{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=224x224"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('images2.jpeg')\n",
    "\n",
    "im2 = PILImage.create('normal_brain_mri.jpeg')\n",
    "\n",
    "im2.thumbnail((224,224))\n",
    "\n",
    "im2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('yes', TensorBase(1), TensorBase([0.1362, 0.8638]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn = load_learner('brain_tumor_prediction_model.pkl')\n",
    "\n",
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorBase([0.7014, 0.2986])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'No tumor detected': 0.7013819217681885,\n",
       " 'A tumor was detected': 0.2986180782318115}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = ('No tumor detected', 'A tumor was detected')\n",
    "\n",
    "def classify_image(img):\n",
    "    pred, pred_idx, probs = learn.predict(img)\n",
    "    print (probs)\n",
    "    return {categories[i]: float(probs[i]) for i in range(2)}\n",
    "    # return dict(zip(categories, map(float, probs)))\n",
    "\n",
    "classify_image(im2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/inputs.py:258: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  \"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\",\n",
      "/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/outputs.py:198: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  \"Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\",\n",
      "/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
      "  warnings.warn(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "\n",
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       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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      "TensorBase([0.0823, 0.9177])\n"
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     "name": "stderr",
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     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/routes.py\", line 343, in run_predict\n",
      "    event_id=event_id,\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/blocks.py\", line 1016, in process_api\n",
      "    fn_index, inputs, iterator, request, event_id\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/gradio/blocks.py\", line 834, in call_function\n",
      "    fn, *processed_input, limiter=self.limiter\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/anyio/to_thread.py\", line 32, in run_sync\n",
      "    func, *args, cancellable=cancellable, limiter=limiter\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"/var/folders/ft/4l_3t3w92_371mw5w18zm63c0000gn/T/ipykernel_82092/2955207862.py\", line 4, in classify_image\n",
      "    pred, pred_idx, probs = learn.predict(img)\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/fastai/learner.py\", line 312, in predict\n",
      "    dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/fastai/data/core.py\", line 533, in test_dl\n",
      "    ) if isinstance(self.valid_ds, (Datasets, TfmdLists)) else test_items\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/fastai/data/core.py\", line 511, in test_set\n",
      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/fastai/data/core.py\", line 511, in <listcomp>\n",
      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
      "  File \"/Users/joaquinlopez/miniconda3/envs/hf/lib/python3.7/site-packages/fastai/data/core.py\", line 405, in infer_idx\n",
      "    assert idx < len(self.types), f\"Expected an input of type in \\n{pretty_types}\\n but got {type(x)}\"\n",
      "AssertionError: Expected an input of type in \n",
      "  - <class 'pathlib.PosixPath'>\n",
      "  - <class 'pathlib.Path'>\n",
      "  - <class 'str'>\n",
      "  - <class 'torch.Tensor'>\n",
      "  - <class 'numpy.ndarray'>\n",
      "  - <class 'bytes'>\n",
      "  - <class 'fastai.vision.core.PILImage'>\n",
      " but got <class 'NoneType'>\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "image = gr.inputs.Image(shape=(224,224))\n",
    "label = gr.outputs.Label()\n",
    "examples = [\n",
    "    'normal_brain_mri.jpeg', 'images2.jpeg'\n",
    "]\n",
    "\n",
    "gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples).launch(inline=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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