Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+#
+# 1) Load a broad food classification model
+#    This recognizes 100+ food categories (food101).
+#
+food_classifier = pipeline(
+    "image-classification", 
+    model="nateraw/food101"
+)
+
+def estimate_health_and_calories(dish_name: str):
+
+    dish_lower = dish_name.lower()
+
+    # Basic logic: healthy if it sounds like salad, fruit, etc., 
+    # less healthy if it sounds like fried, sugary, or dessert.
+    
+    if any(k in dish_lower for k in ["salad", "fruit", "broccoli", "tomato", "carrot", "spinach"]):
+        health = 9
+        calories = 80
+    elif any(k in dish_lower for k in ["fried", "pizza", "burger", "bacon", "cream", "chips"]):
+        health = 3
+        calories = 350
+    elif any(k in dish_lower for k in ["cake", "pastry", "dessert", "cookie", "chocolate"]):
+        health = 2
+        calories = 400
+    elif any(k in dish_lower for k in ["soup", "stew", "chili"]):
+        health = 7
+        calories = 150
+    elif any(k in dish_lower for k in ["sandwich", "wrap", "taco"]):
+        health = 6
+        calories = 250
+    else:
+        # Default fallback
+        health = 5
+        calories = 200
+
+    return health, calories
+
+def analyze_image(image):
+
+    # Run the model
+    outputs = food_classifier(image)
+
+    # Each output item is like {'label': 'omelette', 'score': 0.98}
+    # Sort by descending confidence (not strictly necessary if pipeline does so by default)
+    outputs = sorted(outputs, key=lambda x: x["score"], reverse=True)
+    top_label = outputs[0]["label"]
+    top_score = outputs[0]["score"]
+
+    # Confidence threshold to decide if it's "real food" or not
+    if top_score < 0.5:
+        return "The picture does not depict any food, please upload a different photo."
+
+    # If we pass the threshold, treat the top label as recognized dish
+    health_rating, cal_estimate = estimate_health_and_calories(top_label)
+
+    # Build response text
+    return (
+        f"**Food Identified**: {top_label} (confidence: {top_score:.2f})\n\n"
+        f"**Health Rating** (1 = extremely unhealthy, 10 = extremely healthy): **{health_rating}**\n\n"
+        f"**Estimated Calories**: ~{cal_estimate} kcal per serving\n\n"
+    )
+
+
+# Build a nice Gradio interface
+demo = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.Image(type="pil"),   # PIL image
+    outputs="markdown",
+    title="Universal Food Recognizer",
+    description=(
+        "Upload a photo of a dish or ingredient. "
+        "We'll attempt to recognize it (from 100+ categories) and rate how healthy it is, "
+        "along with a rough calorie estimate. "
+        "If no food is detected, you'll see an error message."
+    ),
+    allow_flagging="never",  # optional: hides the 'flag' button for simpler UI
+)
+
+if __name__ == "__main__":
+    demo.launch()