added himalaya products

Face Problems.ipynb

@@ -1341,9 +1341,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "d7b76171",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\envs\\development\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: `enable_queue` is deprecated in `Interface()`, please use it within `launch()` instead.\n",
+      "  warnings.warn(value)\n",
+      "D:\\Anaconda\\envs\\development\\lib\\site-packages\\gradio\\deprecation.py:43: UserWarning: You have unused kwarg parameters in Blocks, please remove them: {'description': 'A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces.', 'examples': [['harmonal_acne.jpg'], ['forehead_wrinkles.jpg'], ['oily_skin.jpg']]}\n",
+      "  warnings.warn(\n",
+      "D:\\Anaconda\\envs\\development\\lib\\site-packages\\gradio\\inputs.py:256: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+      "  warnings.warn(\n",
+      "D:\\Anaconda\\envs\\development\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  warnings.warn(value)\n",
+      "D:\\Anaconda\\envs\\development\\lib\\site-packages\\gradio\\outputs.py:196: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+      "  warnings.warn(\n",
+      "D:\\Anaconda\\envs\\development\\lib\\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://127.0.0.1:7875\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7875/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<gradio.routes.App at 0x17529404130>, 'http://127.0.0.1:7875/', None)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "from fastai.vision.all import *\n",
+    "import skimage\n",
+    "import pathlib\n",
+    "import pandas as pd\n",
+    "\n",
+    "plt = platform.system()\n",
+    "if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath\n",
+    "title = \"Face condition Analyzer\"\n",
+    "description = \"A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces.\"\n",
+    "examples = [['harmonal_acne.jpg'],['forehead_wrinkles.jpg'],['oily_skin.jpg']]\n",
+    "enable_queue=True\n",
+    "\n",
+    "with gr.Blocks(title=title,description=description,examples=examples,enable_queue=enable_queue) as demo:\n",
+    "    learn = load_learner('export.pkl')\n",
+    "    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))}\n",
+    "    gr.Markdown(\"# Face Skin Analyzer\")\n",
+    "    gr.Markdown(\"A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces. Kindly upload a photo of your face.\")\n",
+    "    with gr.Row():\n",
+    "        inputs = gr.inputs.Image(shape=(512, 512))\n",
+    "        outputs = gr.outputs.Label(num_top_classes=3)\n",
+    "    btn = gr.Button(\"Predict\")\n",
+    "    btn.click(fn=predict, inputs=inputs, outputs=outputs)\n",
+    "    \n",
+    "    df=pd.read_excel(\"recommendation.xlsx\")\n",
+    "    classes = df['class'].unique()\n",
+    "    with gr.Accordion(\"Find your skin condition using above analyzer and see the Recommended solutions\",open=True):\n",
+    "        for c in classes:\n",
+    "            with gr.Accordion(c,open=False):\n",
+    "                df_temp = df[df['class']==c]\n",
+    "                with gr.Row():\n",
+    "                    for i,current_row in df_temp.iterrows():\n",
+    "                        with gr.Column():\n",
+    "                            html_box = gr.HTML(\"<a href='{}'><img src ='{}'></a>\".format(current_row['profit_link'],current_row['product_image']))                   \n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e575d70d",
+   "metadata": {},
    "outputs": [],
    "source": []
   }

app.py

@@ -6,22 +6,11 @@ import pandas as pd
 
 plt = platform.system()
 if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
-
-# 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 = "Face condition Analyzer"
 description = "A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces."
 examples = [['harmonal_acne.jpg'],['forehead_wrinkles.jpg'],['oily_skin.jpg']]
 enable_queue=True
 
-# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,
-#              description=description,examples=examples,enable_queue=enable_queue).launch()
 with gr.Blocks(title=title,description=description,examples=examples,enable_queue=enable_queue) as demo:
     learn = load_learner('export.pkl')
     labels = learn.dls.vocab
@@ -39,11 +28,12 @@ with gr.Blocks(title=title,description=description,examples=examples,enable_queu
     
     df=pd.read_excel("recommendation.xlsx")
     classes = df['class'].unique()
-    with gr.Accordion("Find your skin condition using above analyzer and see the Recommended solutions",open=False):
+    with gr.Accordion("Find your skin condition using above analyzer and see the Recommended solutions",open=True):
         for c in classes:
             with gr.Accordion(c,open=False):
                 df_temp = df[df['class']==c]
-                for i,current_row in df_temp.iterrows():
-                    html_box = gr.HTML("<span><a href='{}'><img src ='{}'></a></span>".format(current_row['profit_link'],current_row['product_image']))
-
+                with gr.Row():
+                    for i,current_row in df_temp.iterrows():
+                        with gr.Column():
+                            html_box = gr.HTML("<a href='{}'><img src ='{}'></a>".format(current_row['profit_link'],current_row['product_image']))                   
 demo.launch()