Update app.py

app.py

@@ -1,37 +1,78 @@
 import streamlit as st
-import torch
-import torchvision.transforms as transforms
-from PIL import Image
+import cv2
+import requests
+from transformers import pipeline
+from ultralytics import YOLO
 import numpy as np
+from io import BytesIO
 
-# Load your pre-trained model
-model = torch.load('model/your_model_file.pt')
-model.eval()
+# Initialize the object detection model
+object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
+thermal_model = YOLO("thermal_model.pt")
 
-# Define image transformations
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                         std=[0.229, 0.224, 0.225])
-])
+def detect_intrusion(image):
+    detections = object_detector(image)
+    return [d for d in detections if d['score'] > 0.7]
 
-st.title("VIEP: Utility Pole Fault Detection")
+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
 
-uploaded_file = st.file_uploader("Upload an image of a utility pole", type=["jpg", "jpeg", "png"])
+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
 
-if uploaded_file is not None:
-    image = Image.open(uploaded_file).convert('RGB')
-    st.image(image, caption='Uploaded Image', use_column_width=True)
+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)
 
-    # Preprocess the image
-    input_tensor = transform(image).unsqueeze(0)
+    return detections, thermal_anomalies, shading
 
-    # Perform inference
-    with torch.no_grad():
-        output = model(input_tensor)
-        _, predicted = torch.max(output, 1)
+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!"
 
-    # Map the prediction to class names
-    classes = ['No Fault', 'Fault Detected']
-    st.write(f"Prediction: {classes[predicted.item()]}")
+    # 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