Update app.py

app.py

@@ -82,61 +82,41 @@ def generate_pdf(info, advice, risk_score, risk_level, flags, conditions, lang,
     pdf.output(path)
     return path
 
-# Main function
-def classify_food(image, conditions, language):
-    if not conditions:
-        return "⚠️ Please select at least one health condition.", None, None, None
-
-    # Preprocess image
-    image = image.convert("RGB").resize((225, 225))
-    img = tf.keras.preprocessing.image.img_to_array(image)
-    img = efficientnet_preprocess(img)
-    img = np.expand_dims(img, axis=0)
-
-    # Predict
-    preds = model.predict(img)[0]
-    idx = np.argmax(preds)
-    predicted_class = class_names[idx]
-    confidence = round(float(preds[idx]) * 100, 2)
-
-    info = food_info[predicted_class]
-    display_name = info.get("display_name", predicted_class.replace("_", " ").title())
-
-    # Advice logic
-    advice, risk_score, flags = generate_advice(info, conditions)
-    risk_level = get_risk_level(risk_score)
-
-    # Save audio
-    audio_path = os.path.join(tempfile.gettempdir(), "tts.mp3")
-    tts = gTTS(text=advice, lang=LANG_CODE.get(language, "en"))
-    tts.save(audio_path)
-
-    # Save image
-    image_path = os.path.join(tempfile.gettempdir(), "upload.jpg")
-    image.save(image_path)
-
-    # Generate PDF
-    pdf_path = generate_pdf(info, advice, risk_score, risk_level, flags, conditions, language, display_name, image_path)
-
-    # Output summary
-    result = (
-        f"🍽️ Food: {display_name}\n"
-        f"🌍 Ethnicity: {info['ethnicity']}\n"
-        f"🥦 Ingredients: {info['ingredients']}\n"
-        f"🔥 Calories: {info['calories']} kcal\n"
-        f"🍞 Carbs: {info['carbs']}g\n"
-        f"🥩 Protein: {info['protein']}g\n"
-        f"🧈 Fat: {info['fat']}g\n"
-        f"🌱 Diet Type: {info['diet_type']}\n"
-        f"🔁 Substitute: {info.get('substitute', 'None')}\n\n"
-        f"📈 Confidence: {confidence}%\n"
-        f"📊 Risk Score: {risk_score}% ({risk_level})\n"
-        f"⚠️ Risk Factors: {', '.join(flags) if flags else 'None'}\n"
-        f"✅ Advice: {advice}\n\n"
-        f"📝 *Generated by HoodHealth Pro+.*"
-    )
-
-    return result, audio_path, f"🗣️ Language: {language}", pdf_path
+def classify_food(image):
+    try:
+        # Convert to grayscale since model expects 1 channel
+        image = image.convert("L").resize((225, 225))  # "L" = grayscale
+        img = tf.keras.preprocessing.image.img_to_array(image)
+        
+        # Ensure shape is (225, 225, 1)
+        if img.shape[-1] != 1:
+            img = np.expand_dims(img, axis=-1)
+        
+        # Preprocess for EfficientNet
+        img = efficientnet_preprocess(img)
+        img = np.expand_dims(img, axis=0)
+
+        # Predict
+        preds = model.predict(img)
+        pred_class = np.argmax(preds[0])
+        confidence = float(np.max(preds[0]))
+
+        food_name = class_names[pred_class]
+        food_details = food_info[food_name]
+
+        result = f"🍽️ Food: {food_name}\n"
+        result += f"🌍 Ethnicity: {food_details['Ethnicity']}\n"
+        result += f"🥦 Ingredients: {', '.join(food_details['Ingredients'])}\n"
+        result += f"🔥 Nutrients: {food_details['Nutrients']}\n"
+        result += f"❤️ Health Advice: {food_details['Health_Advice']}\n"
+        result += f"🌱 Diet Type: {food_details['Diet_Type']}\n"
+        result += f"📊 Confidence: {confidence:.2f}"
+
+        return result
+
+    except Exception as e:
+        return f"Error during classification: {str(e)}"
+
 
 # Gradio interface
 interface = gr.Interface(