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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="prithivMLmods/Food-101-93M"
)
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()
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