app.py

import streamlit as st
import torch
from transformers import DetrImageProcessor, DetrForObjectDetection
import cv2
import numpy as np
import tempfile
import os
import asyncio
from concurrent.futures import ThreadPoolExecutor
import warnings
from transformers.utils import logging

# 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 video (MP4) to detect thermal, dust, and power generation faults.")

# UI controls for optimization parameters
st.sidebar.header("Analysis Settings")
frame_skip = st.sidebar.slider("Frame Skip (higher = faster, less thorough)", min_value=1, max_value=50, value=30)
batch_size = st.sidebar.slider("Batch Size (adjust for hardware)", min_value=1, max_value=32, value=16 if torch.cuda.is_available() else 8)
resize_enabled = st.sidebar.checkbox("Resize Frames (faster processing)", value=True)
resize_width = 512 if resize_enabled else None
quantize_model = st.sidebar.checkbox("Quantize Model (faster, esp. on CPU)", value=True)

# Load model and processor
@st.cache_resource
def load_model(quantize=quantize_model):
    warnings.filterwarnings("ignore", message="Some weights of the model checkpoint.*were not used")
    logging.set_verbosity_error()
    
    try:
        processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
        model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model.to(device)
        
        # Apply dynamic quantization if enabled
        if quantize and device.type == "cpu":
            model = torch.quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
        
        model.eval()
        return processor, model, device
    except Exception as e:
        st.error(f"Failed to load model: {str(e)}. Check internet connection or cache (~/.cache/huggingface/hub).")
        raise

processor, model, device = load_model()

# Function to resize frame
def resize_frame(frame, width=None):
    if width is None:
        return frame
    aspect_ratio = frame.shape[1] / frame.shape[0]
    height = int(width / aspect_ratio)
    return cv2.resize(frame, (width, height), interpolation=cv2.INTER_LINEAR)

# Function to process a batch of frames
async def detect_faults_batch(frames, processor, model, device):
    try:
        frames = [resize_frame(frame, resize_width) for frame in frames]
        inputs = processor(images=frames, return_tensors="pt").to(device)
        with torch.no_grad():
            outputs = model(**inputs)
        target_sizes = torch.tensor([frame.shape[:2] for frame in frames]).to(device)
        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)
        
        annotated_frames = []
        all_faults = []
        
        for frame, result in zip(frames, results):
            faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
            annotated_frame = frame.copy()
            
            for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
                box = [int(i) for i in box.tolist()]
                roi = frame[box[1]:box[3], box[0]:box[2]]
                mean_intensity = np.mean(roi)
                
                if mean_intensity > 200:
                    faults["Thermal Fault"] = True
                    cv2.rectangle(annotated_frame, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
                    cv2.putText(annotated_frame, "Thermal Fault", (box[0], box[1]-10), 
                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
                elif mean_intensity < 100:
                    faults["Dust Fault"] = True
                    cv2.rectangle(annotated_frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
                    cv2.putText(annotated_frame, "Dust Fault", (box[0], box[1]-10), 
                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                
                if faults["Thermal Fault"] or faults["Dust Fault"]:
                    faults["Power Generation Fault"] = True
            
            annotated_frames.append(annotated_frame)
            all_faults.append(faults)
        
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        
        return annotated_frames, all_faults
    except Exception as e:
        st.error(f"Error during fault detection: {str(e)}")
        return [], []

# Function to process video
async def process_video(video_path, frame_skip, batch_size):
    try:
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            st.error("Error: Could not open video file.")
            return None, None
        
        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        out_width = resize_width if resize_width else frame_width
        out_height = int(out_width * frame_height / frame_width) if resize_width else frame_height
        
        output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        out = cv2.VideoWriter(output_path, fourcc, fps, (out_width, out_height))
        
        video_faults = {"Thermal Fault": False, "Dust Fault": False, "Power Generation Fault": False}
        frame_count = 0
        frames_batch = []
        processed_frames = 0
        
        with st.spinner("Analyzing video..."):
            progress = st.progress(0)
            executor = ThreadPoolExecutor(max_workers=2)
            
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                
                if frame_count % frame_skip != 0:
                    frame = resize_frame(frame, resize_width)
                    out.write(frame)
                    frame_count += 1
                    processed_frames += 1
                    progress.progress(min(processed_frames / total_frames, 1.0))
                    continue
                
                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frames_batch.append(frame_rgb)
                
                if len(frames_batch) >= batch_size:
                    annotated_frames, batch_faults = await detect_faults_batch(frames_batch, processor, model, device)
                    for annotated_frame, faults in zip(annotated_frames, batch_faults):
                        for fault in video_faults:
                            video_faults[fault] |= faults[fault]
                        annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
                        out.write(annotated_frame_bgr)
                    
                    frames_batch = []
                    processed_frames += batch_size
                    progress.progress(min(processed_frames / total_frames, 1.0))
                
                frame_count += 1
            
            if frames_batch:
                annotated_frames, batch_faults = await detect_faults_batch(frames_batch, processor, model, device)
                for annotated_frame, faults in zip(annotated_frames, batch_faults):
                    for fault in video_faults:
                        video_faults[fault] |= faults[fault]
                    annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
                    out.write(annotated_frame_bgr)
                
                processed_frames += len(frames_batch)
                progress.progress(min(processed_frames / total_frames, 1.0))
        
        cap.release()
        out.release()
        return output_path, video_faults
    
    except Exception as e:
        st.error(f"Error processing video: {str(e)}")
        return None, None
    finally:
        if 'cap' in locals() and cap.isOpened():
            cap.release()
        if 'out' in locals():
            out.release()

# File uploader
uploaded_file = st.file_uploader("Upload a thermal video", type=["mp4"])

if uploaded_file is not None:
    try:
        tfile = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
        tfile.write(uploaded_file.read())
        tfile.close()
        
        st.video(tfile.name, format="video/mp4")
        
        # Create a new event loop for Streamlit's ScriptRunner thread
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        try:
            output_path, video_faults = loop.run_until_complete(process_video(tfile.name, frame_skip, batch_size))
        finally:
            loop.close()
        
        if output_path and video_faults:
            st.subheader("Fault Detection Results")
            st.video(output_path, format="video/mp4")
            
            st.write("**Detected Faults in Video:**")
            for fault, detected in video_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)
            
            if any(video_faults.values()):
                st.subheader("Recommendations")
                if video_faults["Thermal Fault"]:
                    st.write("- **Thermal Fault**: Inspect for damaged components or overheating issues.")
                if video_faults["Dust Fault"]:
                    st.write("- **Dust Fault**: Schedule cleaning to remove dust accumulation.")
                if video_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.")
            
            if os.path.exists(output_path):
                os.unlink(output_path)
        
        if os.path.exists(tfile.name):
            os.unlink(tfile.name)
    
    except Exception as e:
        st.error(f"Error handling uploaded file: {str(e)}")
    finally:
        if 'tfile' in locals() and os.path.exists(tfile.name):
            os.unlink(tfile.name)

# Footer
st.markdown("---")
st.write("Built with Streamlit, Hugging Face Transformers, and OpenCV for Solar Panel Fault Detection PoC")