Update app.py

app.py

@@ -1,32 +1,33 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load AI model (FLAN-T5 for text-based food recognition)
+# Load AI model (FLAN-T5 for food recognition & substitution)
 food_model = pipeline("text2text-generation", model="google/flan-t5-small")
 
 # Function to get calorie data & substitute using AI
 def get_food_substitute(food, portion_size):
-    # AI model processes input and provides a substitute
-    query = f"Suggest a healthier food alternative for {food} along with its calorie content"
-    response = food_model(query, max_length=50)[0]['generated_text']
+    # AI model processes input and provides a structured response
+    query = f"Suggest a lower-calorie substitute for {food}. Format: Original Food - Calories, Substitute - Calories"
+    response = food_model(query, max_length=100, truncation=True)[0]['generated_text']
 
-    # AI also provides calorie data (parsing response)
+    # Try to extract structured values from AI response
     try:
-        original_food, original_calories, substitute, substitute_calories = response.split(", ")
+        parts = response.split(", ")
+        if len(parts) < 4:
+            return "AI model returned incomplete data. Try another food item."
+
+        original_food, original_calories, substitute, substitute_calories = parts
         original_calories = float(original_calories.split(" ")[0]) * portion_size / 100
         substitute_calories = float(substitute_calories.split(" ")[0]) * portion_size / 100
         calories_saved = original_calories - substitute_calories
 
-        # Format results
-        result = (
+        return (
             f"**Original Food:** {original_food} ({original_calories:.2f} kcal)\n"
             f"**Suggested Substitute:** {substitute} ({substitute_calories:.2f} kcal)\n"
             f"**Calories Saved:** {calories_saved:.2f} kcal"
         )
-    except:
-        result = "Error: AI model could not process the input correctly."
-
-    return result
+    except Exception as e:
+        return f"Error: AI model output format incorrect. Details: {str(e)}"
 
 # Gradio UI
 with gr.Blocks() as app: