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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"f:\\miniconda3\\envs\\btl\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import gradio as gd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"def greet(name, intensity):\n",
" return \"Hello, \" + name + \"!\" * int(intensity)\n",
"\n",
"demo = gr.Interface(\n",
" fn=greet,\n",
" inputs=[\"text\", \"slider\"],\n",
" outputs=[\"text\"],\n",
")\n",
"\n",
"demo.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7904\n",
"\n",
"Could not create share link. Missing file: f:\\miniconda3\\envs\\btl\\Lib\\site-packages\\gradio\\frpc_windows_amd64_v0.2. \n",
"\n",
"Please check your internet connection. This can happen if your antivirus software blocks the download of this file. You can install manually by following these steps: \n",
"\n",
"1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_windows_amd64.exe\n",
"2. Rename the downloaded file to: frpc_windows_amd64_v0.2\n",
"3. Move the file to this location: f:\\miniconda3\\envs\\btl\\Lib\\site-packages\\gradio\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7904/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# #setup\n",
"# model_path = \"KAITANY/finetuned-roberta-base-sentiment\"\n",
"\n",
"# tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
"# #config = AutoConfig.from_pretrained(model_path)\n",
"# model = AutoModelForSequenceClassification.from_pretrained(model_path)\n",
"\n",
"# def preprocess(text):\n",
"# # Preprocess text (username and link placeholders)\n",
"# new_text = []\n",
"# for t in text.split(\" \"):\n",
"# t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
"# t = 'http' if t.startswith('http') else t\n",
"# new_text.append(t)\n",
"# return \" \".join(new_text)\n",
"\n",
"# def sentiment_analysis(text):\n",
" # text = preprocess(text)\n",
"\n",
" # # Tokenize the text\n",
" # inputs = tokenizer(text, return_tensors=\"pt\", padding=True)\n",
"\n",
" # # Make a prediction\n",
" # with torch.no_grad():\n",
" # outputs = model(**inputs)\n",
"\n",
" # # Get the predicted class probabilities\n",
" # scores = torch.softmax(outputs.logits, dim=1).tolist()[0]\n",
" # # Map the scores to labels\n",
" # labels = ['Negative', 'Neutral', 'Positive']\n",
" # scores_dict = {label: score for label, score in zip(labels, scores)}\n",
"\n",
" # return scores_dict\n",
"#demo\n",
"aspects = ['General', 'Battery', 'Performance', 'Camera', 'Ser&Acc', 'Others', 'Design', 'Screen', 'Features', 'Price']\n",
"aspects_ratio = (np.random.dirichlet(np.ones(10), size=1) * 100).flatten()\n",
"\n",
"sentiments_ratio = (np.random.dirichlet(np.ones(3), size=1) * 100).flatten()\n",
"sentiments = ['Positive', 'Negative', 'Neutral']\n",
"\n",
"aspects_polarity = []\n",
"aspects_polarity_ratio = (np.random.dirichlet(np.ones(30), size=1) * 100).flatten()\n",
"for aspect in aspects:\n",
" for sentiment in sentiments:\n",
" aspects_polarity.append(aspect + '#' + sentiment) \n",
"\n",
"def sentiment_analysis(text, aspect):\n",
"\n",
" # Tạo biểu đồ cảm xúc theo aspect\n",
" pie_sentiments_of_an_aspect = draw_pie_sentiments_of_an_aspect(aspect)\n",
"\n",
" #Biểu đồ aspect\n",
" pie_of_all_aspect = draw_pie_of_aspect()\n",
"\n",
" #Biểu đồ aspect#polirity\n",
" pie_aspect_polarity = draw_pie_aspect_polarity()\n",
" # return [pie_aspect, pie_all_aspect, pie_aspect_polarity]\n",
" return pie_sentiments_of_an_aspect, pie_of_all_aspect, pie_aspect_polarity\n",
"\n",
"def draw_pie_sentiments_of_an_aspect(aspect):\n",
" sentiments_ratio = (np.random.dirichlet(np.ones(3), size=1) * 100).flatten()\n",
" pie_sentiments_of_an_aspect = plt.figure(figsize=(5,5))\n",
" plt.pie(sentiments_ratio, labels=sentiments, autopct='%1.1f%%', startangle=140)\n",
" return pie_sentiments_of_an_aspect\n",
"\n",
"def draw_pie_of_aspect():\n",
" pie_aspect = plt.figure(figsize=(5,5))\n",
" plt.pie(aspects_ratio, labels=aspects, autopct='%1.1f%%', startangle=140)\n",
" return pie_aspect\n",
"\n",
"def draw_pie_aspect_polarity():\n",
" pie_aspect_polarity = plt.figure(figsize=(20, 10))\n",
" plt.pie(aspects_polarity_ratio, labels=aspects_polarity, autopct='%1.1f%%', startangle=140)\n",
" plt.legend(aspects_polarity, loc='upper right', bbox_to_anchor=(1.5, 1.))\n",
" return pie_aspect_polarity\n",
"\n",
"def submit(comment, aspect):\n",
" return sentiment_analysis(comment, aspect)\n",
" \n",
"title = \"Sentiment Analysis Application\\n\\n\\nThis application assesses if a twitter post relating to vaccination is positive,neutral or negative\"\n",
"with gr.Blocks() as demo:\n",
" with gr.Row():\n",
" text_box = gr.Textbox(placeholder=\"Write your comment here...\", visible=True, label=\"Comment\")\n",
" submit_btn = gr.Button(\"Submit\")\n",
" with gr.Row():\n",
" with gr.Column():\n",
" choose_aspect_dropdown = gr.Dropdown(\n",
" choices=['General', 'Battery', 'Performance', 'Camera', 'Ser&Acc', 'Others', 'Design', 'Screen', 'Features', 'Price'], \n",
" label=\"Choose Aspect\",\n",
" value='General'\n",
" )\n",
" pie_sentiment = gr.Plot()\n",
" pie_all_aspect = gr.Plot()\n",
" pie_aspect_polarity = gr.Plot(min_width=2000)\n",
" # demo.fn(draw_pie_aspect(choose_aspect_dropdown))\n",
"\n",
" choose_aspect_dropdown.select(\n",
" fn = draw_pie_sentiments_of_an_aspect,\n",
" inputs = [choose_aspect_dropdown],\n",
" outputs = [pie_sentiment],\n",
" )\n",
"\n",
" submit_btn.click(\n",
" fn = submit,\n",
" inputs = [text_box, choose_aspect_dropdown],\n",
" outputs = [pie_sentiment, pie_all_aspect, pie_aspect_polarity],\n",
")\n",
"\n",
"\n",
"# demo = gr.Interface(\n",
"# fn=sentiment_analysis,\n",
"# inputs=gr.Textbox(placeholder=\"Write your tweet here...\"),\n",
"# outputs=gr.Plot(),\n",
"# examples=[[\"The Vaccine is harmful!\"],[\"I cant believe people don't vaccinate their kids\"],[\"FDA think just not worth the AE unfortunately\"],[\"For a vaccine given to healthy\"]],\n",
"# title=title\n",
"# )\n",
"\n",
"demo.launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7881\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7881/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"with gr.Blocks() as demo:\n",
" error_box = gr.Textbox(label=\"Error\", visible=False)\n",
"\n",
" name_box = gr.Textbox(label=\"Name\")\n",
" age_box = gr.Number(label=\"Age\", minimum=0, maximum=100)\n",
" symptoms_box = gr.CheckboxGroup([\"Cough\", \"Fever\", \"Runny Nose\"])\n",
" submit_btn = gr.Button(\"Submit\")\n",
"\n",
" with gr.Column(visible=False) as output_col:\n",
" diagnosis_box = gr.Textbox(label=\"Diagnosis\")\n",
" patient_summary_box = gr.Textbox(label=\"Patient Summary\")\n",
"\n",
" def submit(name, age, symptoms):\n",
" if len(name) == 0:\n",
" return {error_box: gr.Textbox(value=\"Enter name\", visible=True)}\n",
" return {\n",
" output_col: gr.Column(visible=True),\n",
" diagnosis_box: \"covid\" if \"Cough\" in symptoms else \"flu\",\n",
" patient_summary_box: f\"{name}, {age} y/o\",\n",
" }\n",
"\n",
" submit_btn.click(\n",
" submit,\n",
" [name_box, age_box, symptoms_box],\n",
" [error_box, diagnosis_box, patient_summary_box, output_col],\n",
" )\n",
"\n",
"demo.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "btl",
"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
}
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