Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load Hugging Face object detection model
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_intrusion(video_path):
+    cap = cv2.VideoCapture(video_path)
+    alerts = []
+    count = 0
+    while cap.isOpened() and count < 20:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        inputs = processor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+            label_name = model.config.id2label[label.item()]
+            if label_name == "person":
+                alerts.append(f"Frame {count}: 🔴 Person Detected!")
+                break
+        count += 1
+    cap.release()
+    return "\n".join(alerts) if alerts else "✅ No intrusion detected."
+
+def detect_overheat(temp, humidity, solar_output):
+    if temp > 75:
+        return "🔥 Overheat Fault!"
+    elif humidity < 20 and solar_output < 300:
+        return "🌫️ Dust/Shade Fault!"
+    else:
+        return "✅ All Good"
+
+video_tab = gr.Interface(fn=detect_intrusion,
+                         inputs=gr.Video(label="Upload Video"),
+                         outputs=gr.Textbox(label="Intrusion Detection Alerts"))
+
+sensor_tab = gr.Interface(fn=detect_overheat,
+                          inputs=[gr.Number(label="Temperature (°C)"),
+                                  gr.Number(label="Humidity (%)"),
+                                  gr.Number(label="Solar Output (W)")],
+                          outputs=gr.Textbox(label="Sensor Fault Detection"))
+
+gr.TabbedInterface([video_tab, sensor_tab], ["Intrusion (Video)", "Sensor (Input)"]).launch()