Shared in the forum

.ipynb_checkpoints/tower_parts_app-checkpoint.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 109,
    "id": "7cfefe75",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 110,
    "id": "2d33068b",
    "metadata": {},
    "outputs": [],
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 111,
    "id": "1ca4a10e",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 112,
    "id": "b3fe061e",
    "metadata": {},
    "outputs": [],
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 113,
    "id": "28688e96",
    "metadata": {},
    "outputs": [
@@ -65,7 +65,7 @@
        "PILImage mode=RGB size=168x224"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 114,
    "id": "4155e249",
    "metadata": {},
    "outputs": [
@@ -125,7 +125,7 @@
        "         7.7552e-06, 4.4144e-04]))"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 115,
    "id": "ab3ad7a3",
    "metadata": {},
    "outputs": [
@@ -147,7 +147,7 @@
        "PILImage mode=RGB size=168x224"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 116,
    "id": "0539d586",
    "metadata": {},
    "outputs": [
@@ -207,7 +207,7 @@
        "         1.5120e-02, 1.7516e-03]))"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 117,
    "id": "c6f98324",
    "metadata": {},
    "outputs": [],
@@ -233,21 +233,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 154,
    "id": "369e09cd",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
-    "title = \"Telecommunication Tower Parts Classifier\"\n",
-    "description = \"This deep learning model was trained with only 478 images of telecommunication towers' parts.\"\n",
-    "description += \"\\nThe parts the model recognize are:\"\n",
-    "description += \"\\n- Base plate / Grounding bar / Ladder / Light / Lightning rod / Platform / Transmission lines\""
+    "title = \"Classify Telecommunication Tower Parts\"\n",
+    "description = \"This deep learning model was trained with fastai using only 478 images of telecommunication towers parts.\"\n",
+    "description += \"\\nThe model was trained to recognize the following 8 categories:\"\n",
+    "description += \"\\n- Base plate | Grounding bar | Identification | Ladder | Light | Lightning rod | Platform | Transmission lines\"\n",
+    "description += \"\\n- You can test the model with the given examples (see below), or upload your own pictures.\"\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 155,
    "id": "94e69313",
    "metadata": {},
    "outputs": [
@@ -298,7 +299,7 @@
        " 'transmission_lines': 0.0017515908693894744}"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,13 +310,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 156,
    "id": "a5a2404a",
    "metadata": {},
    "outputs": [],
    "source": [
     "#export\n",
-    "image = gr.inputs.Image(shape=(200, 200), label='Load image')\n",
+    "image = gr.inputs.Image(\n",
+    "    shape=(200,200), \n",
+    "    label='Load image'\n",
+    ")\n",
     "label = gr.outputs.Label()\n",
     "\n",
     "\n",
@@ -327,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 157,
    "id": "d9af47a8",
    "metadata": {},
    "outputs": [
@@ -335,7 +339,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7873/\n",
+      "Running on local URL:  http://127.0.0.1:7888/\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -344,11 +348,11 @@
      "data": {
       "text/plain": [
        "(<fastapi.applications.FastAPI at 0x7f9f892f96a0>,\n",
-       " 'http://127.0.0.1:7873/',\n",
+       " 'http://127.0.0.1:7888/',\n",
        " None)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,7 +366,6 @@
     "    examples=examples,\n",
     "    title=title,\n",
     "    description=description,\n",
-    "    live=True,\n",
     ")\n",
     "\n",
     "intf.launch(inline=False)"
@@ -370,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 158,
    "id": "01519030",
    "metadata": {},
    "outputs": [

app.py

@@ -21,9 +21,10 @@ def classify_image(img):
 
 # Cell
 title = "Classify Telecommunication Tower Parts"
-description = "This deep learning model was trained with fastai using only 478 images of telecommunication towers' parts."
-description += "\nThe model recognizes the following 7 categories:"
+description = "This deep learning model was trained with fastai using only 478 images of telecommunication towers parts."
+description += "\nThe model was trained to recognize the following 8 categories:"
 description += "\n- Base plate | Grounding bar | Identification | Ladder | Light | Lightning rod | Platform | Transmission lines"
+description += "\n- You can test the model with the given examples (see below), or upload your own pictures."
 
 
 # Cell

tower_parts_app.ipynb

@@ -233,21 +233,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 164,
    "id": "369e09cd",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
     "title = \"Classify Telecommunication Tower Parts\"\n",
-    "description = \"This deep learning model was trained with fastai using only 478 images of telecommunication towers' parts.\"\n",
-    "description += \"\\nThe model recognizes the following 7 categories:\"\n",
-    "description += \"\\n- Base plate | Grounding bar | Identification | Ladder | Light | Lightning rod | Platform | Transmission lines\"\n"
+    "description = \"This deep learning model was trained with fastai using only 478 images of telecommunication towers parts.\"\n",
+    "description += \"\\nThe model was trained to recognize the following 8 categories:\"\n",
+    "description += \"\\n- Base plate | Grounding bar | Identification | Ladder | Light | Lightning rod | Platform | Transmission lines\"\n",
+    "description += \"\\n- You can test the model by clicking on the given examples below, or uploading your own pictures and then clicking on «Submit»\"\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 165,
    "id": "94e69313",
    "metadata": {},
    "outputs": [
@@ -298,7 +299,7 @@
        " 'transmission_lines': 0.0017515908693894744}"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 166,
    "id": "a5a2404a",
    "metadata": {},
    "outputs": [],
@@ -330,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 167,
    "id": "d9af47a8",
    "metadata": {},
    "outputs": [
@@ -338,7 +339,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7885/\n",
+      "Running on local URL:  http://127.0.0.1:7890/\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -347,11 +348,11 @@
      "data": {
       "text/plain": [
        "(<fastapi.applications.FastAPI at 0x7f9f892f96a0>,\n",
-       " 'http://127.0.0.1:7885/',\n",
+       " 'http://127.0.0.1:7890/',\n",
        " None)"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 168,
    "id": "01519030",
    "metadata": {},
    "outputs": [