Add another tab for uploading an image to generate a new image

app.py

@@ -4,6 +4,9 @@ from transformers import pipeline, CLIPProcessor, CLIPModel
 from simple_train import simple_train
 import glob
 from pathlib import Path
+from PIL import Image
+import numpy as np
+from sklearn.cluster import KMeans
 
 
 MODEL_ID = os.getenv("MODEL_ID", "stabilityai/sdxl-turbo")
@@ -183,6 +186,103 @@ def load_trained_model(model_selection):
     else:
         return load_classifier("openai/clip-vit-base-patch32")
 
+# French-style arrangement functions
+def extract_dominant_colors(image, num_colors=5):
+    """Extract dominant colors from an image using k-means clustering"""
+    if image is None:
+        return [], "No image provided"
+    
+    # Convert PIL image to numpy array
+    img_array = np.array(image)
+    
+    # Reshape image to be a list of pixels
+    pixels = img_array.reshape(-1, 3)
+    
+    # Use k-means to find dominant colors
+    kmeans = KMeans(n_clusters=num_colors, random_state=42, n_init=10)
+    kmeans.fit(pixels)
+    
+    # Get the colors and convert to RGB values
+    colors = kmeans.cluster_centers_.astype(int)
+    
+    # Convert to color names/descriptions
+    color_names = []
+    for color in colors:
+        r, g, b = color
+        if r > 200 and g > 200 and b > 200:
+            color_names.append("white")
+        elif r < 50 and g < 50 and b < 50:
+            color_names.append("black")
+        elif r > g and r > b:
+            if r > 150 and g < 100:
+                color_names.append("red" if g < 50 else "pink")
+            else:
+                color_names.append("coral")
+        elif g > r and g > b:
+            if b < 100:
+                color_names.append("yellow" if g > 200 and r > 150 else "green")
+            else:
+                color_names.append("teal")
+        elif b > r and b > g:
+            if r < 100:
+                color_names.append("blue" if b > 150 else "navy")
+            else:
+                color_names.append("purple" if r > g else "lavender")
+        elif r > 150 and g > 100 and b < 100:
+            color_names.append("orange")
+        else:
+            color_names.append("cream")
+    
+    return color_names, colors
+
+def analyze_and_generate_french_style(image):
+    """Analyze uploaded flower image and generate French-style arrangement"""
+    if image is None:
+        return None, "Please upload an image", ""
+    
+    # Identify the flower type
+    if zs_classifier is None:
+        return None, "Model not loaded", ""
+    
+    try:
+        # Identify flower
+        results = zs_classifier(
+            image,
+            candidate_labels=FLOWER_LABELS,
+            hypothesis_template="a photo of a {}"
+        )
+        
+        top_flower = results[0]["label"] if results else "flower"
+        confidence = results[0]["score"] if results else 0
+        
+        # Extract dominant colors
+        color_names, color_rgb = extract_dominant_colors(image, num_colors=3)
+        
+        # Create color description
+        main_colors = color_names[:3]  # Top 3 colors
+        color_desc = ", ".join(main_colors)
+        
+        # Generate French-style prompt
+        prompt = f"elegant French-style floral arrangement featuring {top_flower}s in {color_desc} colors, displayed in a clear crystal vase on a marble kitchen countertop, soft natural lighting, minimalist French country kitchen background, professional photography, sophisticated composition"
+        
+        # Generate the image
+        generated_image = generate(prompt, steps=4, width=1024, height=1024, seed=-1)
+        
+        # Create analysis summary
+        analysis = f"""
+**🌸 Flower Analysis:**
+- **Type:** {top_flower} (confidence: {confidence:.2%})
+- **Dominant Colors:** {color_desc}
+
+**🇫🇷 Generated Prompt:**
+"{prompt}"
+"""
+        
+        return generated_image, "✅ Analysis complete! French-style arrangement generated.", analysis
+        
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}", ""
+
 # ---------- UI ----------
 with gr.Blocks() as demo:
     gr.Markdown("# 🌸 SDXL-Turbo — Text → Image + Flower Identifier")
@@ -240,6 +340,20 @@ with gr.Blocks() as demo:
                     gr.Markdown("### Training Status")
                     training_output = gr.Markdown()
 
+        with gr.TabItem("French Style"):
+            gr.Markdown("## 🇫🇷 French-Style Flower Arrangements")
+            gr.Markdown("Upload a flower image and generate an elegant French-style arrangement with matching colors!")
+            
+            with gr.Row():
+                with gr.Column():
+                    upload_img = gr.Image(label="Upload Flower Image", type="pil")
+                    analyze_btn = gr.Button("🎨 Analyze & Generate French Style", variant="primary", size="lg")
+                    
+                with gr.Column():
+                    french_result = gr.Image(label="Generated French-Style Arrangement", type="pil")
+                    french_status = gr.Markdown()
+                    analysis_details = gr.Markdown()
+
     # Wire events
     go.click(generate, [prompt, steps, width, height, seed], [out])
     # Auto-send generated image to Identify tab
@@ -253,6 +367,13 @@ with gr.Blocks() as demo:
     load_model_btn.click(load_trained_model, inputs=[model_dropdown], outputs=[model_status])
     train_btn.click(start_training, inputs=[epochs, batch_size, learning_rate], outputs=[training_output])
     
+    # French Style tab events
+    analyze_btn.click(
+        analyze_and_generate_french_style, 
+        inputs=[upload_img], 
+        outputs=[french_result, french_status, analysis_details]
+    )
+    
     # Initialize data count on load
     demo.load(count_training_images, outputs=[data_status])
 

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "diffusers>=0.35.1",
     "gradio>=5.44.0",
     "pillow>=11.3.0",
+    "scikit-learn>=1.7.1",
     "torch>=2.8.0",
     "torchvision>=0.23.0",
     "transformers[torch]>=4.55.4",

uv.lock

@@ -247,6 +247,7 @@ dependencies = [
     { name = "diffusers" },
     { name = "gradio" },
     { name = "pillow" },
+    { name = "scikit-learn" },
     { name = "torch" },
     { name = "torchvision" },
     { name = "transformers", extra = ["torch"] },
@@ -257,6 +258,7 @@ requires-dist = [
     { name = "diffusers", specifier = ">=0.35.1" },
     { name = "gradio", specifier = ">=5.44.0" },
     { name = "pillow", specifier = ">=11.3.0" },
+    { name = "scikit-learn", specifier = ">=1.7.1" },
     { name = "torch", specifier = ">=2.8.0" },
     { name = "torchvision", specifier = ">=0.23.0" },
     { name = "transformers", extras = ["torch"], specifier = ">=4.55.4" },
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 version = "4.0.0"
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