Update app.py

app.py

@@ -1,83 +1,83 @@
-# import gradio as gr
+import gradio as gr
 
-# gr.load("models/dima806/facial_emotions_image_detection").launch()
+gr.load("models/dima806/facial_emotions_image_detection").launch()
 
-import gradio as gr
-import tensorflow as tf
-from transformers import pipeline
+# import gradio as gr
+# import tensorflow as tf
+# from transformers import pipeline
 
-def process_image(image):
-    try:
-        # Initialize emotion classifier
-        classifier = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
+# def process_image(image):
+#     try:
+#         # Initialize emotion classifier
+#         classifier = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
         
-        # Get predictions
-        result = classifier(image)
+#         # Get predictions
+#         result = classifier(image)
         
-        # Convert results to required format
-        emotions = {item['label']: float(item['score']) * 100 for item in result}
+#         # Convert results to required format
+#         emotions = {item['label']: float(item['score']) * 100 for item in result}
         
-        # Generate analysis and report
-        analysis = generate_analysis(emotions)
-        report = generate_report(emotions, analysis)
+#         # Generate analysis and report
+#         analysis = generate_analysis(emotions)
+#         report = generate_report(emotions, analysis)
         
-        return [(k, v) for k, v in emotions.items()], analysis['detailed_analysis'], report
-    except Exception as e:
-        return [("error", 0)], f"Error: {str(e)}", "Error generating report"
+#         return [(k, v) for k, v in emotions.items()], analysis['detailed_analysis'], report
+#     except Exception as e:
+#         return [("error", 0)], f"Error: {str(e)}", "Error generating report"
 
-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'
-    }
+# 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(emotions.items(), key=lambda x: x[1])
+#     top_emotion = max(emotions.items(), key=lambda x: x[1])
     
-    return {
-        "primary_emotion": top_emotion[0],
-        "detailed_analysis": f"Primary emotion detected is {top_emotion[0]} ({top_emotion[1]:.1f}%), which {descriptions.get(top_emotion[0], '')}."
-    }
+#     return {
+#         "primary_emotion": 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(emotions, analysis):
-    return f"""Emotion Analysis Report
+# def generate_report(emotions, analysis):
+#     return f"""Emotion Analysis Report
 
-Primary Emotion: {analysis['primary_emotion'].upper()}
-Confidence: {emotions[analysis['primary_emotion']]:.1f}%
+# Primary Emotion: {analysis['primary_emotion'].upper()}
+# Confidence: {emotions[analysis['primary_emotion']]:.1f}%
 
-All Detected Emotions:
-""" + '\n'.join(f"- {k}: {v:.1f}%" for k, v in sorted(
-        emotions.items(), 
-        key=lambda x: x[1], 
-        reverse=True))
+# All Detected Emotions:
+# """ + '\n'.join(f"- {k}: {v:.1f}%" for k, v in sorted(
+#         emotions.items(), 
+#         key=lambda x: x[1], 
+#         reverse=True))
 
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as app:
-    gr.Markdown("# Facial Emotion Analysis System")
+# # Create Gradio interface
+# with gr.Blocks(theme=gr.themes.Soft()) as app:
+#     gr.Markdown("# Facial Emotion Analysis System")
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_image = gr.Image(type="numpy")
-            submit_btn = gr.Button("Analyze", variant="primary")
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             input_image = gr.Image(type="numpy")
+#             submit_btn = gr.Button("Analyze", variant="primary")
             
-        with gr.Column(scale=1):
-            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")
+#         with gr.Column(scale=1):
+#             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")
     
-    submit_btn.click(
-        fn=process_image,
-        inputs=[input_image],
-        outputs=[emotion_scores, analysis_text, report_text]
-    )
+#     submit_btn.click(
+#         fn=process_image,
+#         inputs=[input_image],
+#         outputs=[emotion_scores, analysis_text, report_text]
+#     )
     
-    download_btn.click(
-        fn=lambda x: x,
-        inputs=[report_text],
-        outputs=[gr.File(label="Download Report")]
-    )
+#     download_btn.click(
+#         fn=lambda x: x,
+#         inputs=[report_text],
+#         outputs=[gr.File(label="Download Report")]
+#     )
 
-app.launch()
+# app.launch()