Upload folder using huggingface_hub

.editorconfig

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+[*]
+indent_size = 4
+indent_style = tab
+charset = utf-8
+
+[*.ts, *.tsx]
+insert_final_newline = true

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+vgg16_model.keras filter=lfs diff=lfs merge=lfs -text

.gitignore

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+data/cifar-10-python.tar.gz
+.DS_Store
+data

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+}

README copy.md

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+---
+title: ironhack-project-1
+app_file: app.py
+sdk: gradio
+sdk_version: 5.30.0
+---
+# Ironhack Project 1
+
+For this project, we selected the smallest dataset [Animals-10](https://www.kaggle.com/datasets/alessiocorrado99/animals10/data) to be able to work on our own machines. 
+
+We tried to use the Animals 10, but we encountered so many bugs that we decided to switch.
+
+### Run app
+
+#### Server
+
+Open this directory in a terminal and run `python app.py`
+
+#### Client
+
+Open the `client` directory in a separate terminal and run `npm run dev`

README.md

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+---
+title: Ironhack Project 1 Fix
+emoji: 👀
+colorFrom: pink
+colorTo: pink
+sdk: gradio
+sdk_version: 5.31.0
+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 tensorflow as tf
+import cv2
+
+model = tf.keras.models.load_model(r"./vgg16_model.keras")
+
+class_names = [
+    "airplane",
+    "automobile",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+]
+
+
+def predict(image):
+    # Resize image to (32, 32)
+    image = cv2.resize(image, (32, 32))
+    print("Resized image shape:", image.shape)  # Print the shape of the resized image
+    # Convert image to float32 and normalize
+    image = image.astype("float32") / 255.0
+    # Add batch dimension
+    image = tf.expand_dims(image, 0)
+    # Predict using the model
+    prediction = model.predict(image)
+    class_index = tf.argmax(prediction, axis=1)[0].numpy()
+    class_label = class_names[class_index]  # Get the class label
+    return class_label
+
+
+gr.Interface(fn=predict, inputs="image", outputs="text").launch(share=True)

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