Update app.py

app.py

@@ -62,185 +62,4 @@ def resize_frame(frame, width=None):
 # 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))
-                GROUP BY 
-                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")
+        frames = [resize_frame(frame, resize