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| # Plant Species Classifier - Hugging Face Space | |
| # Developed with PyTorch + Gradio | |
| import gradio as gr | |
| import torch | |
| import torch.nn as nn | |
| from torchvision import models, transforms | |
| from PIL import Image | |
| import wikipediaapi | |
| import random | |
| from io import BytesIO | |
| import requests | |
| # Load model and mappings | |
| print("Loading model...") | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| checkpoint = torch.load('best_model.pt', map_location=device) | |
| num_classes = checkpoint['num_classes'] | |
| idx_to_label = checkpoint['idx_to_label'] | |
| # Build model | |
| model = models.resnet18(weights=None) | |
| model.fc = nn.Linear(model.fc.in_features, num_classes) | |
| model.load_state_dict(checkpoint['model_state_dict']) | |
| model = model.to(device) | |
| model.eval() | |
| # Transforms | |
| val_test_transform = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| # Wikipedia API | |
| wiki = wikipediaapi.Wikipedia( | |
| language='en', | |
| user_agent='PlantClassifier/1.0 (https://huggingface.co/spaces)' | |
| ) | |
| def get_species_info(species_name): | |
| try: | |
| page = wiki.page(species_name) | |
| if page.exists(): | |
| summary = page.summary[:500] | |
| if len(page.summary) > 500: | |
| summary += "..." | |
| return { | |
| 'title': page.title, | |
| 'summary': summary, | |
| 'url': page.fullurl, | |
| 'found': True | |
| } | |
| else: | |
| genus = species_name.split()[0] | |
| page = wiki.page(genus) | |
| if page.exists(): | |
| summary = page.summary[:500] | |
| if len(page.summary) > 500: | |
| summary += "..." | |
| return { | |
| 'title': f"{page.title} (genus)", | |
| 'summary': summary, | |
| 'url': page.fullurl, | |
| 'found': True | |
| } | |
| return { | |
| 'title': species_name, | |
| 'summary': f"No Wikipedia information found for {species_name}.", | |
| 'url': '', | |
| 'found': False | |
| } | |
| except Exception as e: | |
| return { | |
| 'title': species_name, | |
| 'summary': f"Error fetching information: {str(e)}", | |
| 'url': '', | |
| 'found': False | |
| } | |
| def predict_and_display(input_image, image_url): | |
| img = None | |
| if image_url and image_url.strip(): | |
| try: | |
| response = requests.get(image_url.strip(), timeout=10, | |
| headers={"User-Agent": "Mozilla/5.0"}) | |
| response.raise_for_status() | |
| img = Image.open(BytesIO(response.content)).convert('RGB') | |
| except Exception as e: | |
| return f"❌ Error downloading image from URL: {str(e)}", None, [] | |
| elif input_image is not None: | |
| img = input_image | |
| else: | |
| return "⚠️ Please upload an image or provide a URL", None, [] | |
| model.eval() | |
| img_tensor = val_test_transform(img).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| outputs = model(img_tensor) | |
| probs = torch.nn.functional.softmax(outputs, dim=1) | |
| topk_probs, topk_indices = probs.topk(5, dim=1) | |
| predictions = [] | |
| for prob, idx in zip(topk_probs[0], topk_indices[0]): | |
| label = idx_to_label[str(idx.item())] | |
| predictions.append((label, prob.item())) | |
| html = "<h2>🎯 Top 5 Predictions</h2>" | |
| html += "<div style='margin-top: 20px;'>" | |
| for i, (label, prob) in enumerate(predictions, 1): | |
| if prob > 0.5: | |
| color = "#2ecc71" | |
| elif prob > 0.2: | |
| color = "#f39c12" | |
| else: | |
| color = "#95a5a6" | |
| bar_width = int(prob * 100) | |
| html += f""" | |
| <div style='margin-bottom: 15px; padding: 15px; background: #f8f9fa; border-radius: 8px; border-left: 4px solid {color};'> | |
| <div style='display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;'> | |
| <span style='font-size: 18px; font-weight: bold; color: #2c3e50;'>#{i}</span> | |
| <span style='font-size: 18px; font-weight: 700; color: {color};'>{prob*100:.2f}%</span> | |
| </div> | |
| <div style='font-size: 16px; font-weight: 600; color: #2c3e50; margin-bottom: 10px; line-height: 1.4;'>{label}</div> | |
| <div style='width: 100%; background: #e0e0e0; border-radius: 10px; height: 10px;'> | |
| <div style='width: {bar_width}%; background: {color}; height: 10px; border-radius: 10px;'></div> | |
| </div> | |
| </div> | |
| """ | |
| html += "</div>" | |
| return html, img, predictions | |
| def show_species_details(predictions, selected_index): | |
| if not predictions or selected_index < 1 or selected_index > len(predictions): | |
| return "⚠️ Please make a prediction first" | |
| selected_species = predictions[int(selected_index) - 1][0] | |
| confidence = predictions[int(selected_index) - 1][1] | |
| wiki_info = get_species_info(selected_species) | |
| html = f""" | |
| <div style='padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| border-radius: 12px; color: white; margin-bottom: 20px;'> | |
| <h1 style='margin: 0 0 10px 0; font-size: 28px;'>{selected_species}</h1> | |
| <p style='margin: 0; font-size: 18px; opacity: 0.9;'>Confidence: {confidence*100:.2f}%</p> | |
| </div> | |
| <div style='background: #f8f9fa; padding: 20px; border-radius: 12px; margin-bottom: 20px;'> | |
| <h2 style='color: #2c3e50; margin-top: 0;'>📚 {wiki_info['title']}</h2> | |
| <p style='line-height: 1.6; color: #34495e; font-size: 15px;'>{wiki_info['summary']}</p> | |
| {f"<a href='{wiki_info['url']}' target='_blank' style='color: #3498db; text-decoration: none; font-weight: 600;'>📖 Read more on Wikipedia →</a>" if wiki_info['url'] else ""} | |
| </div> | |
| """ | |
| return html | |
| # Create Gradio Interface | |
| with gr.Blocks(title="🌿 Plant Species Classifier", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # 🌿 Plant Species Classifier | |
| ### AI-powered plant species identification from GBIF TOP100 dataset | |
| **How to use:** | |
| 1. Upload an image or paste an image URL | |
| 2. Click "🔍 Predict" to see top 5 species predictions | |
| 3. Click "📊 Show Details" to explore detailed information | |
| **Model:** ResNet18 trained on 100 plant species from GBIF | |
| """) | |
| predictions_state = gr.State([]) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 📤 Input") | |
| input_image = gr.Image( | |
| label="Upload Image", | |
| type="pil", | |
| height=300 | |
| ) | |
| gr.Markdown("**OR**") | |
| image_url = gr.Textbox( | |
| label="Image URL", | |
| placeholder="https://example.com/plant.jpg", | |
| lines=1 | |
| ) | |
| with gr.Row(): | |
| predict_btn = gr.Button("🔍 Predict", variant="primary", size="lg") | |
| clear_btn = gr.ClearButton([input_image, image_url], value="🗑️ Clear") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 🎯 Predictions") | |
| predictions_display = gr.HTML( | |
| "<p style='text-align: center; color: #95a5a6; padding: 50px;'>Predictions will appear here...</p>" | |
| ) | |
| gr.Markdown("---") | |
| gr.Markdown("### 🔬 Species Details") | |
| gr.Markdown("Select a prediction number (1-5) to see detailed information:") | |
| with gr.Row(): | |
| species_selector = gr.Slider( | |
| minimum=1, | |
| maximum=5, | |
| step=1, | |
| value=1, | |
| label="Select Prediction #", | |
| info="Choose which prediction to explore" | |
| ) | |
| details_btn = gr.Button("📊 Show Details", variant="secondary", size="lg") | |
| species_info = gr.HTML() | |
| def predict_wrapper(img, url): | |
| html, display_img, preds = predict_and_display(img, url) | |
| return html, preds | |
| predict_btn.click( | |
| fn=predict_wrapper, | |
| inputs=[input_image, image_url], | |
| outputs=[predictions_display, predictions_state] | |
| ) | |
| details_btn.click( | |
| fn=show_species_details, | |
| inputs=[predictions_state, species_selector], | |
| outputs=[species_info] | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| <div style='text-align: center; color: #95a5a6;'> | |
| <p>🌱 Built with PyTorch + Gradio | Data from GBIF</p> | |
| </div> | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() | |