code changes

Methane_Classifier/app.py

@@ -0,0 +1,38 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../methane-plume-classification-yes-or-no.ipynb.
+
+# %% auto 0
+__all__ = ['temp', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 2
+import warnings
+warnings.filterwarnings('ignore')
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 3
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 4
+import fastbook
+import pandas as pd
+import numpy as np
+import os 
+
+from fastbook import *
+from fastai.vision.widgets import *
+import gradio as gr
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 20
+categories = ('No Plume', 'Plume')
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 22
+image = gr.inputs.Image(shape = (152, 152))
+label = gr.outputs.Label()
+examples = ['plume1.png', 'plume2.png', 'noplume1.png', 'noplume2.png']
+
+intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
+intf.launch(inline = False)

app.py

@@ -1,7 +1,38 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../methane-plume-classification-yes-or-no.ipynb.
+
+# %% auto 0
+__all__ = ['temp', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 2
+import warnings
+warnings.filterwarnings('ignore')
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 3
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 4
+import fastbook
+import pandas as pd
+import numpy as np
+import os 
+
+from fastbook import *
+from fastai.vision.widgets import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% ../methane-plume-classification-yes-or-no.ipynb 20
+categories = ('No Plume', 'Plume')
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../methane-plume-classification-yes-or-no.ipynb 22
+image = gr.inputs.Image(shape = (152, 152))
+label = gr.outputs.Label()
+examples = ['plume1.png', 'plume2.png', 'noplume1.png', 'noplume2.png']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
+intf.launch(inline = False)

methane-plume-classification-yes-or-no.ipynb

@@ -36,6 +36,40 @@
     "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "import pathlib\n",
+    "temp = pathlib.PosixPath\n",
+    "pathlib.PosixPath = pathlib.WindowsPath"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -51,7 +85,8 @@
    },
    "outputs": [],
    "source": [
-    "!pip install fastbook\n",
+    "#|export\n",
+    "\n",
     "import fastbook\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -62,6 +97,17 @@
     "import gradio as gr"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = PILImage.create('plume1.png')\n",
+    "im.thumbnail((182,192))\n",
+    "im"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -280,6 +326,85 @@
     "learn.export('methane plume detect.pkl')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "learn = load_learner('methane plume detect.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "categories = ('No Plume', 'Plume')\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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "image = gr.inputs.Image(shape = (152, 152))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['plume1.png', 'plume2.png', 'noplume1.png', 'noplume2.png']\n",
+    "\n",
+    "intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)\n",
+    "intf.launch(inline = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Export successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('methane-plume-classification-yes-or-no.ipynb', 'app')\n",
+    "print('Export successful')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,