app.py

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     "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"
     ]
    },
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     "text": [
      "Running on local URL:  http://127.0.0.1:7869\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
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     "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"
   ]
  }
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