Update app.py

app.py

@@ -2,78 +2,56 @@
 
 # gr.load("models/dima806/facial_emotions_image_detection").launch()
 
-
 import gradio as gr
-from huggingface_hub import from_pretrained_keras
-import numpy as np
-from datetime import datetime
 import tensorflow as tf
+from transformers import pipeline
 
 def process_image(image):
     try:
-        model = from_pretrained_keras("dima806/facial_emotions_image_detection")
-        
-        # Preprocess image
-        img = tf.image.resize(image, (48, 48))
-        img = tf.image.rgb_to_grayscale(img)
-        img = img / 255.0
-        img = tf.expand_dims(img, 0)
+        # Initialize emotion classifier
+        classifier = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
         
         # Get predictions
-        pred = model.predict(img)
+        result = classifier(image)
         
-        emotions = {
-            'happy': float(pred[0][0]), 
-            'angry': float(pred[0][1]),
-            'surprise': float(pred[0][2]),
-            'fear': float(pred[0][3]),
-            'neutral': float(pred[0][4])
-        }
+        # Convert results to required format
+        emotions = {item['label']: float(item['score']) * 100 for item in result}
         
-        # Generate analysis
+        # Generate analysis and report
         analysis = generate_analysis(emotions)
-        report = generate_report(image, analysis)
+        report = generate_report(emotions, analysis)
         
-        return emotions, analysis['detailed_analysis'], report
+        return [(k, v) for k, v in emotions.items()], analysis['detailed_analysis'], report
     except Exception as e:
-        return {"error": str(e)}, "Error in processing", "Error generating report"
+        return [("error", 0)], f"Error: {str(e)}", "Error generating report"
 
-def generate_analysis(emotion_scores):
-    emotions = {
-        'happy': 'indicates joy, satisfaction, or positive mood',
-        'angry': 'suggests frustration, irritation, or displeasure',
-        'surprise': 'shows astonishment or unexpected reaction',
-        'fear': 'reflects anxiety, worry, or concern',
-        'neutral': 'displays a balanced or unemotional state'
+def generate_analysis(emotions):
+    descriptions = {
+        'happy': 'indicates joy and positive mood',
+        'angry': 'suggests frustration or displeasure',
+        'surprise': 'shows astonishment',
+        'fear': 'reflects anxiety or concern',
+        'neutral': 'displays a balanced state'
     }
     
-    top_emotion = max(emotion_scores.items(), key=lambda x: x[1])
+    top_emotion = max(emotions.items(), key=lambda x: x[1])
     
     return {
-        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         "primary_emotion": top_emotion[0],
-        "confidence_scores": emotion_scores,
-        "detailed_analysis": f"Primary emotion: {top_emotion[0]} ({top_emotion[1]:.1f}% confidence)",
-        "secondary_indicators": [f"{k} ({v:.1f}%)" for k, v in emotion_scores.items() if v > 20 and k != top_emotion[0]]
+        "detailed_analysis": f"Primary emotion detected is {top_emotion[0]} ({top_emotion[1]:.1f}%), which {descriptions.get(top_emotion[0], '')}."
     }
 
-def generate_report(image, analysis):
-    report = f"""Emotion Analysis Report
-Generated: {analysis['timestamp']}
+def generate_report(emotions, analysis):
+    return f"""Emotion Analysis Report
 
 Primary Emotion: {analysis['primary_emotion'].upper()}
-Confidence: {analysis['confidence_scores'][analysis['primary_emotion']]:.1f}%
+Confidence: {emotions[analysis['primary_emotion']]:.1f}%
 
-Secondary Emotions:
-{', '.join(analysis['secondary_indicators']) if analysis['secondary_indicators'] else 'None significant'}
-
-All Scores:
+All Detected Emotions:
 """ + '\n'.join(f"- {k}: {v:.1f}%" for k, v in sorted(
-        analysis['confidence_scores'].items(), 
+        emotions.items(), 
         key=lambda x: x[1], 
         reverse=True))
-    
-    return report
 
 # Create Gradio interface
 with gr.Blocks(theme=gr.themes.Soft()) as app:
@@ -81,12 +59,12 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     
     with gr.Row():
         with gr.Column(scale=1):
-            input_image = gr.Image(label="Upload Image")
+            input_image = gr.Image(type="numpy")
             submit_btn = gr.Button("Analyze", variant="primary")
             
         with gr.Column(scale=1):
-            emotion_scores = gr.Label(label="Emotion Confidence Scores")
-            analysis_text = gr.Textbox(label="Detailed Analysis", lines=3)
+            emotion_scores = gr.Label(label="Emotion Scores")
+            analysis_text = gr.Textbox(label="Analysis", lines=3)
             report_text = gr.Textbox(label="Full Report", lines=10)
             download_btn = gr.Button("Download Report")