Upload folder using huggingface_hub

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+model.h5 filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
-title: Trafic Sing
-emoji: 🏆
-colorFrom: red
-colorTo: blue
+title: trafic_sing
+app_file: app.py
 sdk: gradio
 sdk_version: 4.19.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading trained model\n",
+    "model = tf.keras.models.load_model(\"model.h5\")  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Label Overview\n",
+    "class_num = { 0:'Speed limit (20km/h)',\n",
+    "            1:'Speed limit (30km/h)', +\n",
+    "            2:'Speed limit (50km/h)', \n",
+    "            3:'Speed limit (60km/h)', \n",
+    "            4:'Speed limit (70km/h)', \n",
+    "            5:'Speed limit (80km/h)', \n",
+    "            6:'End of speed limit (80km/h)', \n",
+    "            7:'Speed limit (100km/h)', \n",
+    "            8:'Speed limit (120km/h)', \n",
+    "            9:'No passing', \n",
+    "            10:'No passing veh over 3.5 tons', \n",
+    "            11:'Right-of-way at intersection', \n",
+    "            12:'Priority road', \n",
+    "            13:'Yield', \n",
+    "            14:'Stop', \n",
+    "            15:'No vehicles', \n",
+    "            16:'Veh > 3.5 tons prohibited', \n",
+    "            17:'No entry', \n",
+    "            18:'General caution', \n",
+    "            19:'Dangerous curve left', \n",
+    "            20:'Dangerous curve right', \n",
+    "            21:'Double curve', \n",
+    "            22:'Bumpy road', \n",
+    "            23:'Slippery road', \n",
+    "            24:'Road narrows on the right', \n",
+    "            25:'Road work', \n",
+    "            26:'Traffic signals', \n",
+    "            27:'Pedestrians', \n",
+    "            28:'Children crossing', \n",
+    "            29:'Bicycles crossing', \n",
+    "            30:'Beware of ice/snow',\n",
+    "            31:'Wild animals crossing', \n",
+    "            32:'End speed + passing limits', \n",
+    "            33:'Turn right ahead', \n",
+    "            34:'Turn left ahead', \n",
+    "            35:'Ahead only', \n",
+    "            36:'Go straight or right', \n",
+    "            37:'Go straight or left', \n",
+    "            38:'Keep right', \n",
+    "            39:'Keep left', \n",
+    "            40:'Roundabout mandatory', \n",
+    "            41:'End of no passing', \n",
+    "            42:'End no passing veh > 3.5 tons' }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define a function to preprocess input image\n",
+    "def preprocess_image(image):\n",
+    "    # Resize the image to the required dimensions\n",
+    "    image = image.resize((30, 30))\n",
+    "    # Convert the PIL image to a NumPy array\n",
+    "    image_array = np.array(image)\n",
+    "    # Normalize pixel values to be between 0 and 1\n",
+    "    image_array = image_array / 255.0\n",
+    "    # Add batch dimension to the image\n",
+    "    image_array = np.expand_dims(image_array, axis=0)\n",
+    "    return image_array\n",
+    "\n",
+    "# Define a function to make predictions\n",
+    "def predict_class(image):\n",
+    "    # Preprocess the input image\n",
+    "    processed_image = preprocess_image(image)\n",
+    "    # Make predictions using the loaded model\n",
+    "    predictions = model.predict(processed_image)\n",
+    "    # Get the predicted class index\n",
+    "    predicted_class_index = np.argmax(predictions)\n",
+    "    # Return the predicted class label and class number\n",
+    "    return f\"{class_num[predicted_class_index]} (Class {predicted_class_index})\"\n",
+    "\n",
+    "# Create Gradio interface\n",
+    "iface = gr.Interface(\n",
+    "    fn=predict_class,\n",
+    "    inputs=gr.Image(type=\"pil\", label=\"Upload an image\"),\n",
+    "    outputs=\"text\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Launch the Gradio interface\n",
+    "iface.launch()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Loading trained model
+model = tf.keras.models.load_model("model.h5")  
+
+# Label Overview
+class_num = { 0:'Speed limit (20km/h)',
+            1:'Speed limit (30km/h)', +
+            2:'Speed limit (50km/h)', 
+            3:'Speed limit (60km/h)', 
+            4:'Speed limit (70km/h)', 
+            5:'Speed limit (80km/h)', 
+            6:'End of speed limit (80km/h)', 
+            7:'Speed limit (100km/h)', 
+            8:'Speed limit (120km/h)', 
+            9:'No passing', 
+            10:'No passing veh over 3.5 tons', 
+            11:'Right-of-way at intersection', 
+            12:'Priority road', 
+            13:'Yield', 
+            14:'Stop', 
+            15:'No vehicles', 
+            16:'Veh > 3.5 tons prohibited', 
+            17:'No entry', 
+            18:'General caution', 
+            19:'Dangerous curve left', 
+            20:'Dangerous curve right', 
+            21:'Double curve', 
+            22:'Bumpy road', 
+            23:'Slippery road', 
+            24:'Road narrows on the right', 
+            25:'Road work', 
+            26:'Traffic signals', 
+            27:'Pedestrians', 
+            28:'Children crossing', 
+            29:'Bicycles crossing', 
+            30:'Beware of ice/snow',
+            31:'Wild animals crossing', 
+            32:'End speed + passing limits', 
+            33:'Turn right ahead', 
+            34:'Turn left ahead', 
+            35:'Ahead only', 
+            36:'Go straight or right', 
+            37:'Go straight or left', 
+            38:'Keep right', 
+            39:'Keep left', 
+            40:'Roundabout mandatory', 
+            41:'End of no passing', 
+            42:'End no passing veh > 3.5 tons' }
+
+# Define a function to preprocess input image
+def preprocess_image(image):
+    # Resize the image to the required dimensions
+    image = image.resize((30, 30))
+    # Convert the PIL image to a NumPy array
+    image_array = np.array(image)
+    # Normalize pixel values to be between 0 and 1
+    image_array = image_array / 255.0
+    # Add batch dimension to the image
+    image_array = np.expand_dims(image_array, axis=0)
+    return image_array
+
+# Define a function to make predictions
+def predict_class(image):
+    # Preprocess the input image
+    processed_image = preprocess_image(image)
+    # Make predictions using the loaded model
+    predictions = model.predict(processed_image)
+    # Get the predicted class index
+    predicted_class_index = np.argmax(predictions)
+    # Return the predicted class label and class number
+    return f"{class_num[predicted_class_index]} (Class {predicted_class_index})"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_class,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs="text"
+)
+
+# Launch the Gradio interface
+iface.launch()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef22e9ce57a458756d8ab375a2386c42df42d108c5e7f176f7bb189a2a56028
+size 8478464