Update app.py

app.py

@@ -8,78 +8,102 @@ import threading
 processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
 model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
 
-def process_image(image, prompt):
-    inputs = processor(
-        text=prompt, images=image, padding="max_length", return_tensors="pt"
-    )
-    with torch.no_grad():
-        outputs = model(**inputs)
-        preds = outputs.logits
-    pred = torch.sigmoid(preds)
-    mat = pred.cpu().numpy()
-    mask = Image.fromarray(np.uint8(mat * 255), "L")
-    mask = mask.convert("RGB")
-    mask = mask.resize(image.size)
-    mask = np.array(mask)[:, :, 0]
-    mask_min = mask.min()
-    mask_max = mask.max()
-    mask = (mask - mask_min) / (mask_max - mask_min)
-    return mask
-
-def get_masks(prompts, img, threshold):
-    prompts = prompts.split(",")
-    masks = []
-    for prompt in prompts:
-        mask = process_image(img, prompt)
-        mask = mask > threshold
-        masks.append(mask)
-    return masks
-
-def extract_image(pos_prompts, neg_prompts, img, threshold):
-    positive_masks = get_masks(pos_prompts, img, 0.5)
-    negative_masks = get_masks(neg_prompts, img, 0.5)
-    
-    pos_mask = np.any(np.stack(positive_masks), axis=0)
-    neg_mask = np.any(np.stack(negative_masks), axis=0)
-    final_mask = pos_mask & ~neg_mask
-    
-    final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
-    output_image = Image.new("RGBA", img.size, (0, 0, 0, 0))
-    output_image.paste(img, mask=final_mask)
-    return output_image, final_mask
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts")
+
+    # Add your article and description here
+    gr.Markdown("Your article goes here")
+    gr.Markdown("Your description goes here")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil")
+            positive_prompts = gr.Textbox(
+                label="Please describe what you want to identify (comma separated)"
+            )
+            negative_prompts = gr.Textbox(
+                label="Please describe what you want to ignore (comma separated)"
+            )
+            input_slider_T = gr.Slider(
+                minimum=0, maximum=1, value=0.4, label="Threshold"
+            )
+            btn_process = gr.Button(label="Process")
+
+        with gr.Column():
+            output_image = gr.Image(label="Result")
+            output_mask = gr.Image(label="Mask")
+
+    def process_image(image, prompt):
+        inputs = processor(
+            text=prompt, images=image, padding="max_length", return_tensors="pt"
+        )
+        with torch.no_grad():
+            outputs = model(**inputs)
+            preds = outputs.logits
+
+        pred = torch.sigmoid(preds)
+        mat = pred.cpu().numpy()
+        mask = Image.fromarray(np.uint8(mat * 255), "L")
+        mask = mask.convert("RGB")
+        mask = mask.resize(image.size)
+        mask = np.array(mask)[:, :, 0]
+
+        mask_min = mask.min()
+        mask_max = mask.max()
+        mask = (mask - mask_min) / (mask_max - mask_min)
+
+        return mask
+
+    def get_masks(prompts, img, threshold):
+        prompts = prompts.split(",")
+        masks = []
+        for prompt in prompts:
+            mask = process_image(img, prompt)
+            mask = mask > threshold
+            masks.append(mask)
+
+        return masks
 
+    def extract_image(pos_prompts, neg_prompts, img, threshold):
+        positive_masks = get_masks(pos_prompts, img, 0.5)
+        negative_masks = get_masks(neg_prompts, img, 0.5)
+
+        pos_mask = np.any(np.stack(positive_masks), axis=0)
+        neg_mask = np.any(np.stack(negative_masks), axis=0)
+        final_mask = pos_mask & ~neg_mask
+
+        final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
+        output_image = Image.new("RGBA", img.size, (0, 0, 0, 0))
+        output_image.paste(img, mask=final_mask)
+
+        return output_image, final_mask
+
+    btn_process.click(
+        extract_image,
+        inputs=[
+            positive_prompts,
+            negative_prompts,
+            input_image,
+            input_slider_T,
+        ],
+        outputs=[output_image, output_mask],
+    )
 iface = gr.Interface(
-    fn=extract_image,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Textbox(label="Positive Prompts (comma separated)"),
-        gr.Textbox(label="Negative Prompts (comma separated)"),
-        gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
-    ],
-    outputs=[
-        gr.Image(type="pil", label="Output Image"),
-        gr.Image(type="pil", label="Output Mask"),
+    extract_image,
+    [
+        gr.Textbox(label="Positive prompts"),
+        gr.Textbox(label="Negative prompts"),
+        gr.Image(type="pil"),
+        gr.Slider(minimum=0, maximum=1, value=0.4, label="Threshold"),
     ],
+    [gr.Image(label="Result")],  # Only return the final image
+    "textbox,textbox,image,slider",  # Match the directory name (without mask)
+    "image",
+    title="CLIPSeg API",
 )
 
-# Launch Gradio UI
-iface.launch()
-
-# Define API interface
-api_interface = gr.Interface(
-    fn=extract_image,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Textbox(label="Positive Prompts (comma separated)"),
-        gr.Textbox(label="Negative Prompts (comma separated)"),
-        gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
-    ],
-    outputs=[
-        gr.Image(type="pil", label="Output Image"),
-        gr.Image(type="pil", label="Output Mask"),
-    ],
-    live=True  # Setting live to True enables the API endpoint
-)
+# Launch both UI and API
+demo.launch()
+iface.launch(share=True)
 
-# Launch API
-api_interface.launch(share=True)  # share=True allows external access to the API