my first model deployment project

app.py

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+import gradio as gr
+from fastcore.all import *
+from  fastai.vision.all import *
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+
+# iface.launch()
+
+learn = load_learner('export.pkl')
+
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "my little food Classifier"
+description = "My first foodclassifier trained one some food I know. Made to learn using FastAI and deploying models."
+examples = ['barfi.webp',"brocoli.jfif","dal.jpg","gulabjamun.jfif","jalebi.jpg","ladoo.webp","pakora.webp","samosa.webp"]
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=5),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:400942e94af445f079eb2f58b59e6e44ffd921af76380c9105154a70c5b41a04
+size 102903379

gradio_notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'fastai'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn [2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfastai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mvision\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mall\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
+     ]
+    }
+   ],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn = load_learner('export.pkl')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = learn.dls.vocab\n",
+    "def predict(img):\n",
+    "    img = PILImage.create(img)\n",
+    "    pred,pred_idx,probs = learn.predict(img)\n",
+    "    return {labels[i]: float(probs[i]) for i in range(len(labels))}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('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": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b781f973879b6653a182b86dce637bbad8607b90046e75a81b5febd27741eaed"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+#fastai