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.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import cv2
+import numpy as np
+import tensorflow as tf
+
+from PIL import Image
+
+# Assuming you have already defined img_height, img_width, and class_names
+# img_height, img_width = 180, 180
+class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
+
+# Load the fine-tuned model (from local)
+resnet_model = tf.keras.models.load_model('./flower_image_classification_ResNet50_v1.0.h5')
+
+def preprocess_image(image):
+    # Convert the PIL image to an array
+    image = np.array(image)
+    
+    # Read and resize the image
+    image_resized = cv2.resize(image, (img_height, img_width))
+    
+    # Preprocess the image
+    image = np.expand_dims(image_resized, axis=0)
+    
+    # Predict with the model
+    pred = resnet_model.predict(image)
+    
+    # Get the predicted class label
+    predicted_class = np.argmax(pred)
+    output_class = class_names[predicted_class]
+    
+    # Get the confidence level (probability)
+    confidence_level = pred[0][predicted_class]
+    
+    return image_resized, output_class, confidence_level
+
+def predict(image):
+    image_resized, output_class, confidence_level = preprocess_image(image)
+    return Image.fromarray(image_resized), output_class, str(confidence_level)
+
+# Define the Gradio interface
+inputs = gr.Image(type="pil", label="Upload Image")
+outputs = [
+    gr.Image(type="pil", label="Resized Image"),
+    gr.Textbox(label="Predicted Class"),
+    gr.Textbox(label="Confidence Level")
+]
+
+# Create the Gradio Interface
+gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Flower Classification with ResNet50",
+    description="Upload an image of a flower to classify it into one of the five categories.",
+    live=True
+).launch()

app.py

@@ -1,165 +1,57 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "44c33030-a3e9-4f51-a5e8-a37de32e54e1",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7869\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7869/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IMPORTANT: You are using gradio version 4.25.0, however version 4.29.0 is available, please upgrade.\n",
-      "--------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\gradio\\queueing.py\", line 522, in process_events\n",
-      "    response = await route_utils.call_process_api(\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\gradio\\route_utils.py\", line 260, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\gradio\\blocks.py\", line 1741, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\gradio\\blocks.py\", line 1296, in call_function\n",
-      "    prediction = await anyio.to_thread.run_sync(\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
-      "    return await get_async_backend().run_sync_in_worker_thread(\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"C:\\Users\\ASUSS\\anaconda3\\envs\\bootcampai\\lib\\site-packages\\gradio\\utils.py\", line 751, in wrapper\n",
-      "    response = f(*args, **kwargs)\n",
-      "  File \"C:\\Users\\ASUSS\\AppData\\Local\\Temp\\ipykernel_3748\\1829143819.py\", line 37, in predict\n",
-      "    image_resized, output_class, confidence_level = preprocess_image(image)\n",
-      "  File \"C:\\Users\\ASUSS\\AppData\\Local\\Temp\\ipykernel_3748\\1829143819.py\", line 19, in preprocess_image\n",
-      "    image_resized = cv2.resize(image, (img_height, img_width))\n",
-      "NameError: name 'img_height' is not defined\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from PIL import Image\n",
-    "\n",
-    "# Assuming you have already defined img_height, img_width, and class_names\n",
-    "# img_height, img_width = 180, 180\n",
-    "class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']\n",
-    "\n",
-    "# Load the fine-tuned model (from local)\n",
-    "resnet_model = tf.keras.models.load_model('./flower_image_classification_ResNet50_v1.0.h5')\n",
-    "\n",
-    "def preprocess_image(image):\n",
-    "    # Convert the PIL image to an array\n",
-    "    image = np.array(image)\n",
-    "    \n",
-    "    # Read and resize the image\n",
-    "    image_resized = cv2.resize(image, (img_height, img_width))\n",
-    "    \n",
-    "    # Preprocess the image\n",
-    "    image = np.expand_dims(image_resized, axis=0)\n",
-    "    \n",
-    "    # Predict with the model\n",
-    "    pred = resnet_model.predict(image)\n",
-    "    \n",
-    "    # Get the predicted class label\n",
-    "    predicted_class = np.argmax(pred)\n",
-    "    output_class = class_names[predicted_class]\n",
-    "    \n",
-    "    # Get the confidence level (probability)\n",
-    "    confidence_level = pred[0][predicted_class]\n",
-    "    \n",
-    "    return image_resized, output_class, confidence_level\n",
-    "\n",
-    "def predict(image):\n",
-    "    image_resized, output_class, confidence_level = preprocess_image(image)\n",
-    "    return Image.fromarray(image_resized), output_class, str(confidence_level)\n",
-    "\n",
-    "# Define the Gradio interface\n",
-    "inputs = gr.Image(type=\"pil\", label=\"Upload Image\")\n",
-    "outputs = [\n",
-    "    gr.Image(type=\"pil\", label=\"Resized Image\"),\n",
-    "    gr.Textbox(label=\"Predicted Class\"),\n",
-    "    gr.Textbox(label=\"Confidence Level\")\n",
-    "]\n",
-    "\n",
-    "# Create the Gradio Interface\n",
-    "gr.Interface(\n",
-    "    fn=predict,\n",
-    "    inputs=inputs,\n",
-    "    outputs=outputs,\n",
-    "    title=\"Flower Classification with ResNet50\",\n",
-    "    description=\"Upload an image of a flower to classify it into one of the five categories.\",\n",
-    "    live=True\n",
-    ").launch()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+import gradio as gr
+import cv2
+import numpy as np
+import tensorflow as tf
+
+from PIL import Image
+
+# Assuming you have already defined img_height, img_width, and class_names
+# img_height, img_width = 180, 180
+class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
+
+# Load the fine-tuned model (from local)
+resnet_model = tf.keras.models.load_model('./flower_image_classification_ResNet50_v1.0.h5')
+
+def preprocess_image(image):
+    # Convert the PIL image to an array
+    image = np.array(image)
+    
+    # Read and resize the image
+    image_resized = cv2.resize(image, (img_height, img_width))
+    
+    # Preprocess the image
+    image = np.expand_dims(image_resized, axis=0)
+    
+    # Predict with the model
+    pred = resnet_model.predict(image)
+    
+    # Get the predicted class label
+    predicted_class = np.argmax(pred)
+    output_class = class_names[predicted_class]
+    
+    # Get the confidence level (probability)
+    confidence_level = pred[0][predicted_class]
+    
+    return image_resized, output_class, confidence_level
+
+def predict(image):
+    image_resized, output_class, confidence_level = preprocess_image(image)
+    return Image.fromarray(image_resized), output_class, str(confidence_level)
+
+# Define the Gradio interface
+inputs = gr.Image(type="pil", label="Upload Image")
+outputs = [
+    gr.Image(type="pil", label="Resized Image"),
+    gr.Textbox(label="Predicted Class"),
+    gr.Textbox(label="Confidence Level")
+]
+
+# Create the Gradio Interface
+gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Flower Classification with ResNet50",
+    description="Upload an image of a flower to classify it into one of the five categories.",
+    live=True
+).launch()

requirements.txt

@@ -1,6 +1,6 @@
-# Python 3.10.12
-gradio==4.25.0
-opencv-python==4.10.0.82
-numpy==1.26.4
-tensorflow==2.16.1
+# Python 3.10.12
+gradio==4.25.0
+opencv-python==4.10.0
+numpy==1.26.4
+tensorflow==2.16.1
 pillow==10.3.0