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app.py

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+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import requests
+import io
+import warnings
+import gradio as gr
+import threading
+import uvicorn
+
+warnings.filterwarnings('ignore')
+
+# Load the pre-trained Vision Transformer model and feature extractor
+model_name = "google/vit-base-patch16-224"
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(model_name)
+
+# API key for the nutrition information
+api_key = 'TTnVMFxStvcP3sUXow/sGw==CnAQfUtuozhAAO5M'
+
+app = FastAPI()
+
+def identify_image(image: Image.Image):
+    """Identify the food item in the image."""
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_label = model.config.id2label[predicted_class_idx]
+    food_name = predicted_label.split(',')[0]
+    return food_name
+
+def get_calories(food_name: str):
+    """Get the calorie information of the identified food item."""
+    api_url = f'https://api.api-ninjas.com/v1/nutrition?query={food_name}'
+    response = requests.get(api_url, headers={'X-Api-Key': api_key})
+    if response.status_code == requests.codes.ok:
+        return response.json()
+    else:
+        raise HTTPException(status_code=response.status_code, detail=response.text)
+
+@app.post("/identify_and_get_nutrition")
+async def identify_and_get_nutrition(file: UploadFile = File(...)):
+    image_data = await file.read()
+    image = Image.open(io.BytesIO(image_data))
+    food_name = identify_image(image)
+    nutrition_info = get_calories(food_name)
+    if len(nutrition_info) == 0:
+        return JSONResponse(content={"message": "No nutritional information found."}, status_code=404)
+    return nutrition_info
+
+# Gradio Interface
+def gradio_interface(image_file):
+    image = Image.open(image_file)
+    food_name = identify_image(image)
+    nutrition_info = get_calories(food_name)
+    if len(nutrition_info) == 0:
+        return "No nutritional information found."
+    
+    nutrition_data = nutrition_info[0]
+    table = f"""
+    <table border="1" style="width: 100%; border-collapse: collapse;">
+        <tr><th colspan="4" style="text-align: center;"><b>Nutrition Facts</b></th></tr>
+        <tr><td colspan="4" style="text-align: center;"><b>Food Name: {nutrition_data['name']}</b></td></tr>
+        <tr>
+            <td style="text-align: left;"><b>Calories</b></td><td style="text-align: right;">{nutrition_data['calories']}</td>
+            <td style="text-align: left;"><b>Serving Size (g)</b></td><td style="text-align: right;">{nutrition_data['serving_size_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Total Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_total_g']}</td>
+            <td style="text-align: left;"><b>Saturated Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_saturated_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Protein (g)</b></td><td style="text-align: right;">{nutrition_data['protein_g']}</td>
+            <td style="text-align: left;"><b>Sodium (mg)</b></td><td style="text-align: right;">{nutrition_data['sodium_mg']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Potassium (mg)</b></td><td style="text-align: right;">{nutrition_data['potassium_mg']}</td>
+            <td style="text-align: left;"><b>Cholesterol (mg)</b></td><td style="text-align: right;">{nutrition_data['cholesterol_mg']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Total Carbohydrates (g)</b></td><td style="text-align: right;">{nutrition_data['carbohydrates_total_g']}</td>
+            <td style="text-align: left;"><b>Fiber (g)</b></td><td style="text-align: right;">{nutrition_data['fiber_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Sugar (g)</b></td><td style="text-align: right;">{nutrition_data['sugar_g']}</td>
+            <td></td><td></td>
+        </tr>
+    </table>
+    """
+    return table
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="filepath"),
+    outputs="html",
+    title="Food Identification and Nutrition Info",
+    description="Upload an image of food to get nutritional information.",
+    allow_flagging="never"  # Disable flagging
+)
+
+def run_gradio():
+    iface.launch(share=True)
+
+def run_fastapi():
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    # Run Gradio and FastAPI in separate threads
+    gradio_thread = threading.Thread(target=run_gradio)
+    fastapi_thread = threading.Thread(target=run_fastapi)
+
+    gradio_thread.start()
+    fastapi_thread.start()
+
+    gradio_thread.join()
+    fastapi_thread.join()

requirements.txt

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+gradio
+transformers
+torch
+torchvision
+requests
+python-dotenv