inicial commit

.gitignore

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+venv
+models

.gradio/flagged/dataset1.csv

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+image,Classe prevista,timestamp
+"{""background"": "".gradio\\flagged\\image\\2fa0d477d3d7a47e41e4\\background.png"", ""layers"": ["".gradio\\flagged\\image\\3a5f4694877ab3c14fcb\\layer_0.png""], ""composite"": "".gradio\\flagged\\image\\b6d8ebce78d56bf966d8\\composite.png"", ""id"": null}",,2025-06-19 13:51:31.311676
+"{""background"": "".gradio\\flagged\\image\\3c05ae06f88de6e976f6\\background.png"", ""layers"": ["".gradio\\flagged\\image\\782b7ede3eb388a3b583\\layer_0.png""], ""composite"": "".gradio\\flagged\\image\\1b4feabd698cceec7ebf\\composite.png"", ""id"": null}",,2025-06-19 13:58:44.445782
+"{""background"": "".gradio\\flagged\\image\\dc7898043f6e7a661667\\background.png"", ""layers"": ["".gradio\\flagged\\image\\56d2383e839f42f253e4\\layer_0.png""], ""composite"": "".gradio\\flagged\\image\\58839e1b29750e1c9af7\\composite.png"", ""id"": null}",,2025-06-19 14:02:14.136177
+"{""background"": "".gradio\\flagged\\image\\ef5108db4c6b89664191\\background.png"", ""layers"": ["".gradio\\flagged\\image\\0206e03a92fece916018\\layer_0.png""], ""composite"": "".gradio\\flagged\\image\\e4bc1f7342505dd9f966\\composite.png"", ""id"": null}",,2025-06-19 14:05:11.896509

.gradio/flagged/dataset2.csv

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+image,output,timestamp
+"{""background"": "".gradio\\flagged\\image\\72b571c1cc3a3abf0707\\background.png"", ""layers"": ["".gradio\\flagged\\image\\70b10cc9d3e368757c03\\layer_0.png""], ""composite"": "".gradio\\flagged\\image\\b175b154315f90d20bd5\\composite.png"", ""id"": null}","{""label"": null, ""confidences"": null}",2025-06-19 15:01:06.418014

README.md

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----
-title: Placeholder2
-emoji: 🏢
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 5.34.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+import torch.nn.functional as F
+from PIL import Image
+
+# Caminho do modelo
+model_path = "models/simple_cnn_jit_epoch_10.pt"
+
+# Pré-processamento 
+transform = transforms.Compose([
+    transforms.Resize((8, 8)),
+    transforms.ToTensor(),
+    transforms.Lambda(lambda x: 1 - x)
+])
+
+# Carregar o modelo JIT
+model = torch.jit.load(model_path, map_location="cpu")
+model.eval()
+
+# Função de predição
+def predict(inputs):
+    pil_img = Image.fromarray(inputs['composite'])
+    image = transform(pil_img).unsqueeze(0)
+    print(image)
+    with torch.no_grad():
+        output = model(image)
+        probs = F.softmax(output, dim=1).squeeze().cpu().numpy() 
+        print(probs) 
+    labels = [str(i) for i in range(len(probs))] 
+    print(labels)
+    result = {label: float(prob) for label, prob in zip(labels, probs)}
+    print(result)
+    return result
+
+# Interface Gradio
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Sketchpad(crop_size=(256,256), type='numpy', image_mode='L', brush=gr.Brush(color_mode="fixed", colors=['#000000']), layers=False, show_label=False),
+    outputs=gr.Label(show_label=False, num_top_classes=7),
+    examples=["examples/digit1.png", "examples/digit2.png", "examples/digit3.png", "examples/digit4.png", "examples/digit5.png", "examples/digit6.png", "examples/digit7.png", "examples/digit8.png", "examples/digit9.png"],
+    title="Number Classification",
+    description="Number classification using a simple CNN model trained on the MNIST dataset. Draw a digit in the sketchpad or select from the examples and click 'Submit' to classify it.",
+)
+
+demo.launch()

requirements.txt

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+gradio
+torch
+torchvision
+PIL