Project_Frontend_Gradio.ipynb

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       "(#1) [Path('export.pkl')]"
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   "source": [
    "import gradio as gr\n",
    "from fastai.vision.all import *\n",
    "\n",
    "import PIL\n",
    "from PIL import Image\n",
    "path = Path()\n",
    "path.ls(file_exts='.pkl')"
   ]
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     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
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       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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   "source": [
    "import io\n",
    "learn_inf = load_learner(path/'export.pkl')\n",
    "def predict_image (img):\n",
    "    # Convert numpy array to PIL Image\n",
    "    img = PIL.Image.fromarray(img)\n",
    "\n",
    "    # Resize the uploaded image\n",
    "    img = img.resize((224, 224))\n",
    "\n",
    "    pred,_,_ = learn_inf.predict(img)\n",
    "    \n",
    "    # Return the predicted label\n",
    "    return pred\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Define the Gradio interface\n",
    "iface = gr.Interface(\n",
    "    fn=predict_image,\n",
    "    inputs=gr.components.Image(),\n",
    "    outputs=gr.components.Textbox(label=\"Predicted type of Pneumonia\"),\n",
    "    title=\"Pneumonia classifier\",\n",
    "    description=\"Upload an image of the lung XRay\"\n",
    ")\n",
    "\n",
    "# Launch the interface\n",
    "iface.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "406d5248",
   "metadata": {},
   "outputs": [],
   "source": [
    "So we have shown the frontend above using Gradio API :)"
   ]
  }
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