Create app.py

app.py

@@ -0,0 +1,106 @@
+!pip install -q transformers torch Pillow requests matplotlib gradio
+
+import torch
+import requests
+from io import BytesIO
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import gradio as gr
+
+from transformers import AutoProcessor, OmDetTurboForObjectDetection
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Iniciando no dispositivo: {device.upper()}")
+
+processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+model = OmDetTurboForObjectDetection.from_pretrained(
+    "omlab/omdet-turbo-swin-tiny-hf"
+).to(device)
+
+def plot_results(image, results):
+    fig, ax = plt.subplots(1, figsize=(8, 6))
+    ax.imshow(image)
+    ax.axis("off")
+
+    labels = results.get("text_labels", results.get("classes", []))
+
+    for score, class_name, box in zip(results["scores"], labels, results["boxes"]):
+        xmin, ymin, xmax, ymax = box.tolist()
+        rect = patches.Rectangle(
+            (xmin, ymin),
+            xmax - xmin,
+            ymax - ymin,
+            linewidth=2,
+            edgecolor='red',
+            facecolor='none'
+        )
+        ax.add_patch(rect)
+
+        label = f"{class_name}: {score:.2f}"
+        ax.text(
+            xmin,
+            ymin - 5,
+            label,
+            color='white',
+            fontsize=10,
+            weight='bold',
+            backgroundcolor="red"
+        )
+
+    return fig
+
+def detectar_objetos(url, classes_texto):
+    try:
+        image = Image.open(BytesIO(requests.get(url).content)).convert("RGB")
+
+        classes = [c.strip() for c in classes_texto.split(",")]
+        task = "Detect {}.".format(", ".join(classes))
+
+        inputs = processor(
+            images=[image],
+            text=[classes],
+            task=[task],
+            return_tensors="pt",
+        ).to(device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        results = processor.post_process_grounded_object_detection(
+            outputs,
+            text_labels=[classes],
+            target_sizes=[image.size[::-1]],
+            threshold=0.2,
+            nms_threshold=0.3,
+        )[0]
+
+        saida = ""
+        labels = results.get("text_labels", results.get("classes", []))
+
+        for score, class_name, box in zip(results["scores"], labels, results["boxes"]):
+            box_rounded = [round(b, 1) for b in box.tolist()]
+            saida += f"{class_name} ({round(score.item(),2)}) -> {box_rounded}\n"
+
+        fig = plot_results(image, results)
+
+        return fig, saida
+
+    except Exception as e:
+        return None, f"Erro: {str(e)}"
+
+interface = gr.Interface(
+    fn=detectar_objetos,
+    inputs=[
+        gr.Textbox(label="URL da imagem"),
+        gr.Textbox(label="Classes (separadas por vírgula)", value="cat, dog")
+    ],
+    outputs=[
+        gr.Plot(label="Imagem com detecção"),
+        gr.Textbox(label="Resultados")
+    ],
+    title="Detecção de Objetos por URL",
+    description="Cole uma URL de imagem e informe os objetos que deseja detectar."
+)
+
+interface.launch()