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import streamlit as st
import cv2
import requests
from transformers import pipeline
from ultralytics import YOLO
import numpy as np
from io import BytesIO
# Initialize the object detection model
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
thermal_model = YOLO("thermal_model.pt")
def detect_intrusion(image):
detections = object_detector(image)
return [d for d in detections if d['score'] > 0.7]
def detect_thermal_anomalies(image):
results = thermal_model(image)
flagged = []
for r in results:
if hasattr(r, 'temperature') and r.temperature > 75:
flagged.append(r)
return flagged
def detect_shading(image):
# Basic approach to detect shadows or dust
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return len(contours) > 5 # heuristic for detecting large shadow regions
def process_frame(frame):
# Convert the frame into the format expected by the AI models
detections = detect_intrusion(frame)
thermal_anomalies = detect_thermal_anomalies(frame)
shading = detect_shading(frame)
return detections, thermal_anomalies, shading
def create_alert(detections, thermal_anomalies, shading):
alert_message = "Solar Panel Fault Detected!"
if detections:
alert_message += " Intrusion detected!"
if thermal_anomalies:
alert_message += " Overheating detected!"
if shading:
alert_message += " Shading or dust detected!"
# Optionally send to Salesforce or another CRM system
payload = {
"Alert_Type__c": "Fault Detected",
"Message__c": alert_message,
"Confidence_Score__c": 85 # Example value, replace with actual confidence
}
requests.post("YOUR_SALESFORCE_API_ENDPOINT", json=payload)
return alert_message
# Streamlit interface
st.title("Solar Panel Fault Detection")
uploaded_file = st.file_uploader("Upload a video", type=["mp4"])
if uploaded_file:
video_bytes = uploaded_file.read()
video = cv2.VideoCapture(BytesIO(video_bytes))
while video.isOpened():
ret, frame = video.read()
if not ret:
break
detections, thermal_anomalies, shading = process_frame(frame)
alert_message = create_alert(detections, thermal_anomalies, shading)
st.image(frame, caption="Current Frame", channels="BGR")
st.write(alert_message)
# Display alerts or other relevant info
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