app.py

# app.py

import gradio as gr
import cv2
import os
from services.video_service import get_video_frame
from services.detection_service import detect_objects
from services.thermal_service import detect_thermal_anomalies
from services.shadow_detection import detect_shadow_coverage
from services.salesforce_dispatcher import send_to_salesforce

# Global frame generator
frame_gen = get_video_frame("data/drone_day.mp4")

def monitor_feed():
    try:
        frame = next(frame_gen)
        temp_path = "temp.jpg"
        cv2.imwrite(temp_path, frame)

        detections = detect_objects(temp_path)
        thermal_flags = detect_thermal_anomalies(temp_path)
        shadow_flag = detect_shadow_coverage(temp_path)

        # Prepare alert payload
        alert_payload = {
            "detections": detections,
            "thermal": bool(thermal_flags),
            "shadow_issue": shadow_flag,
        }
        send_to_salesforce(alert_payload)

        # Create human-readable alert
        detected_classes = [d['label'] for d in detections]
        confidence_scores = [round(d['score'] * 100, 2) for d in detections]
        confidence_max = max(confidence_scores) if confidence_scores else 0

        alert_message = f"""
        🚨 Detection Summary 🚨
        Detected Objects: {', '.join(detected_classes) if detected_classes else 'None'}
        Thermal Issue: {'Yes' if alert_payload['thermal'] else 'No'}
        Shadow Issue: {'Yes' if alert_payload['shadow_issue'] else 'No'}
        Highest Confidence: {confidence_max}%
        """

        # Return the frame and the alert
        return frame, alert_message.strip()

    except StopIteration:
        return None, "End of Video Feed."

# 🎯 Create better Gradio Interface
with gr.Blocks(title="Solar Surveillance Feed Simulation") as app:
    gr.Markdown("# ☀️ Solar Site Live Surveillance")
    gr.Markdown("### Live Drone Feed with AI Intrusion + Thermal + Shadow Monitoring")
    
    video_output = gr.Image(label="Drone Camera View", shape=(512, 512))
    alert_output = gr.Textbox(label="AI Generated Alerts", lines=5)

    live_button = gr.Button("Start Live Surveillance 🚀")

    live_button.click(fn=monitor_feed, outputs=[video_output, alert_output])

app.queue().launch()