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Update app.py
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app.py
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@@ -1,32 +1,33 @@
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import gradio as gr
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from transformers import pipeline
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# Load AI model (FLAN-T5 for
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food_model = pipeline("text2text-generation", model="google/flan-t5-small")
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# Function to get calorie data & substitute using AI
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def get_food_substitute(food, portion_size):
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# AI model processes input and provides a
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query = f"Suggest a
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response = food_model(query, max_length=
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#
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try:
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original_calories = float(original_calories.split(" ")[0]) * portion_size / 100
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substitute_calories = float(substitute_calories.split(" ")[0]) * portion_size / 100
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calories_saved = original_calories - substitute_calories
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result = (
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f"**Original Food:** {original_food} ({original_calories:.2f} kcal)\n"
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f"**Suggested Substitute:** {substitute} ({substitute_calories:.2f} kcal)\n"
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f"**Calories Saved:** {calories_saved:.2f} kcal"
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)
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except:
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return result
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# Gradio UI
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with gr.Blocks() as app:
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import gradio as gr
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from transformers import pipeline
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# Load AI model (FLAN-T5 for food recognition & substitution)
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food_model = pipeline("text2text-generation", model="google/flan-t5-small")
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# Function to get calorie data & substitute using AI
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def get_food_substitute(food, portion_size):
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# AI model processes input and provides a structured response
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query = f"Suggest a lower-calorie substitute for {food}. Format: Original Food - Calories, Substitute - Calories"
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response = food_model(query, max_length=100, truncation=True)[0]['generated_text']
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# Try to extract structured values from AI response
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try:
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parts = response.split(", ")
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if len(parts) < 4:
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return "AI model returned incomplete data. Try another food item."
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original_food, original_calories, substitute, substitute_calories = parts
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original_calories = float(original_calories.split(" ")[0]) * portion_size / 100
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substitute_calories = float(substitute_calories.split(" ")[0]) * portion_size / 100
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calories_saved = original_calories - substitute_calories
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return (
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f"**Original Food:** {original_food} ({original_calories:.2f} kcal)\n"
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f"**Suggested Substitute:** {substitute} ({substitute_calories:.2f} kcal)\n"
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f"**Calories Saved:** {calories_saved:.2f} kcal"
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)
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except Exception as e:
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return f"Error: AI model output format incorrect. Details: {str(e)}"
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# Gradio UI
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with gr.Blocks() as app:
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