Create app.py

app.py

@@ -0,0 +1,57 @@
+from flask import Flask, render_template, request, jsonify
+import os
+import base64
+import requests
+from PIL import Image
+from io import BytesIO
+
+app = Flask(__name__)
+
+# Configuration
+UPLOAD_FOLDER = 'static/captures'
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# Hugging Face API settings (replace with your API key)
+HF_API_TOKEN = "your_hugging_face_api_token"  # Get from https://huggingface.co/settings/tokens
+HF_API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50"  # Example model for object detection
+
+def query_hugging_face(image_data):
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+    response = requests.post(HF_API_URL, headers=headers, data=image_data)
+    return response.json()
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/capture', methods=['POST'])
+def capture():
+    try:
+        # Get the base64 image data from the request
+        data = request.form['image']
+        header, encoded = data.split(",", 1)
+        binary_data = base64.b64decode(encoded)
+        
+        # Save the image
+        filename = f"capture_{len(os.listdir(app.config['UPLOAD_FOLDER'])) + 1}.jpg"
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        with open(filepath, "wb") as f:
+            f.write(binary_data)
+        
+        # Process with Hugging Face API
+        with open(filepath, "rb") as f:
+            hf_result = query_hugging_face(f.read())
+        
+        # Return the image URL and Hugging Face result
+        image_url = f"/{filepath}"
+        return jsonify({
+            'status': 'success',
+            'image_url': image_url,
+            'hf_result': hf_result
+        })
+    except Exception as e:
+        return jsonify({'status': 'error', 'message': str(e)})
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)