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| #!/usr/bin/env python3 | |
| """ | |
| ACCEPTIN - Telecom Site Quality Classification App | |
| AI-powered telecom site inspection using ConvNeXt transfer learning | |
| """ | |
| import streamlit as st | |
| import torch | |
| import torch.nn.functional as F | |
| from PIL import Image | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| from plotly.subplots import make_subplots | |
| import pandas as pd | |
| import sys | |
| import os | |
| import time | |
| from io import BytesIO | |
| import base64 | |
| # Add utils to path | |
| from utils.model_utils import load_model, TelecomClassifier | |
| from utils.data_utils import get_inference_transform, prepare_image_for_inference, check_data_directory | |
| # Page Configuration | |
| st.set_page_config( | |
| page_title="📡 ACCEPTIN - Telecom Site Inspector", | |
| page_icon="📡", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Custom CSS for Beautiful UI | |
| st.markdown(""" | |
| <style> | |
| .main-header { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| padding: 2rem; | |
| border-radius: 15px; | |
| text-align: center; | |
| color: white; | |
| margin-bottom: 2rem; | |
| box-shadow: 0 8px 32px rgba(0,0,0,0.1); | |
| } | |
| .main-header h1 { | |
| font-size: 3rem; | |
| margin: 0; | |
| font-weight: bold; | |
| text-shadow: 2px 2px 4px rgba(0,0,0,0.3); | |
| } | |
| .main-header p { | |
| font-size: 1.2rem; | |
| margin: 0.5rem 0 0 0; | |
| opacity: 0.9; | |
| } | |
| .upload-section { | |
| background: linear-gradient(135deg, #56ab2f 0%, #a8e6cf 100%); | |
| color: white; | |
| padding: 20px; | |
| border-radius: 15px; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| box-shadow: 0 6px 20px rgba(86, 171, 47, 0.3); | |
| } | |
| .result-good { | |
| background: linear-gradient(135deg, #28a745 0%, #20c997 100%); | |
| color: white; | |
| padding: 20px; | |
| border-radius: 15px; | |
| text-align: center; | |
| margin: 20px 0; | |
| box-shadow: 0 6px 20px rgba(40, 167, 69, 0.3); | |
| } | |
| .result-bad { | |
| background: linear-gradient(135deg, #dc3545 0%, #e83e8c 100%); | |
| color: white; | |
| padding: 20px; | |
| border-radius: 15px; | |
| text-align: center; | |
| margin: 20px 0; | |
| box-shadow: 0 6px 20px rgba(220, 53, 69, 0.3); | |
| } | |
| .metric-card { | |
| background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); | |
| padding: 15px; | |
| border-radius: 10px; | |
| text-align: center; | |
| color: white; | |
| margin: 10px 0; | |
| box-shadow: 0 4px 15px rgba(0,0,0,0.2); | |
| } | |
| .info-card { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| padding: 20px; | |
| border-radius: 15px; | |
| margin: 15px 0; | |
| box-shadow: 0 8px 32px rgba(0,0,0,0.1); | |
| } | |
| .stButton > button { | |
| background: linear-gradient(45deg, #667eea, #764ba2); | |
| color: white; | |
| border: none; | |
| border-radius: 10px; | |
| padding: 12px 24px; | |
| font-weight: bold; | |
| font-size: 1.1rem; | |
| transition: all 0.3s ease; | |
| box-shadow: 0 4px 15px rgba(0,0,0,0.2); | |
| width: 100%; | |
| } | |
| .stButton > button:hover { | |
| transform: translateY(-2px); | |
| box-shadow: 0 6px 20px rgba(0,0,0,0.3); | |
| } | |
| .sidebar .stSelectbox > div > div { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def load_telecom_model(): | |
| """Load the telecom classification model""" | |
| model_path = 'models/telecom_classifier.pth' | |
| if not os.path.exists(model_path): | |
| return None, "Model not found. Please ensure the model is in the models/ directory." | |
| try: | |
| model, model_info = load_model(model_path, device='cpu') | |
| return model, model_info | |
| except Exception as e: | |
| return None, f"Error loading model: {str(e)}" | |
| def get_prediction(image, model, transform, confidence_threshold=0.70): | |
| """Get prediction from the model with confidence threshold""" | |
| try: | |
| # Prepare image | |
| input_tensor = prepare_image_for_inference(image, transform) | |
| # Get prediction | |
| with torch.no_grad(): | |
| model.eval() | |
| outputs = model(input_tensor) | |
| probabilities = F.softmax(outputs, dim=1) | |
| confidence, predicted = torch.max(probabilities, 1) | |
| # Convert to numpy | |
| predicted_class = predicted.item() | |
| confidence_score = confidence.item() | |
| all_probs = probabilities.squeeze().cpu().numpy() | |
| # Check confidence threshold | |
| if confidence_score < confidence_threshold: | |
| return None, confidence_score, all_probs # Return confidence for display | |
| return predicted_class, confidence_score, all_probs | |
| except Exception as e: | |
| st.error(f"Error during prediction: {str(e)}") | |
| return None, None, None | |
| def create_confidence_chart(probabilities, class_names): | |
| """Create confidence chart using Plotly""" | |
| fig = go.Figure(data=[ | |
| go.Bar( | |
| x=class_names, | |
| y=probabilities, | |
| marker_color=['#dc3545', '#28a745'], | |
| text=[f'{p:.1%}' for p in probabilities], | |
| textposition='auto', | |
| ) | |
| ]) | |
| fig.update_layout( | |
| title="Classification Confidence", | |
| xaxis_title="Site Quality", | |
| yaxis_title="Confidence", | |
| yaxis=dict(range=[0, 1]), | |
| showlegend=False, | |
| height=400, | |
| template="plotly_white" | |
| ) | |
| return fig | |
| def create_quality_metrics_chart(predicted_class, confidence): | |
| """Create quality metrics visualization""" | |
| if predicted_class == 1: # Good | |
| quality_score = confidence * 100 | |
| color = '#28a745' | |
| status = 'ACCEPTED' | |
| else: # Bad | |
| quality_score = (1 - confidence) * 100 | |
| color = '#dc3545' | |
| status = 'REJECTED' | |
| fig = go.Figure(go.Indicator( | |
| mode="gauge+number+delta", | |
| value=quality_score, | |
| domain={'x': [0, 1], 'y': [0, 1]}, | |
| title={'text': f"Quality Score<br><span style='font-size:0.8em;color:{color}'>{status}</span>"}, | |
| delta={'reference': 80}, | |
| gauge={ | |
| 'axis': {'range': [None, 100]}, | |
| 'bar': {'color': color}, | |
| 'steps': [ | |
| {'range': [0, 50], 'color': "lightgray"}, | |
| {'range': [50, 80], 'color': "yellow"}, | |
| {'range': [80, 100], 'color': "lightgreen"} | |
| ], | |
| 'threshold': { | |
| 'line': {'color': "red", 'width': 4}, | |
| 'thickness': 0.75, | |
| 'value': 90 | |
| } | |
| } | |
| )) | |
| fig.update_layout(height=400) | |
| return fig | |
| def analyze_site_quality(predicted_class, confidence): | |
| """Analyze site quality and provide detailed feedback""" | |
| class_names = ['Bad', 'Good'] | |
| predicted_label = class_names[predicted_class] | |
| if predicted_class == 1: # Good site | |
| analysis = { | |
| 'status': 'ACCEPTED ✅', | |
| 'color': 'result-good', | |
| 'icon': '✅', | |
| 'message': 'Site installation meets quality standards', | |
| 'details': [ | |
| '✅ Cable assembly appears properly organized', | |
| '✅ Equipment installation looks correct', | |
| '✅ Overall site organization is acceptable', | |
| '✅ No obvious safety violations detected' | |
| ], | |
| 'recommendations': [ | |
| '🔍 Verify all labels are clearly readable', | |
| '🔧 Double-check all card installations', | |
| '📋 Complete final inspection checklist', | |
| '📸 Document final installation state' | |
| ] | |
| } | |
| else: # Bad site | |
| analysis = { | |
| 'status': 'REJECTED ❌', | |
| 'color': 'result-bad', | |
| 'icon': '❌', | |
| 'message': 'Site installation requires attention', | |
| 'details': [ | |
| '❌ Cable organization may need improvement', | |
| '❌ Equipment installation issues detected', | |
| '❌ Site organization below standards', | |
| '❌ Potential safety or quality concerns' | |
| ], | |
| 'recommendations': [ | |
| '🔧 Reorganize cable routing and bundling', | |
| '🔍 Check all card installations and seating', | |
| '🏷️ Verify all labels are present and readable', | |
| '⚠️ Address any safety violations', | |
| '📋 Complete corrective actions before acceptance' | |
| ] | |
| } | |
| analysis['confidence'] = confidence | |
| analysis['predicted_label'] = predicted_label | |
| return analysis | |
| def display_inspection_checklist(): | |
| """Display telecom site inspection checklist""" | |
| st.markdown(""" | |
| <div class="info-card"> | |
| <h3>📋 Telecom Site Inspection Checklist</h3> | |
| <p>Use this checklist to ensure comprehensive site evaluation:</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| checklist_items = { | |
| "Card Installation": [ | |
| "All required cards present", | |
| "Cards properly seated and secured", | |
| "No missing or damaged cards", | |
| "Card configurations correct" | |
| ], | |
| "Labeling": [ | |
| "All equipment properly labeled", | |
| "Labels clearly readable", | |
| "Label placement follows standards", | |
| "No missing identification tags" | |
| ], | |
| "Safety & Organization": [ | |
| "Safety covers properly installed", | |
| "Grounding connections secure", | |
| "Warning signs present where required", | |
| "Overall rack organization acceptable" | |
| ] | |
| } | |
| # Create three columns for better organization | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.subheader("🔍 Card Installation") | |
| for item in checklist_items["Card Installation"]: | |
| st.write(f"• {item}") | |
| with col2: | |
| st.subheader("🔍 Labeling") | |
| for item in checklist_items["Labeling"]: | |
| st.write(f"• {item}") | |
| with col3: | |
| st.subheader("🔍 Safety & Organization") | |
| for item in checklist_items["Safety & Organization"]: | |
| st.write(f"• {item}") | |
| def main(): | |
| """Main application function""" | |
| # Custom CSS for beautiful gradient colors | |
| st.markdown(""" | |
| <style> | |
| /* Beautiful gradient background */ | |
| .stApp { | |
| background: linear-gradient(135deg, #f8f9fa 0%, #e3f2fd 25%, #f3e5f5 50%, #e8f5e8 75%, #fff3e0 100%); | |
| background-attachment: fixed; | |
| } | |
| /* Header styling with gradient */ | |
| .main-header { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| padding: 2rem; | |
| border-radius: 15px; | |
| margin-bottom: 2rem; | |
| box-shadow: 0 8px 32px rgba(102, 126, 234, 0.2); | |
| text-align: center; | |
| border: 1px solid rgba(255, 255, 255, 0.2); | |
| } | |
| .main-header h1 { | |
| color: white; | |
| font-size: 3rem; | |
| margin-bottom: 0.5rem; | |
| text-shadow: 2px 2px 4px rgba(0,0,0,0.3); | |
| font-weight: 700; | |
| } | |
| .main-header p { | |
| color: rgba(255, 255, 255, 0.9); | |
| font-size: 1.2rem; | |
| margin: 0; | |
| font-weight: 300; | |
| } | |
| /* Sidebar styling */ | |
| .css-1d391kg { | |
| background: linear-gradient(180deg, #f8f9fa 0%, #e3f2fd 100%); | |
| border-right: 1px solid rgba(102, 126, 234, 0.1); | |
| } | |
| /* Card styling */ | |
| .stCard { | |
| background: linear-gradient(135deg, rgba(255,255,255,0.95) 0%, rgba(248,249,250,0.9) 100%); | |
| border-radius: 15px; | |
| padding: 1.5rem; | |
| margin: 1rem 0; | |
| box-shadow: 0 4px 20px rgba(102, 126, 234, 0.1); | |
| border: 1px solid rgba(102, 126, 234, 0.1); | |
| backdrop-filter: blur(10px); | |
| } | |
| /* Upload section styling */ | |
| .upload-section { | |
| background: linear-gradient(135deg, rgba(227, 242, 253, 0.4) 0%, rgba(243, 229, 245, 0.4) 100%); | |
| padding: 1.5rem; | |
| border-radius: 12px; | |
| border: 1px solid rgba(102, 126, 234, 0.2); | |
| box-shadow: 0 2px 10px rgba(102, 126, 234, 0.05); | |
| } | |
| /* Success message styling */ | |
| .success-message { | |
| background: linear-gradient(135deg, #4caf50 0%, #66bb6a 100%); | |
| color: white; | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| border: none; | |
| box-shadow: 0 2px 8px rgba(76, 175, 80, 0.3); | |
| } | |
| /* Warning message styling */ | |
| .warning-message { | |
| background: linear-gradient(135deg, #ff9800 0%, #ffb74d 100%); | |
| color: white; | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| border: none; | |
| box-shadow: 0 2px 8px rgba(255, 152, 0, 0.3); | |
| } | |
| /* Error message styling */ | |
| .error-message { | |
| background: linear-gradient(135deg, #f44336 0%, #ef5350 100%); | |
| color: white; | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| border: none; | |
| box-shadow: 0 2px 8px rgba(244, 67, 54, 0.3); | |
| } | |
| /* Button styling */ | |
| .stButton > button { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| border: none; | |
| border-radius: 8px; | |
| padding: 0.75rem 1.5rem; | |
| font-weight: 600; | |
| transition: all 0.3s ease; | |
| box-shadow: 0 2px 8px rgba(102, 126, 234, 0.3); | |
| } | |
| .stButton > button:hover { | |
| background: linear-gradient(135deg, #764ba2 0%, #667eea 100%); | |
| transform: translateY(-2px); | |
| box-shadow: 0 4px 16px rgba(102, 126, 234, 0.4); | |
| } | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| background: linear-gradient(135deg, rgba(248, 249, 250, 0.8) 0%, rgba(227, 242, 253, 0.8) 100%); | |
| border-radius: 10px; | |
| border: 1px solid rgba(102, 126, 234, 0.1); | |
| } | |
| /* Chart container styling */ | |
| .stPlotlyChart { | |
| background: rgba(255,255,255,0.95); | |
| border-radius: 12px; | |
| padding: 1.5rem; | |
| margin: 1rem 0; | |
| border: 1px solid rgba(102, 126, 234, 0.1); | |
| box-shadow: 0 4px 16px rgba(102, 126, 234, 0.08); | |
| } | |
| /* Column alignment fix */ | |
| .row-widget.stHorizontal { | |
| align-items: flex-start !important; | |
| } | |
| /* Perfect horizontal alignment for main content boxes */ | |
| .main-content-row { | |
| display: flex; | |
| align-items: stretch; | |
| justify-content: space-between; | |
| width: 100%; | |
| gap: 1rem; | |
| } | |
| /* Ensure equal height and perfect alignment */ | |
| .stColumn { | |
| display: flex; | |
| flex-direction: column; | |
| align-items: stretch; | |
| flex: 1; | |
| } | |
| /* Force same height for content boxes */ | |
| .upload-section, .status-box { | |
| min-height: 250px; | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: flex-start; | |
| height: 100%; | |
| } | |
| /* Ensure both columns are exactly the same height */ | |
| .stHorizontal > div { | |
| height: 100% !important; | |
| display: flex !important; | |
| flex-direction: column !important; | |
| } | |
| /* Perfect alignment for all content sections */ | |
| .stMarkdown, .stImage, .stWarning, .stSuccess { | |
| margin-top: 0 !important; | |
| margin-bottom: 0 !important; | |
| } | |
| /* Ensure consistent spacing */ | |
| .stColumn > div { | |
| padding-top: 0 !important; | |
| padding-bottom: 0 !important; | |
| } | |
| /* Force exact alignment */ | |
| .main-content-row .stColumn { | |
| align-items: stretch !important; | |
| justify-content: flex-start !important; | |
| } | |
| /* Remove repetitive status box */ | |
| .status-box { | |
| display: none !important; | |
| } | |
| /* Text styling for better readability */ | |
| .stMarkdown { | |
| color: #2c3e50; | |
| line-height: 1.6; | |
| } | |
| /* Selectbox styling */ | |
| .stSelectbox > div > div { | |
| background: rgba(255, 255, 255, 0.9); | |
| border: 1px solid rgba(102, 126, 234, 0.2); | |
| border-radius: 8px; | |
| } | |
| /* File uploader styling */ | |
| .stFileUploader > div { | |
| background: rgba(255, 255, 255, 0.9); | |
| border: 2px dashed rgba(102, 126, 234, 0.3); | |
| border-radius: 10px; | |
| padding: 1rem; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Header | |
| st.markdown(""" | |
| <div class="main-header"> | |
| <h1>📡 ACCEPTIN</h1> | |
| <p>AI-Powered Telecom Data Center Site Quality Inspector</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Sidebar | |
| st.sidebar.title("🛠️ Controls") | |
| # Load model | |
| model, model_info = load_telecom_model() | |
| if model is None: | |
| st.error(f"❌ {model_info}") | |
| st.info("Please train the model first using: `python train_telecom.py`") | |
| return | |
| # Model info in sidebar | |
| st.sidebar.success("✅ Model loaded successfully") | |
| if isinstance(model_info, dict): | |
| accuracy = model_info.get('best_acc', 'Unknown') | |
| if isinstance(accuracy, (int, float)): | |
| st.sidebar.write(f"**Accuracy:** {accuracy:.2f}%") | |
| else: | |
| st.sidebar.write(f"**Accuracy:** {accuracy}") | |
| st.sidebar.write(f"**Architecture:** ConvNeXt 197M Parameters") | |
| # Add concise About text below Architecture | |
| st.sidebar.markdown("---") | |
| st.sidebar.markdown("**About ACCEPTIN:**") | |
| st.sidebar.markdown("AI-powered telecom site quality inspector using computer vision to classify installations as good/bad with confidence scores.") | |
| # Main content - 2 equal columns with perfect alignment | |
| st.markdown('<div class="main-content-row">', unsafe_allow_html=True) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown(""" | |
| <div class="upload-section"> | |
| <h3 style='color: #006400;'>📤 Upload or Capture Telecom/IT Datacenter Image</h3> | |
| <p style="color: #000000; font-weight: 500;">Upload an image or take a photo of the telecom/IT datacenter for quality inspection</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # File upload option | |
| uploaded_file = st.file_uploader( | |
| "Choose an image...", | |
| type=['jpg', 'jpeg', 'png', 'bmp', 'tiff'], | |
| help="Upload a clear image of the telecom/IT datacenter installation" | |
| ) | |
| # Camera option (hidden but functional) | |
| st.markdown(""" | |
| <style> | |
| [data-testid="stCameraInput"] { | |
| display: none !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| camera_photo = st.camera_input("Or take a photo with camera") | |
| image = None | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| elif camera_photo is not None: | |
| image = Image.open(camera_photo) | |
| if image is not None: | |
| # Create two columns for image and warning/status | |
| img_col, msg_col = st.columns([2, 1]) | |
| with img_col: | |
| # Display uploaded or captured image | |
| st.image(image, caption="Telecom Site Image", use_container_width=True) | |
| with msg_col: | |
| with st.spinner("Analyzing site quality..."): | |
| # Get prediction | |
| transform = get_inference_transform() | |
| predicted_class, confidence, probabilities = get_prediction( | |
| image, model, transform | |
| ) | |
| if predicted_class is not None: | |
| class_names = ['Bad', 'Good', 'Non Telecom'] | |
| predicted_label = class_names[predicted_class] | |
| if predicted_label == 'Non Telecom': | |
| st.warning("⚠️ This image does not appear to be a telecom site. Please upload a valid telecom site photo.") | |
| st.session_state.prediction_results = None | |
| else: | |
| # Store results in session state | |
| st.session_state.prediction_results = { | |
| 'predicted_class': predicted_class, | |
| 'confidence': confidence, | |
| 'probabilities': probabilities, | |
| 'analysis': analyze_site_quality(predicted_class, confidence) | |
| } | |
| elif confidence is not None and confidence < 0.70: | |
| st.warning(f"⚠️ Low confidence prediction ({confidence:.1%}). Please upload a higher quality image with better lighting and clearer focus for more accurate analysis.") | |
| st.session_state.prediction_results = None | |
| # Move "AI Analysis complete!" message below the image | |
| if hasattr(st.session_state, 'prediction_results') and st.session_state.prediction_results is not None: | |
| st.success("✅ AI Analysis complete!") | |
| with col2: | |
| if hasattr(st.session_state, 'prediction_results') and st.session_state.prediction_results is not None: | |
| results = st.session_state.prediction_results | |
| analysis = results['analysis'] | |
| # Display status box in middle column | |
| st.markdown(f""" | |
| <div class="{analysis['color']}"> | |
| <h2>{analysis['icon']} {analysis['status']}</h2> | |
| <h3>{analysis['message']}</h3> | |
| <p><strong>Confidence:</strong> {analysis['confidence']:.1%}</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Detailed analysis section - moved back to below the green box | |
| if hasattr(st.session_state, 'prediction_results') and st.session_state.prediction_results is not None: | |
| with col2: | |
| st.markdown("---") | |
| st.markdown(""" | |
| <div style="text-align: center;"> | |
| <h2>📊 Detailed Analysis</h2> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| analysis = st.session_state.prediction_results['analysis'] | |
| # Create two columns for Quality Assessment and Recommendations | |
| qa_col, rec_col = st.columns(2) | |
| with qa_col: | |
| st.markdown(""" | |
| <div style="text-align: left;"> | |
| <h3>🔍 Quality Assessment</h3> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| for detail in analysis['details']: | |
| st.write(detail) | |
| with rec_col: | |
| st.markdown(""" | |
| <div style="text-align: left;"> | |
| <h3>💡 Recommendations</h3> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| for recommendation in analysis['recommendations']: | |
| st.write(recommendation) | |
| # Tabs for additional features | |
| st.markdown("---") | |
| tab1, tab2 = st.tabs(["📋 Inspection Checklist", "ℹ️ About"]) | |
| with tab1: | |
| display_inspection_checklist() | |
| with tab2: | |
| st.header("ℹ️ About ACCEPTIN") | |
| st.markdown(""" | |
| **ACCEPTIN** is an AI-powered telecom site quality inspection system that uses computer vision | |
| to automatically classify telecom installations as "good" or "bad" based on visual criteria. | |
| ### 🎯 Key Features: | |
| - **Transfer Learning**: Leverages pre-trained ConvNeXt model (197M parameters) | |
| - **Binary Classification**: Classifies sites as good/bad with confidence scores | |
| - **Quality Assessment**: Evaluates cable assembly, card installation, and labeling | |
| - **Real-time Analysis**: Instant feedback on site quality | |
| ### 🔧 Technical Details: | |
| - **Model**: ConvNeXt Large with custom classification head | |
| - **Training**: Transfer learning from food detection model | |
| - **Input**: 224x224 RGB images | |
| - **Output**: Binary classification with confidence scores | |
| ### 📊 Quality Criteria: | |
| - Cable assembly and routing | |
| - Card installation and labeling | |
| - General organization and safety | |
| - Compliance with telecom standards | |
| ### 🚀 Usage: | |
| 1. Upload telecom site image | |
| 2. Click "Analyze Site Quality" | |
| 3. Review results and recommendations | |
| 4. Use inspection checklist for verification | |
| """) | |
| if __name__ == "__main__": | |
| main() |