Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,112 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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.")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Footer
+st.markdown("---")
+st.write("Built with Streamlit and Hugging Face Transformers for Solar Panel Fault Detection PoC")