fix v1

app.ipynb

@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import nbdev\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "image/png": 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",
+      "text/plain": [
+       "PILImage mode=RGB size=192x139"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im = PILImage.create('dog.png')\n",
+    "im.thumbnail((192, 192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "def is_cat(x): return x[0].isupper() \n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "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": [
+       "('False', TensorImage(0), TensorImage([1.0000e+00, 4.6968e-06]))"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('Dog', 'Cat')\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    pred, idx, probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "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": [
+       "{'Dog': 0.9999953508377075, 'Cat': 4.696836640505353e-06}"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-267 (run):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/threading.py\", line 1073, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 766, in run_closure\n",
+      "    _threading_Thread_run(self)\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/threading.py\", line 1010, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 65, in run\n",
+      "    config = self.config\n",
+      "           ^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/runners.py\", line 194, in run\n",
+      "    return runner.run(main)\n",
+      "           ^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/runners.py\", line 118, in run\n",
+      "    return self._loop.run_until_complete(task)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/base_events.py\", line 685, in run_until_complete\n",
+      "    return future.result()\n",
+      "           ^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 69, in serve\n",
+      "    self.lifespan = config.lifespan_class(config)\n",
+      "        ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 76, in _serve\n",
+      "    \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/config.py\", line 426, in load\n",
+      "    else:\n",
+      "          \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n",
+      "    \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n",
+      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1387, in _gcd_import\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1360, in _find_and_load\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1331, in _find_and_load_unlocked\n",
+      "  File \"<frozen importlib._bootstrap>\", line 935, in _load_unlocked\n",
+      "  File \"<frozen importlib._bootstrap_external>\", line 995, in exec_module\n",
+      "  File \"<frozen importlib._bootstrap>\", line 488, in _call_with_frames_removed\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/protocols/websockets/auto.py\", line 17, in <module>\n",
+      "    from uvicorn.protocols.websockets.websockets_impl import WebSocketProtocol\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py\", line 12, in <module>\n",
+      "    from websockets.legacy.server import HTTPResponse\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/websockets/legacy/server.py\", line 49, in <module>\n",
+      "    from .compatibility import loop_if_py_lt_38\n",
+      "ImportError: cannot import name 'loop_if_py_lt_38' from 'websockets.legacy.compatibility' (/home/tamdd18/miniconda3/lib/python3.12/site-packages/websockets/legacy/compatibility.py)\n",
+      "Exception in thread Thread-268 (run):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/threading.py\", line 1073, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 766, in run_closure\n",
+      "    _threading_Thread_run(self)\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/threading.py\", line 1010, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 65, in run\n",
+      "    config = self.config\n",
+      "           ^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/runners.py\", line 194, in run\n",
+      "    return runner.run(main)\n",
+      "           ^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/runners.py\", line 118, in run\n",
+      "    return self._loop.run_until_complete(task)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/asyncio/base_events.py\", line 685, in run_until_complete\n",
+      "    return future.result()\n",
+      "           ^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 69, in serve\n",
+      "    self.lifespan = config.lifespan_class(config)\n",
+      "        ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/server.py\", line 76, in _serve\n",
+      "    \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/config.py\", line 426, in load\n",
+      "    else:\n",
+      "          \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n",
+      "    \n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n",
+      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1387, in _gcd_import\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1360, in _find_and_load\n",
+      "  File \"<frozen importlib._bootstrap>\", line 1331, in _find_and_load_unlocked\n",
+      "  File \"<frozen importlib._bootstrap>\", line 935, in _load_unlocked\n",
+      "  File \"<frozen importlib._bootstrap_external>\", line 995, in exec_module\n",
+      "  File \"<frozen importlib._bootstrap>\", line 488, in _call_with_frames_removed\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/protocols/websockets/auto.py\", line 17, in <module>\n",
+      "    from uvicorn.protocols.websockets.websockets_impl import WebSocketProtocol\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py\", line 12, in <module>\n",
+      "    from websockets.legacy.server import HTTPResponse\n",
+      "  File \"/home/tamdd18/miniconda3/lib/python3.12/site-packages/websockets/legacy/server.py\", line 49, in <module>\n",
+      "    from .compatibility import loop_if_py_lt_38\n",
+      "ImportError: cannot import name 'loop_if_py_lt_38' from 'websockets.legacy.compatibility' (/home/tamdd18/miniconda3/lib/python3.12/site-packages/websockets/legacy/compatibility.py)\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[74], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m examples \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdog.png\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcat.png\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      6\u001b[0m intf \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39mInterface(fn\u001b[38;5;241m=\u001b[39mclassify_image, inputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m'\u001b[39m, outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m, examples\u001b[38;5;241m=\u001b[39mexamples)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mintf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlaunch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/gradio/blocks.py:2480\u001b[0m, in \u001b[0;36mlaunch\u001b[0;34m(self, inline, inbrowser, share, debug, max_threads, auth, auth_message, prevent_thread_lock, show_error, server_name, server_port, height, width, favicon_path, ssl_keyfile, ssl_certfile, ssl_keyfile_password, ssl_verify, quiet, show_api, allowed_paths, blocked_paths, root_path, app_kwargs, state_session_capacity, share_server_address, share_server_protocol, auth_dependency, max_file_size, enable_monitoring, strict_cors, node_server_name, node_port, ssr_mode, _frontend)\u001b[0m\n\u001b[1;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/gradio/http_server.py:149\u001b[0m, in \u001b[0;36mstart_server\u001b[0;34m(app, server_name, server_port, ssl_keyfile, ssl_certfile, ssl_keyfile_password)\u001b[0m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/gradio/http_server.py:57\u001b[0m, in \u001b[0;36mrun_in_thread\u001b[0;34m(self)\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "image = gr.components.Image(height=192, width=192)\n",
+    "label = gr.components.Label()\n",
+    "examples = ['dog.png', 'cat.png']\n",
+    "\n",
+    "intf = gr.Interface(fn=classify_image, inputs='image', outputs='label', examples=examples)\n",
+    "intf.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('app.ipynb', '')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c09d8f778f89c9eb588eefd9a5ba094eb163cd3ea708a4ea567a83e924f149f
+size 47059947

requirements.txt

@@ -0,0 +1,3 @@
+fastai==2.7.17
+gradio==5.0.0
+nbdev==2.3.31

test.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def image_classifier(inp):
+    return {'cat': 0.3, 'dog': 0.7}
+
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()