Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load AI model (FLAN-T5 for text-based food recognition)
+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 also provides calorie data (parsing response)
+    try:
+        original_food, original_calories, substitute, substitute_calories = response.split(", ")
+        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 = (
+            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
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("# 🍽️ DeepCalorie - AI-Powered Food Substitute Finder")
+    gr.Markdown("Enter a food item, and get a **lower-calorie alternative** along with calories saved.")
+
+    with gr.Row():
+        food_input = gr.Textbox(label="Enter Food/Dish")
+        portion_input = gr.Number(label="Portion Size (grams)", value=100)
+
+    submit_button = gr.Button("Find Substitute")
+    output = gr.Markdown()
+
+    submit_button.click(get_food_substitute, inputs=[food_input, portion_input], outputs=output)
+
+# Run the app
+if __name__ == "__main__":
+    app.launch()