Delete src

src/streamlit_app.py

@@ -1,112 +0,0 @@
-import streamlit as st
-import torch
-from transformers import DetrImageProcessor, DetrForObjectDetection
-from PIL import Image
-import numpy as np
-import cv2
-
-# Set page configuration
-st.set_page_config(page_title="Solar Panel Fault Detection", layout="wide")
-
-# Title and description
-st.title("Solar Panel Fault Detection PoC")
-st.write("Upload a thermal image of a solar panel to detect thermal, dust, and power generation faults.")
-
-# Load model and processor
-@st.cache_resource
-def load_model():
-    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
-    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
-    return processor, model
-
-processor, model = load_model()
-
-# Function to process image and detect faults
-def detect_faults(image):
-    # Convert PIL image to numpy array
-    img_np = np.array(image)
-    
-    # Convert to RGB if necessary
-    if img_np.shape[-1] == 4:
-        img_np = img_np[:, :, :3]
-    
-    # Prepare image for model
-    inputs = processor(images=img_np, return_tensors="pt")
-    
-    # Run inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-    
-    # Post-process outputs
-    target_sizes = torch.tensor([img_np.shape[:2]])
-    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
-    
-    # Initialize fault detection
-    faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
-    annotated_img = img_np.copy()
-    
-    # Analyze thermal image for faults
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        box = [int(i) for i in box.tolist()]
-        # Simulate fault detection based on bounding box and pixel intensity
-        roi = img_np[box[1]:box[3], box[0]:box[2]]
-        mean_intensity = np.mean(roi)
-        
-        # Thermal Fault: High intensity (hotspot)
-        if mean_intensity > 200:  # Adjust threshold based on thermal image scale
-            faults["Thermal Fault"] = True
-            cv2.rectangle(annotated_img, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
-            cv2.putText(annotated_img, "Thermal Fault", (box[0], box[1]-10), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-        
-        # Dust Fault: Low intensity or irregular patterns
-        elif mean_intensity < 100:  # Adjust threshold
-            faults["Dust Fault"] = True
-            cv2.rectangle(annotated_img, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
-            cv2.putText(annotated_img, "Dust Fault", (box[0], box[1]-10), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-        
-        # Power Generation Fault: Any detected anomaly may indicate reduced efficiency
-        if faults["Thermal Fault"] or faults["Dust Fault"]:
-            faults["Power Generation Fault"] = True
-    
-    return annotated_img, faults
-
-# File uploader
-uploaded_file = st.file_uploader("Upload a thermal image", type=["png", "jpg", "jpeg"])
-
-if uploaded_file is not None:
-    # Load and display uploaded image
-    image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption="Uploaded Thermal Image", use_column_width=True)
-    
-    # Process image
-    with st.spinner("Analyzing image..."):
-        annotated_img, faults = detect_faults(image)
-    
-    # Display results
-    st.subheader("Fault Detection Results")
-    st.image(annotated_img, caption="Annotated Image with Detected Faults", use_column_width=True)
-    
-    # Show fault summary
-    st.write("**Detected Faults:**")
-    for fault, detected in faults.items():
-        status = "Detected" if detected else "Not Detected"
-        color = "red" if detected else "green"
-        st.markdown(f"- **{fault}**: <span style='color:{color}'>{status}</span>", unsafe_allow_html=True)
-    
-    # Provide recommendations
-    if any(faults.values()):
-        st.subheader("Recommendations")
-        if faults["Thermal Fault"]:
-            st.write("- **Thermal Fault**: Inspect for damaged components or overheating issues.")
-        if faults["Dust Fault"]:
-            st.write("- **Dust Fault**: Schedule cleaning to remove dust accumulation.")
-        if faults["Power Generation Fault"]:
-            st.write("- **Power Generation Fault**: Investigate efficiency issues due to detected faults.")
-    else:
-        st.write("No faults detected. The solar panel appears to be functioning normally.")
-
-# Footer
-st.markdown("---")
-st.write("Built with Streamlit and Hugging Face Transformers for Solar Panel Fault Detection PoC")