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+# Use standard python image
+FROM python:3.9-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create user with ID 1000
+RUN useradd -m -u 1000 user
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download model during build to avoid runtime permission errors
+RUN pip install ultralytics && \
+    python -c "from ultralytics import YOLO; YOLO('yolov8n.pt')"
+
+# Copy app code and CHANGE OWNER to user
+COPY --chown=user . .
+
+# Switch to user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH" \
+    PORT=7860
+
+# Expose port
+EXPOSE 7860
+
+# Run Flask
+CMD ["python", "app.py"]

app.py

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+import os
+import cv2
+import numpy as np
+import requests
+from flask import Flask, render_template, request, jsonify
+from ultralytics import YOLO
+from datetime import datetime
+
+app = Flask(__name__)
+
+# --- CONFIGURATION ---
+# PASTE YOUR MICROMIND API URL HERE (The one ending in /prediction/...)
+MICROMIND_API_URL = "https://intern.aimicromind.com/api/v1/prediction/616027a7-173c-4e52-b627-a4346da1c5c1"
+
+# GLOBAL SETTINGS (Controlled by MicroMind Agent later)
+current_settings = {
+    "target_class_id": 0,       # 0 = person
+    "target_label": "person",
+    "confidence": 0.5,
+    "status": "ARMED"
+}
+
+# Load AI Model (Runs once when server starts)
+print("--- Loading AI Model... ---")
+try:
+    model = YOLO('yolov8n.pt')
+    print("✅ Model Loaded.")
+except Exception as e:
+    print(f"⚠️ Model download/load warning: {e}")
+
+@app.route('/')
+def index():
+    """Serves the webpage to the user."""
+    return render_template('index.html')
+
+@app.route('/process_frame', methods=['POST'])
+def process_frame():
+    """Receives an image frame from the browser, runs AI, and returns results."""
+    
+    if current_settings['status'] == 'DISARMED':
+        return jsonify({"status": "DISARMED", "detections": []})
+
+    try:
+        # 1. Get Image from Request
+        file = request.files['image'].read()
+        npimg = np.frombuffer(file, np.uint8)
+        img = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
+
+        # 2. Run YOLO AI
+        results = model(img, verbose=False)
+        
+        detected = False
+        detections_list = []
+
+        # 3. Analyze Results
+        for result in results:
+            for box in result.boxes:
+                cls_id = int(box.cls[0])
+                conf = float(box.conf[0])
+                
+                # Check against current settings
+                if cls_id == current_settings['target_class_id'] and conf > current_settings['confidence']:
+                    detected = True
+                    detections_list.append({
+                        "label": current_settings['target_label'],
+                        "confidence": round(conf, 2),
+                        "bbox": box.xyxy[0].tolist()
+                    })
+
+        # 4. Alert MicroMind (Fire and Forget)
+        if detected:
+            print(f"🚨 THREAT: {current_settings['target_label']} detected!")
+            
+            alert_msg = (f"ALERT: {current_settings['target_label']} detected.\n"
+                         f"Source: Cloud Web Cam\n"
+                         f"Confidence: {detections_list[0]['confidence']}")
+            
+            try:
+                # Send alert to Agent (Timeout is short to keep video smooth)
+                requests.post(MICROMIND_API_URL, json={"question": alert_msg}, timeout=0.5)
+            except:
+                pass 
+
+        return jsonify({
+            "status": "ARMED",
+            "target": current_settings['target_label'],
+            "detections": detections_list
+        })
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/update_settings', methods=['POST'])
+def update_settings():
+    """Endpoint for MicroMind Agent to change settings."""
+    data = request.json
+    
+    # Simple mapping for demo
+    label_map = {
+        'person': 0, 'bicycle': 1, 'car': 2, 'motorcycle': 3, 'airplane': 4, 'bus': 5, 'train': 6, 'truck': 7, 'boat': 8,
+        'bird': 14, 'cat': 15, 'dog': 16, 'backpack': 24, 'umbrella': 25, 'handbag': 26, 'tie': 27, 'suitcase': 28,
+        'bottle': 39, 'cup': 41, 'fork': 42, 'knife': 43, 'spoon': 44, 'bowl': 45, 'banana': 46, 'apple': 47, 'sandwich': 48,
+        'chair': 56, 'couch': 57, 'bed': 59, 'dining table': 60, 'laptop': 63, 'mouse': 64, 'remote': 65, 'keyboard': 66,
+        'cell phone': 67, 'microwave': 68, 'oven': 69, 'toaster': 70, 'sink': 71, 'refrigerator': 72, 'book': 73,
+        'clock': 74, 'vase': 75, 'scissors': 76, 'teddy bear': 77, 'toothbrush': 79
+    }
+
+    if 'label' in data:
+        new_label = data['label'].lower()
+        if new_label in label_map:
+            current_settings['target_label'] = new_label
+            current_settings['target_class_id'] = label_map[new_label]
+    
+    if 'confidence' in data:
+        current_settings['confidence'] = float(data['confidence'])
+        
+    if 'status' in data:
+        current_settings['status'] = data['status'].upper()
+
+    return jsonify({"status": "success", "current_state": current_settings})
+
+if __name__ == '__main__':
+    # Hugging Face Spaces sets PORT env var to 7860
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host='0.0.0.0', port=port)

requirements.txt

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+fastapi
+uvicorn
+gradio>=4.0.0
+ultralytics
+opencv-python
+requests
+pydantic
+python-multipart
+huggingface-hub==0.22.2