Package model repo: add README/inference, remove Space app files

HF_SPACE_APP.py

@@ -1,203 +0,0 @@
-"""
-Hugging Face Space - Waste Classification Demo
-Fast API-only inference, no local model loading.
-"""
-
-import gradio as gr
-import os
-import requests
-import io
-from PIL import Image
-
-# Configuration
-MODEL_ID = "Ayushman0502/waste-classifier"
-HF_TOKEN = os.getenv("HF_TOKEN", "")
-API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
-
-# Class labels
-CLASS_DISPLAY = {
-    'dry_waste': '♻️ Dry Waste',
-    'other_waste': '🔶 Other Waste',
-    'wet_waste': '🍃 Wet Waste',
-}
-CLASS_ACTIONS = {
-    'dry_waste': 'Can be recycled → Paper, plastic, metal recovery',
-    'other_waste': 'Needs special handling → Hazardous / e-waste processing',
-    'wet_waste': 'Compostable → Organic composting / biogas generation',
-}
-
-print("✅ Waste Classifier Space Ready")
-
-
-def predict_waste(image):
-    """Classify waste via Hugging Face Inference API."""
-    try:
-        # Convert image to bytes
-        img_bytes = io.BytesIO()
-        image.save(img_bytes, format='JPEG')
-        img_bytes.seek(0)
-        
-        # Call API
-        headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
-        response = requests.post(
-            API_URL,
-            headers=headers,
-            data=img_bytes.getvalue(),
-            timeout=20
-        )
-        
-        if response.status_code != 200:
-            msg = response.text if response.text else f"Status {response.status_code}"
-            if "loading" in msg.lower():
-                return "⏳ Model loading (first use). Retry in 30s.", None
-            return f"❌ Error: {msg[:100]}", None
-        
-        # Parse response
-        preds = response.json()
-        if not isinstance(preds, list):
-            return f"❌ Unexpected response format", None
-        
-        # Extract best prediction
-        best = max(preds, key=lambda x: x.get('score', 0)) if preds else None
-        if not best:
-            return "❌ No predictions", None
-        
-        class_name = best['label'].lower()
-        score = best['score'] * 100
-        
-        # Format output
-        text = f"**{CLASS_DISPLAY.get(class_name, class_name)}**\n"
-        text += f"Confidence: **{score:.1f}%**\n"
-        text += f"Action: {CLASS_ACTIONS.get(class_name, 'N/A')}\n\n"
-        text += "**All Scores:**\n"
-        
-        results = {}
-        for p in preds:
-            label = p['label'].lower()
-            perc = p['score'] * 100
-            results[CLASS_DISPLAY.get(label, label)] = round(perc, 1)
-            text += f"{CLASS_DISPLAY.get(label, label)}: {perc:.1f}%\n"
-        
-        return text, results
-        
-    except requests.exceptions.Timeout:
-        return "⏳ API timeout. Retry in 30s.", None
-    except Exception as e:
-        return f"❌ Error: {str(e)[:50]}", None
-
-
-# Create custom HTML for better styling
-HEADER_HTML = """
-<div style="text-align: center; margin-bottom: 30px;">
-    <h1 style="color: #2c3e50; margin-bottom: 10px;">🌿 Waste Classification AI</h1>
-    <p style="color: #7f8c8d; font-size: 16px;">
-        Upload an image of waste to classify it as Dry, Wet, or Other waste
-    </p>
-    <p style="color: #95a5a6; font-size: 14px;">
-        Powered by EfficientNetB0 • Supporting Circular Economy
-    </p>
-</div>
-"""
-
-FOOTER_HTML = """
-<div style="text-align: center; margin-top: 30px; padding-top: 20px; border-top: 1px solid #ecf0f1;">
-    <p style="color: #7f8c8d; font-size: 13px;">
-        🔗 <a href="https://huggingface.co/Ayushman0502/waste-classifier" target="_blank">View Model Card</a> • 
-        <a href="https://github.com/ayushmansingh0502-hub/ML_MODEL" target="_blank">GitHub Repository</a>
-    </p>
-    <p style="color: #95a5a6; font-size: 12px;">
-        For questions or feedback, please open an issue on GitHub
-    </p>
-</div>
-"""
-
-# Create Gradio interface with custom CSS
-with gr.Blocks(
-    title="Waste Classifier",
-    theme=gr.themes.Soft(),
-    css="""
-    .output-image {
-        max-width: 400px;
-        margin-left: auto;
-        margin-right: auto;
-    }
-    """
-) as demo:
-    # Header
-    gr.HTML(HEADER_HTML)
-    
-    # Main content
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 📸 Step 1: Upload Image")
-            image_input = gr.Image(
-                label="Upload or Drag & Drop a Waste Image",
-                type="pil",
-                sources=["upload", "webcam"],
-            )
-            
-            with gr.Row():
-                clear_btn = gr.ClearButton(image_input, value="Clear Image")
-                submit_btn = gr.Button("🔍 Classify Waste", variant="primary", size="lg")
-        
-        with gr.Column(scale=1):
-            gr.Markdown("### 📊 Step 2: View Results")
-            output_text = gr.Textbox(
-                label="Classification Result",
-                lines=12,
-                interactive=False,
-                show_label=True,
-            )
-    
-    # Chart for predictions
-    gr.Markdown("### 📈 Confidence Distribution")
-    chart = gr.BarChart(
-        label="Confidence Scores",
-        x="Waste Type",
-        y="Confidence (%)",
-        show_label=True,
-        every=0.5,
-    )
-    
-    # Example images section
-    gr.Markdown("---\n### 💡 Tips for Best Results\n"
-                "- Use clear, well-lit images\n"
-                "- Avoid blurry or partially visible items\n"
-                "- Single waste items work best\n"
-                "- Try different angles if unsure")
-    
-    # Example gallery
-    examples_html = """
-    <div style="padding: 20px; background: #f8f9fa; border-radius: 10px; margin: 20px 0;">
-        <h4 style="margin-top: 0;">📸 Example Categories</h4>
-        <p><strong>♻️ Dry Waste:</strong> Paper, cardboard, plastic bottles, cans, books, packaging</p>
-        <p><strong>🍃 Wet Waste:</strong> Food scraps, fruit peels, leaves, grass, cooked food</p>
-        <p><strong>🔶 Other Waste:</strong> Electronics, batteries, broken glass, chemicals</p>
-    </div>
-    """
-    gr.HTML(examples_html)
-    
-    # Footer
-    gr.HTML(FOOTER_HTML)
-    
-    # Connect button to prediction
-    def on_submit(image):
-        if image is None:
-            return "Upload an image first.", None
-        
-        text, results = predict_waste(image)
-        
-        # Format chart data
-        chart_data = [{"Waste Type": k.split()[-1], "Confidence (%)": v} for k, v in (results.items() if results else [])]
-        
-        return text, chart_data if chart_data else None
-    
-    submit_btn.click(
-        on_submit,
-        inputs=[image_input],
-        outputs=[output_text, chart]
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()

README.md

@@ -0,0 +1,48 @@
+---
+language: en
+license: apache-2.0
+library_name: tensorflow
+pipeline_tag: image-classification
+tags:
+- waste-classification
+- efficientnet
+- recycling
+- sustainability
+---
+
+# Waste Classifier (EfficientNetB0)
+
+Image classification model for waste segregation into three classes:
+- dry_waste
+- wet_waste
+- other_waste
+
+## Model Files
+- waste_classifier.keras: TensorFlow Keras model artifact
+- inference.py: Local inference utility
+- requirements.txt: Python dependencies for local inference
+
+## Quick Start
+
+```python
+from inference import predict
+
+result = predict("sample.jpg")
+print(result)
+```
+
+## Output Format
+
+```json
+{
+  "class": "dry_waste",
+  "display_name": "♻️ Dry Waste",
+  "confidence": 92.5,
+  "action": "Can be recycled → Paper, plastic, metal recovery",
+  "all_predictions": {
+    "dry_waste": 92.5,
+    "other_waste": 4.1,
+    "wet_waste": 3.4
+  }
+}
+```

app.py

@@ -1,10 +0,0 @@
-"""Hugging Face Space entrypoint.
-
-Spaces with Gradio SDK look for app.py at repository root.
-"""
-
-from HF_SPACE_APP import demo
-
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)

inference.py

@@ -0,0 +1,51 @@
+"""Local inference helper for waste classification model."""
+
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+IMG_SIZE = 224
+MODEL_PATH = os.path.join(os.getcwd(), "waste_classifier.keras")
+
+CLASS_NAMES = ["dry_waste", "other_waste", "wet_waste"]
+CLASS_DISPLAY = {
+    "dry_waste": "♻️ Dry Waste",
+    "other_waste": "🔶 Other Waste",
+    "wet_waste": "🍃 Wet Waste",
+}
+CLASS_ACTIONS = {
+    "dry_waste": "Can be recycled → Paper, plastic, metal recovery",
+    "other_waste": "Needs special handling → Hazardous / e-waste processing",
+    "wet_waste": "Compostable → Organic composting / biogas generation",
+}
+
+model = tf.keras.models.load_model(MODEL_PATH)
+
+
+def predict(image_path: str):
+    """Predict waste class for an image path."""
+    img = image.load_img(image_path, target_size=(IMG_SIZE, IMG_SIZE))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    predictions = model.predict(img_array, verbose=0)[0]
+
+    index = int(np.argmax(predictions))
+    class_name = CLASS_NAMES[index]
+    confidence = float(predictions[index] * 100)
+
+    all_predictions = {
+        CLASS_NAMES[i]: round(float(predictions[i] * 100), 1)
+        for i in range(len(CLASS_NAMES))
+    }
+
+    return {
+        "class": class_name,
+        "display_name": CLASS_DISPLAY.get(class_name, class_name),
+        "confidence": round(confidence, 1),
+        "action": CLASS_ACTIONS.get(class_name, ""),
+        "all_predictions": all_predictions,
+    }

requirements.txt

@@ -1,5 +1,3 @@
-gradio==5.23.1
+tensorflow-cpu==2.15.1
 numpy==1.26.4
 pillow==10.4.0
-huggingface-hub==0.29.3
-requests