File size: 3,475 Bytes
7aea1a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
```python
from flask import Flask, request, jsonify
from werkzeug.utils import secure_filename
import os
from transformers import pipeline
from PIL import Image
import torch
app = Flask(__name__)
# Configure upload folder
UPLOAD_FOLDER = 'uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
# Load Hugging Face model (this would be your actual food/toxin analysis model)
toxin_classifier = pipeline(
"image-classification",
model="facebook/deit-base-patch16-224" # Example model - replace with your actual food/toxin model
)
@app.route('/api/analyze', methods=['POST'])
def analyze_image():
if 'image' not in request.files:
return jsonify({'error': 'No image provided'}), 400
file = request.files['image']
if file.filename == '':
return jsonify({'error': 'No selected file'}), 400
if file:
filename = secure_filename(file.filename)
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(filepath)
try:
# Process image with Hugging Face model
image = Image.open(filepath)
results = toxin_classifier(image)
# Process results into your application's format
# This is placeholder logic - you would customize based on your model's output
toxin_level = "Medium"
safety = "Caution"
recommendation = "Limit consumption to 1 serving per week"
# For demo purposes, we'll use the first prediction
if results and len(results) > 0:
main_prediction = results[0]
if main_prediction['score'] > 0.7:
toxin_level = "High"
safety = "Avoid"
recommendation = "Do not consume - high toxin levels detected"
elif main_prediction['score'] > 0.4:
toxin_level = "Medium"
safety = "Caution"
else:
toxin_level = "Low"
safety = "Safe"
return jsonify({
'toxin_level': toxin_level,
'safety': safety,
'recommendation': recommendation,
'image_path': f'/uploads/{filename}'
})
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(debug=True)
```
Key changes made:
1. Added file upload and analysis functionality to the dashboard
2. Created a modal to display scan results
3. Added Flask backend API that:
- Accepts image uploads
- Processes them with a Hugging Face model
- Returns analysis results
4. Included error handling and loading states
5. Added UI for displaying the scan results
To implement this:
1. Install required Python packages:
```bash
pip install flask torch transformers pillow
```
2. Choose an appropriate Hugging Face model for your food/toxin analysis needs. The example uses a generic image classification model, but you might want:
- A food-specific model
- A toxicity detection model
- Or a custom fine-tuned model
3. Run the Flask API:
```bash
python api.py
```
4. Update the frontend to point to your API endpoint
The backend currently uses a placeholder model - you should replace it with a model that's actually trained for food toxin analysis. Hugging Face has several food-related models that could be adapted for this purpose. |