline_df,
+            "advice": advice,
+            "face_details": str(face_details),
+            "voice_details": str(voice_details)
+        }
+        
+        return outputs
+    except Exception as e:
+        print(f"Processing error: {e}")
+        return {
+            "current_face": "Error",
+            "current_voice": "Error",
+            "timeline": pd.DataFrame(),
+            "advice": "System error occurred",
+            "face_details": "",
+            "voice_details": ""
+        }
+
+# Gradio interface
+with gr.Blocks(title="Patient Emotion Recognition", theme="soft") as demo:
+    gr.Markdown("# Real-Time Patient Emotion Recognition")
+    gr.Markdown("Analyze facial expressions and voice tone during medical consultations")
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Image(label="Live Camera Feed", source="webcam", streaming=True)
+            audio_input = gr.Audio(label="Voice Input", source="microphone", type="numpy")
+            submit_btn = gr.Button("Analyze Emotions")
+        
+        with gr.Column():
+            current_face = gr.Textbox(label="Current Facial Emotion")
+            current_voice = gr.Textbox(label="Current Voice Emotion")
+            advice_output = gr.Textbox(label="Practitioner Suggestions", lines=3)
+            timeline_output = gr.Dataframe(label="Emotion Timeline", interactive=False)
+            face_details = gr.Textbox(label="Face Analysis Details", visible=False)
+            voice_details = gr.Textbox(label="Voice Analysis Details", visible=False)
+    
+    # Live processing
+    video_input.change(
+        process_input,
+        inputs=[video_input, audio_input],
+        outputs=[current_face, current_voice, timeline_output, advice_output, face_details, voice_details],
+        show_progress="hidden"
+    )
+    
+    audio_input.change(
+        process_input,
+        inputs=[video_input, audio_input],
+        outputs=[current_face, current_voice, timeline_output, advice_output, face_details, voice_details],
+        show_progress="hidden"
+    )
+    
+    submit_btn.click(
+        process_input,
+        inputs=[video_input, audio_input],
+        outputs=[current_face, current_voice, timeline_output, advice_output, face_details, voice_details]
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)