Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from services.video_service import VideoService
+from services.detection_service import DetectionService
+from services.thermal_service import ThermalService
+from services.shadow_detection import ShadowDetection
+from services.salesforce_dispatcher import SalesforceDispatcher
+import os
+
+# Initialize services
+video_service = VideoService()
+detection_service = DetectionService(model_name="facebook/detr-resnet-50")
+thermal_service = ThermalService()
+shadow_detection = ShadowDetection()
+salesforce_dispatcher = SalesforceDispatcher()
+
+# Paths to video files
+VIDEO_PATHS = {
+    "Day Feed": "data/drone_day.mp4",
+    "Night Feed": "data/night_intrusion.mp4",
+    "Thermal Feed": "data/thermal_hotspot.mp4",
+    "Shadow/Dust Feed": "data/shadow_dust_issue.mp4",
+}
+
+def process_video(video_type, confidence_threshold=0.9):
+    """Process the selected video feed and return results."""
+    video_path = VIDEO_PATHS.get(video_type)
+    if not video_path or not os.path.exists(video_path):
+        return "Video file not found.", None, None
+
+    # Load and process video
+    frames = video_service.load_video(video_path)
+    results = []
+    output_frames = []
+
+    for frame in frames:
+        # Convert frame to PIL Image
+        frame_pil = video_service.frame_to_pil(frame)
+
+        if video_type == "Thermal Feed":
+            # Detect overheating (hot spots)
+            detections = thermal_service.detect_hotspots(frame_pil, detection_service, confidence_threshold)
+            alert_type = "Overheating"
+        elif video_type == "Shadow/Dust Feed":
+            # Detect dusty or shaded panels
+            detections = shadow_detection.detect_shadow_dust(frame_pil, detection_service, confidence_threshold)
+            alert_type = "Shadow/Dust"
+        else:
+            # General object detection for day/night feeds
+            detections = detection_service.detect_objects(frame_pil, confidence_threshold)
+            alert_type = "General"
+
+        # Draw detections on frame
+        annotated_frame = video_service.draw_detections(frame, detections)
+
+        # Generate Salesforce case and notifications
+        if detections:
+            case_id = salesforce_dispatcher.create_case(
+                subject=f"{alert_type} Detected in {video_type}",
+                description=str(detections)
+            )
+            salesforce_dispatcher.send_email(
+                to="admin@solarplant.com",
+                subject=f"Alert: {alert_type} in {video_type}",
+                body=f"Case ID: {case_id}\nDetails: {detections}"
+            )
+            salesforce_dispatcher.send_whatsapp(
+                to="+1234567890",
+                message=f"Alert: {alert_type} detected in {video_type}. Case ID: {case_id}"
+            )
+
+        results.append(detections)
+        output_frames.append(annotated_frame)
+
+    # Save output video
+    output_path = "output_annotated.mp4"
+    video_service.save_video(output_frames, output_path)
+
+    return str(results), output_path, None
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Solar Panel Monitoring System")
+    video_type = gr.Dropdown(
+        choices=["Day Feed", "Night Feed", "Thermal Feed", "Shadow/Dust Feed"],
+        label="Select Drone Feed"
+    )
+    confidence_threshold = gr.Slider(0.5, 1.0, value=0.9, label="Confidence Threshold")
+    process_button = gr.Button("Process Video")
+    output_text = gr.Textbox(label="Detection Results")
+    output_video = gr.Video(label="Annotated Video")
+    error_message = gr.Textbox(label="Error Message")
+
+    process_button.click(
+        fn=process_video,
+        inputs=[video_type, confidence_threshold],
+        outputs=[output_text, output_video, error_message]
+    )
+
+demo.launch()