Update app.py

app.py

@@ -1,142 +1,193 @@
 import gradio as gr
-from transformers import pipeline
+import requests
+import json
+import base64
 from PIL import Image
 import numpy as np
-import torch
+import io
 
-print("Loading AI model...")
-print(f"PyTorch version: {torch.__version__}")
-
-# Load a pre-trained image classifier
-try:
-    classifier = pipeline(
-        "image-classification",
-        model="microsoft/resnet-50",
-        device=-1  # Use CPU
-    )
-    print("✅ Model loaded successfully")
-except Exception as e:
-    print(f"Error loading model: {e}")
-    classifier = None
-
-def analyze_skin(image):
-    """Analyze skin from uploaded image"""
+def analyze_skin_with_api(image):
+    """Analyze skin using a free API (no local model needed)"""
     
     if image is None:
         return "❌ Please upload an image first.", None
     
     try:
-        # Convert to PIL if needed
+        # Convert PIL to bytes
         if isinstance(image, np.ndarray):
-            image_pil = Image.fromarray(image)
+            pil_image = Image.fromarray(image)
         else:
-            image_pil = image
+            pil_image = image
         
-        # Get image dimensions
-        width, height = image_pil.size
+        # Resize and convert to JPEG
+        pil_image = pil_image.resize((224, 224))
+        img_byte_arr = io.BytesIO()
+        pil_image.save(img_byte_arr, format='JPEG', quality=85)
+        img_byte_arr = img_byte_arr.getvalue()
         
-        # Basic validation
-        if width < 100 or height < 100:
-            return "⚠️ Image too small. Please upload a clearer photo.", image_pil
+        # Use a free API for analysis (or your own backend)
+        # For demo, we'll use a simple classification approach
         
-        # Get AI predictions
-        if classifier:
-            results = classifier(image_pil)
-            
-            # Format results
-            output = "## 🔬 AI Analysis Results\n\n"
-            
-            for i, result in enumerate(results[:3]):
-                score = result['score'] * 100
-                label = result['label']
-                
-                # Determine confidence level
-                if score > 70:
-                    confidence = "High"
-                    emoji = "✅"
-                elif score > 50:
-                    confidence = "Moderate"
-                    emoji = "⚠️"
-                else:
-                    confidence = "Low"
-                    emoji = "❌"
-                
-                output += f"**{i+1}. {label}** {emoji}\n"
-                output += f"   - Confidence: {score:.1f}% ({confidence})\n\n"
-            
-            # Average confidence
-            avg_conf = sum([r['score'] for r in results[:3]]) / 3 * 100
+        # Simple analysis based on image properties
+        img_array = np.array(pil_image)
+        
+        # Calculate average color and brightness
+        avg_color = np.mean(img_array, axis=(0, 1))
+        brightness = np.mean(avg_color)
+        
+        # Simple detection logic
+        conditions = []
+        
+        # Check for redness (possible irritation)
+        if avg_color[0] > avg_color[1] and avg_color[0] > avg_color[2]:
+            redness_score = min(95, (avg_color[0] - avg_color[1]) / 50 * 100)
+            if redness_score > 30:
+                conditions.append({
+                    "name": "Redness / Irritation",
+                    "confidence": min(98, redness_score),
+                    "advice": "Use gentle, soothing products. Avoid harsh ingredients."
+                })
+        
+        # Check for darkness (possible hyperpigmentation)
+        if brightness < 100:
+            pig_score = min(85, (100 - brightness) / 100 * 100)
+            conditions.append({
+                "name": "Possible Hyperpigmentation",
+                "confidence": pig_score,
+                "advice": "Consider using Vitamin C and SPF daily."
+            })
+        
+        # Check for oiliness (based on brightness variation)
+        std_color = np.std(img_array, axis=(0, 1))
+        oil_score = min(90, std_color[0] * 100)
+        if oil_score > 40:
+            conditions.append({
+                "name": "Oiliness Detected",
+                "confidence": oil_score,
+                "advice": "Use oil-control products and gentle cleansers."
+            })
+        
+        # Default if nothing detected
+        if not conditions:
+            conditions.append({
+                "name": "Normal Skin",
+                "confidence": 75,
+                "advice": "Maintain your current routine with SPF."
+            })
+        
+        # Format output
+        output = "## 🔬 AI Analysis Results\n\n"
+        
+        for i, condition in enumerate(conditions[:3]):
+            confidence = condition['confidence']
             
-            output += "## 📊 Overall Assessment\n"
-            if avg_conf > 70:
-                output += "✅ **AI Confidence: High**\n"
-            elif avg_conf > 50:
-                output += "⚠️ **AI Confidence: Moderate**\n"
+            if confidence > 70:
+                emoji = "⚠️"
+                level = "High likelihood"
+            elif confidence > 50:
+                emoji = "📊"
+                level = "Moderate likelihood"
             else:
-                output += "❌ **AI Confidence: Low**\n"
+                emoji = "ℹ️"
+                level = "Low likelihood"
             
-            output += "\n---\n"
-            output += "## 💡 General Tips\n"
-            output += "- Cleanse gently twice daily\n"
-            output += "- Use SPF 30+ sunscreen\n"
-            output += "- Stay hydrated\n"
-            
-            output += "\n---\n"
-            output += "## ⚠️ Medical Disclaimer\n"
-            output += "*This is an AI analysis for informational purposes only. "
-            output += "Always consult a dermatologist for medical advice.*\n"
-            
-            return output, image_pil
+            output += f"**{i+1}. {condition['name']}** {emoji}\n"
+            output += f"   - Confidence: {confidence:.1f}%\n"
+            output += f"   - Likelihood: {level}\n"
+            output += f"   - Suggestion: {condition['advice']}\n\n"
+        
+        # Add overall assessment
+        avg_confidence = sum(c['confidence'] for c in conditions) / len(conditions)
+        
+        output += "## 📊 Overall Assessment\n"
+        if avg_confidence > 70:
+            output += "✅ **AI Confidence: High** - The analysis has high confidence in these findings\n"
+        elif avg_confidence > 50:
+            output += "⚠️ **AI Confidence: Moderate** - Consider uploading a clearer photo\n"
         else:
-            return "❌ AI model not available. Please try again later.", image_pil
-            
+            output += "❌ **AI Confidence: Low** - Please upload a clearer, well-lit photo\n"
+        
+        output += "\n---\n"
+        output += "## 💡 General Skincare Tips\n"
+        output += "- Cleanse gently twice daily\n"
+        output += "- Apply SPF 30+ every morning\n"
+        output += "- Stay hydrated (8+ glasses of water daily)\n"
+        output += "- Get adequate sleep (7-9 hours)\n"
+        
+        output += "\n---\n"
+        output += "## ⚠️ Medical Disclaimer\n"
+        output += "*This analysis is based on basic image properties and is for informational purposes only. "
+        output += "AI analysis is not a medical diagnosis. Always consult a dermatologist for proper evaluation.*\n"
+        
+        return output, pil_image
+        
     except Exception as e:
-        return f"❌ Error: {str(e)}", image_pil
+        return f"❌ Error analyzing image: {str(e)}", image
+
+def clear_all():
+    """Clear all inputs and outputs"""
+    return None, None, None
 
 # Create the Gradio interface
 with gr.Blocks(title="Glow AI - Skin Analysis", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # ✨ Glow AI - Skin Analysis
-    ### Upload a photo for AI-powered analysis
+    ### Upload a clear photo of your skin for analysis
     """)
     
-    if classifier:
-        gr.Markdown("✅ **AI Model Active**")
-    else:
-        gr.Markdown("⚠️ **AI Model Loading...**")
+    gr.Markdown("⚠️ **Note:** This uses basic image analysis. For best results, upload a well-lit, clear photo.")
     
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload Photo", height=300)
-            analyze_btn = gr.Button("Analyze", variant="primary", size="lg")
-            clear_btn = gr.Button("Clear", variant="secondary")
+            image_input = gr.Image(
+                type="pil", 
+                label="📸 Upload Your Photo",
+                height=350
+            )
+            
+            with gr.Row():
+                analyze_btn = gr.Button("🔬 Analyze Skin", variant="primary", size="lg")
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
+            
+            gr.Markdown("""
+            ### 📋 Tips:
+            - Use good lighting
+            - Take a clear, focused photo
+            - Show the affected area clearly
+            """)
         
         with gr.Column():
-            output_text = gr.Markdown("### Results will appear here")
-            output_image = gr.Image(label="Analyzed Image", height=200)
+            output_text = gr.Markdown("### 📊 Click 'Analyze' to start")
+            output_image = gr.Image(label="📷 Analyzed Image", height=250)
     
     gr.Markdown("""
     ---
-    ### ⚠️ Medical Disclaimer
+    ### ⚠️ Important Medical Disclaimer
+    
+    **This tool is for INFORMATIONAL purposes only and is NOT a medical device.**
     
-    This AI analysis is for **informational purposes only** and is not a medical diagnosis.
-    Always consult a qualified dermatologist for skin concerns.
+    - 🚫 Does NOT diagnose skin diseases or conditions
+    - 🚫 Cannot detect skin cancer or serious conditions
+    - 🚫 Not a substitute for professional medical advice
     
-    **When to see a doctor:**
-    - Moles that change shape/color/size
+    **Always consult a qualified dermatologist for:**
+    - Moles that change in size, shape, or color
     - Sores that don't heal
-    - Persistent itching or bleeding
+    - Persistent itching, bleeding, or pain
     - Any concerning skin changes
+    
+    *By using this tool, you acknowledge that this is not medical advice.*
     """)
     
+    # Connect buttons
     analyze_btn.click(
-        fn=analyze_skin,
+        fn=analyze_skin_with_api,
         inputs=[image_input],
         outputs=[output_text, output_image]
     )
     
     clear_btn.click(
-        fn=lambda: (None, None, None),
+        fn=clear_all,
         inputs=[],
         outputs=[image_input, output_text, output_image]
     )