Upload 3 files

README.md

@@ -1,13 +1,16 @@
 ---
-title: Ambiance En Jp 1
-emoji: 😻
-colorFrom: pink
-colorTo: purple
+title: Lighting Ambiance Classifier
+emoji: 🏠
+colorFrom: yellow
+colorTo: green
 sdk: gradio
-sdk_version: 5.30.0
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏠 Lighting Ambiance Classifier
+
+Classify lighting preferences into bright, cozy, or natural categories.
+Supports English and Japanese input!

app.py

@@ -0,0 +1,186 @@
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+import torch
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global model variable
+model = None
+
+def load_model():
+    """Load the sentence transformer model"""
+    global model
+    if model is None:
+        try:
+            logger.info("Loading sentence transformer model...")
+            model = SentenceTransformer('nabil-tazi/autotrain-c0qmd-8o6a3')
+            logger.info("Model loaded successfully!")
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            raise e
+    return model
+
+def classify_ambiance(user_input):
+    """Classify lighting ambiance from user input"""
+    
+    if not user_input or not user_input.strip():
+        return "❌ Please enter some text", {}, ""
+    
+    try:
+        # Load model if not already loaded
+        current_model = load_model()
+        
+        # Your three reference ambiances
+        references = ["bright", "cozy", "natural"]
+        
+        # Get embeddings
+        user_embedding = current_model.encode([user_input.strip()])
+        ref_embeddings = current_model.encode(references)
+        
+        # Calculate similarities
+        similarities = cos_sim(user_embedding, ref_embeddings)[0]
+        
+        # Get best match
+        best_idx = similarities.argmax()
+        best_ambiance = references[best_idx]
+        confidence = float(similarities[best_idx])
+        
+        # Format all scores for debugging
+        all_scores = {ref: round(float(sim), 4) for ref, sim in zip(references, similarities)}
+        
+        # Create result with emoji
+        emoji_map = {"bright": "☀️", "cozy": "🕯️", "natural": "🌿"}
+        result_text = f"## {emoji_map.get(best_ambiance, '💡')} **{best_ambiance.upper()}**\n**Confidence:** {confidence:.3f}"
+        
+        # Create confidence bar
+        confidence_bar = f"**Confidence Level:** {'█' * int(confidence * 20)}{'░' * (20 - int(confidence * 20))} {confidence:.1%}"
+        
+        logger.info(f"Classified '{user_input}' as '{best_ambiance}' with confidence {confidence:.3f}")
+        
+        return result_text, all_scores, confidence_bar
+        
+    except Exception as e:
+        error_msg = f"❌ Error: {str(e)}"
+        logger.error(f"Classification error: {e}")
+        return error_msg, {}, ""
+
+# Create Gradio interface
+with gr.Blocks(
+    title="🏠 Lighting Ambiance Classifier",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {
+        max-width: 800px !important;
+        margin: auto !important;
+    }
+    .result-box {
+        background: linear-gradient(45deg, #f0f0f0, #ffffff);
+        border-radius: 10px;
+        padding: 20px;
+    }
+    """
+) as demo:
+    
+    # Header
+    gr.Markdown(
+        """
+        # 🏠 Lighting Ambiance Classifier
+        
+        **Classify your lighting preferences into three categories:**
+        - ☀️ **Bright**: Well-lit, luminous, clear lighting
+        - 🕯️ **Cozy**: Warm, dim, soft, ambient lighting  
+        - 🌿 **Natural**: Daylight, sunlight, organic lighting
+        
+        **Supports both English and Japanese!** 🇺🇸🇯🇵
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Input section
+            gr.Markdown("### 💬 Enter your lighting preference:")
+            input_text = gr.Textbox(
+                label="Your lighting preference",
+                placeholder="e.g., 'not bright', '明るくない', 'cozy lighting', '自然な光が欲しい'",
+                lines=3,
+                max_lines=5
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("🔍 Classify", variant="primary", size="lg")
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
+            
+        with gr.Column(scale=2):
+            # Output section
+            gr.Markdown("### 🎯 Classification Result:")
+            result = gr.Markdown(value="Enter text and click classify!", elem_classes=["result-box"])
+            confidence_bar = gr.Markdown(value="")
+    
+    # Detailed scores
+    with gr.Row():
+        scores = gr.JSON(label="📊 Detailed Similarity Scores", visible=True)
+    
+    # Example inputs
+    gr.Markdown("### 💡 Try these examples:")
+    with gr.Row():
+        examples = gr.Examples(
+            examples=[
+                ["not bright"],
+                ["明るくない"], 
+                ["I want cozy lighting"],
+                ["自然な光が欲しい"],
+                ["make it brighter"],
+                ["暗くしたい"],
+                ["romantic atmosphere"],
+                ["作業しやすい明るさ"],
+                ["candle light"],
+                ["太陽光みたい"],
+                ["harsh fluorescent"],
+                ["優しい照明"]
+            ],
+            inputs=input_text,
+            examples_per_page=6
+        )
+    
+    # Footer
+    gr.Markdown(
+        """
+        ---
+        **Model:** Fine-tuned multilingual sentence transformer trained on English-Japanese lighting preference pairs.
+        
+        **How it works:** The model compares your input text with the three ambiance categories and returns the most similar one with a confidence score.
+        """
+    )
+    
+    # Event handlers
+    def clear_all():
+        return "", "Enter text and click classify!", {}, ""
+    
+    submit_btn.click(
+        fn=classify_ambiance,
+        inputs=input_text,
+        outputs=[result, scores, confidence_bar]
+    )
+    
+    input_text.submit(
+        fn=classify_ambiance,
+        inputs=input_text,
+        outputs=[result, scores, confidence_bar]
+    )
+    
+    clear_btn.click(
+        fn=clear_all,
+        outputs=[input_text, result, scores, confidence_bar]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )

requirements.txt

@@ -0,0 +1,5 @@
+sentence-transformers>=2.2.0
+torch
+gradio>=4.0.0
+transformers
+numpy