Update app.py

app.py

@@ -1,154 +1,103 @@
+from PIL import Image
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+import warnings
+import requests
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
 
+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 = 'XshljGSwf/pq3GcgBdHtOg==G9X2wnPqW5c6vp0F'
+
+def identify_image(image_path):
+    """Identify the food item in the image."""
+    image = Image.open(image_path)
+    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):
+    """Get the calorie information of the identified food item."""
+    api_url = 'https://api.api-ninjas.com/v1/nutrition?query={}'.format(food_name)
+    response = requests.get(api_url, headers={'X-Api-Key': api_key})
+    if response.status_code == requests.codes.ok:
+        nutrition_info = response.json()
+    else:
+        nutrition_info = {"Error": response.status_code, "Message": response.text}
+    return nutrition_info
+
+def format_nutrition_info(nutrition_info):
+    """Format the nutritional information into an HTML table."""
+    if "Error" in nutrition_info:
+        return f"Error: {nutrition_info['Error']} - {nutrition_info['Message']}"
+    
+    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
+
+def main_process(image_path):
+    """Identify the food item and fetch its calorie information."""
+    food_name = identify_image(image_path)
+    nutrition_info = get_calories(food_name)
+    formatted_nutrition_info = format_nutrition_info(nutrition_info)
+    return formatted_nutrition_info
+
+# Define the Gradio interface
+def gradio_interface(image):
+    formatted_nutrition_info = main_process(image)
+    return formatted_nutrition_info
+
+# Create the Gradio UI
+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
+)
+
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()