Create app.py

app.py

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+import gradio as gr
+import cv2
+import os
+import numpy as np
+from datetime import datetime
+import matplotlib.pyplot as plt
+from services.detection_service import detect_faults_solar, detect_faults_windmill
+from services.anomaly_service import track_faults, predict_fault
+from models.solar_model import load_solar_model
+from models.windmill_model import load_windmill_model
+from config.settings import VIDEO_FOLDER
+
+# Initialize global state
+logs = []
+fault_counts = []
+frame_numbers = []
+total_detected = 0
+
+# Custom CSS to match the screenshot
+css = """
+<style>
+.main-header {
+    text-align: center;
+    font-size: 24px;
+    font-weight: bold;
+    color: #333;
+    margin-bottom: 10px;
+}
+.status {
+    text-align: center;
+    font-size: 16px;
+    color: #333;
+    margin-bottom: 20px;
+}
+.section-title {
+    font-size: 16px;
+    font-weight: bold;
+    color: #333;
+    text-transform: uppercase;
+    margin-bottom: 10px;
+}
+.section-box {
+    border: 1px solid #4A90E2;
+    padding: 10px;
+    border-radius: 5px;
+    margin-bottom: 20px;
+}
+.log-entry {
+    font-size: 14px;
+    color: #333;
+    margin-bottom: 5px;
+}
+.metrics-text {
+    font-size: 14px;
+    color: #333;
+    margin-bottom: 5px;
+}
+</style>
+"""
+
+def process_video(video_path, detection_type):
+    global logs, fault_counts, frame_numbers, total_detected
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        return "Error: Could not open video file.", None, None, None, None, None
+
+    model = load_solar_model() if detection_type == "Solar Panel" else load_windmill_model()
+    frame_count = 0
+
+    # Clear previous state for a new video
+    logs.clear()
+    fault_counts.clear()
+    frame_numbers.clear()
+    total_detected = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_count += 1
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Detect faults
+        faults = detect_faults_solar(model, frame_rgb) if detection_type == "Solar Panel" else detect_faults_windmill(model, frame_rgb)
+        num_faults = len(faults)
+
+        # Draw bounding boxes and labels
+        for fault in faults:
+            x, y = int(fault['location'][0]), int(fault['location'][1])
+            cv2.rectangle(frame_rgb, (x-30, y-30), (x+30, y+30), (255, 0, 0), 2)
+            cv2.putText(frame_rgb, f"{fault['type']}", (x, y-40), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
+
+        # Update state
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        log_entry = f"{timestamp} - Frame {frame_count} - Faults: {num_faults}"
+        logs.append(log_entry)
+        total_detected += num_faults
+        fault_counts.append(num_faults)
+        frame_numbers.append(frame_count)
+
+        # Limit to last 100 frames
+        if len(frame_numbers) > 100:
+            frame_numbers.pop(0)
+            fault_counts.pop(0)
+
+        # Prepare outputs
+        video_output = frame_rgb
+        metrics = f"faults: {num_faults}<br>total_detected: {total_detected}"
+        live_logs = "<br>".join(logs[-20:])  # Show last 20 logs for brevity
+        last_5_events = "<br>".join(logs[-5:]) if logs else "No events yet"
+        prediction = "Potential fault escalation detected!" if predict_fault(fault_counts) else ""
+
+        # Generate trends graph
+        fig, ax = plt.subplots(figsize=(6, 3))
+        ax.plot(frame_numbers, fault_counts, marker='o', color='blue')
+        ax.set_title("Faults Over Time", fontsize=10)
+        ax.set_xlabel("Frame", fontsize=8)
+        ax.set_ylabel("Count", fontsize=8)
+        ax.grid(True)
+        ax.tick_params(axis='both', which='major', labelsize=6)
+        plt.tight_layout()
+
+        return video_output, metrics, live_logs, last_5_events, fig, prediction
+
+# Gradio interface
+with gr.Blocks(css=css) as demo:
+    gr.Markdown('<div class="main-header">THERMAL FAULT DETECTION DASHBOARD</div>')
+    gr.Markdown('<div class="status">🟢 RUNNING</div>')
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            with gr.Box():
+                gr.Markdown('<div class="section-title">LIVE VIDEO FEED</div>', unsafe_allow_html=True)
+                video_output = gr.Image(label="", interactive=False)
+        with gr.Column(scale=1):
+            with gr.Box():
+                gr.Markdown('<div class="section-title">LIVE METRICS</div>', unsafe_allow_html=True)
+                metrics_output = gr.Markdown(label="", unsafe_allow_html=True)
+                prediction_output = gr.Markdown(label="")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Box():
+                gr.Markdown('<div class="section-title">LIVE LOGS</div>', unsafe_allow_html=True)
+                logs_output = gr.Markdown(label="", unsafe_allow_html=True)
+            with gr.Box():
+                gr.Markdown('<div class="section-title">LAST 5 CAPTURED EVENTS</div>', unsafe_allow_html=True)
+                events_output = gr.Markdown(label="", unsafe_allow_html=True)
+        with gr.Column(scale=2):
+            with gr.Box():
+                gr.Markdown('<div class="section-title">DETECTION TRENDS</div>', unsafe_allow_html=True)
+                gr.Markdown('<div style="font-size: 14px; font-weight: bold; margin-bottom: 10px;">Faults Over Time</div>', unsafe_allow_html=True)
+                trends_output = gr.Plot(label="")
+
+    # Sidebar for settings
+    with gr.Row():
+        with gr.Column():
+            video_files = [f for f in os.listdir(VIDEO_FOLDER) if f.endswith('.mp4')]
+            video_input = gr.Dropdown(choices=video_files, label="Select Video")
+            detection_type = gr.Dropdown(choices=["Solar Panel", "Windmill"], label="Detection Type")
+            submit_btn = gr.Button("Start Processing")
+
+    # Connect inputs to outputs
+    submit_btn.click(
+        fn=process_video,
+        inputs=[video_input, detection_type],
+        outputs=[video_output, metrics_output, logs_output, events_output, trends_output, prediction_output],
+        _js="() => [document.querySelector('input[type=\"file\"]').value, document.querySelector('select[name=\"detection_type\"]').value]"
+    )
+
+demo.launch()