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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image, ImageFilter, ImageEnhance, ImageOps
+from transformers import pipeline
+
+# ---- Load models (cached on first use) ----
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+detector = pipeline("object-detection", model="facebook/detr-resnet-50")
+segmenter = pipeline("image-segmentation", model="facebook/detr-resnet-50-panoptic")
+
+# ---- Tab 1: Filters & Effects ----
+def apply_filter(image, effect, intensity):
+    if image is None:
+        raise gr.Error("Please upload an image first.")
+
+    img = Image.fromarray(image)
+
+    if effect == "Grayscale":
+        filtered = ImageOps.grayscale(img).convert("RGB")
+        if intensity < 1.0:
+            filtered = Image.blend(img, filtered, intensity)
+    elif effect == "Sepia":
+        gray = ImageOps.grayscale(img)
+        sepia = ImageOps.colorize(gray, "#704214", "#C0A080")
+        filtered = Image.blend(img, sepia, intensity)
+    elif effect == "Blur":
+        radius = int(intensity * 10)
+        filtered = img.filter(ImageFilter.GaussianBlur(radius=max(1, radius)))
+    elif effect == "Sharpen":
+        enhancer = ImageEnhance.Sharpness(img)
+        filtered = enhancer.enhance(1 + intensity * 4)
+    elif effect == "Edge Detect":
+        edges = img.filter(ImageFilter.FIND_EDGES)
+        filtered = Image.blend(img, edges, intensity)
+    elif effect == "Emboss":
+        embossed = img.filter(ImageFilter.EMBOSS)
+        filtered = Image.blend(img, embossed, intensity)
+    elif effect == "Invert":
+        inverted = ImageOps.invert(img.convert("RGB"))
+        filtered = Image.blend(img, inverted, intensity)
+    elif effect == "Posterize":
+        bits = max(1, int(8 - intensity * 6))
+        filtered = ImageOps.posterize(img.convert("RGB"), bits)
+    elif effect == "Brightness":
+        enhancer = ImageEnhance.Brightness(img)
+        filtered = enhancer.enhance(0.5 + intensity * 1.5)
+    elif effect == "Contrast":
+        enhancer = ImageEnhance.Contrast(img)
+        filtered = enhancer.enhance(0.5 + intensity * 2)
+    else:
+        filtered = img
+
+    return np.array(filtered)
+
+
+# ---- Tab 2: Image Classification ----
+def classify_image(image):
+    if image is None:
+        raise gr.Error("Please upload an image first.")
+    img = Image.fromarray(image)
+    results = classifier(img)
+    return {r["label"]: r["score"] for r in results}
+
+
+# ---- Tab 3: Object Detection ----
+def detect_objects(image, threshold):
+    if image is None:
+        raise gr.Error("Please upload an image first.")
+    img = Image.fromarray(image)
+    results = detector(img, threshold=threshold)
+
+    annotations = []
+    for r in results:
+        box = r["box"]
+        annotations.append((
+            (box["xmin"], box["ymin"], box["xmax"], box["ymax"]),
+            f"{r['label']} ({r['score']:.0%})"
+        ))
+
+    return (image, annotations)
+
+
+# ---- Tab 4: Segmentation ----
+def segment_image(image):
+    if image is None:
+        raise gr.Error("Please upload an image first.")
+    img = Image.fromarray(image)
+    results = segmenter(img)
+
+    annotations = []
+    for r in results:
+        mask = np.array(r["mask"])
+        annotations.append((mask, r["label"]))
+
+    return (image, annotations)
+
+
+# ---- Build the UI ----
+css = """
+.main-title { text-align: center; margin-bottom: 0.5em; }
+.subtitle { text-align: center; color: #666; margin-top: 0; }
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.Markdown("# Image Processing Studio", elem_classes="main-title")
+    gr.Markdown(
+        "Upload an image and explore filters, classification, object detection, and segmentation -- all powered by state-of-the-art models.",
+        elem_classes="subtitle"
+    )
+
+    with gr.Tab("Filters & Effects"):
+        with gr.Row():
+            with gr.Column():
+                filter_input = gr.Image(label="Upload Image", type="numpy")
+                filter_effect = gr.Dropdown(
+                    choices=[
+                        "Grayscale", "Sepia", "Blur", "Sharpen",
+                        "Edge Detect", "Emboss", "Invert", "Posterize",
+                        "Brightness", "Contrast"
+                    ],
+                    value="Sepia",
+                    label="Effect"
+                )
+                filter_intensity = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.7, step=0.05,
+                    label="Intensity"
+                )
+                filter_btn = gr.Button("Apply Filter", variant="primary")
+            with gr.Column():
+                filter_output = gr.Image(label="Result", type="numpy")
+
+        filter_btn.click(
+            fn=apply_filter,
+            inputs=[filter_input, filter_effect, filter_intensity],
+            outputs=filter_output
+        )
+
+    with gr.Tab("Image Classification"):
+        with gr.Row():
+            with gr.Column():
+                cls_input = gr.Image(label="Upload Image", type="numpy")
+                cls_btn = gr.Button("Classify", variant="primary")
+            with gr.Column():
+                cls_output = gr.Label(label="Predictions", num_top_classes=5)
+
+        cls_btn.click(
+            fn=classify_image,
+            inputs=cls_input,
+            outputs=cls_output
+        )
+
+    with gr.Tab("Object Detection"):
+        with gr.Row():
+            with gr.Column():
+                det_input = gr.Image(label="Upload Image", type="numpy")
+                det_threshold = gr.Slider(
+                    minimum=0.1, maximum=0.95, value=0.5, step=0.05,
+                    label="Confidence Threshold"
+                )
+                det_btn = gr.Button("Detect Objects", variant="primary")
+            with gr.Column():
+                det_output = gr.AnnotatedImage(label="Detections")
+
+        det_btn.click(
+            fn=detect_objects,
+            inputs=[det_input, det_threshold],
+            outputs=det_output
+        )
+
+    with gr.Tab("Segmentation"):
+        with gr.Row():
+            with gr.Column():
+                seg_input = gr.Image(label="Upload Image", type="numpy")
+                seg_btn = gr.Button("Segment", variant="primary")
+            with gr.Column():
+                seg_output = gr.AnnotatedImage(label="Segmentation Map")
+
+        seg_btn.click(
+            fn=segment_image,
+            inputs=seg_input,
+            outputs=seg_output
+        )
+
+demo.launch()