Update app.py

app.py

@@ -1,305 +1,77 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "import tensorflow as tf\n",
-    "import opendatasets as od\n",
-    "import numpy as np\n",
-    "from tensorflow.keras import layers\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\n",
-    "\n",
-    "tf.get_logger().setLevel('ERROR')\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"blanchefort/rubert-base-cased-sentiment-rurewiews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(\"blanchefort/rubert-base-cased-sentiment-rurewiews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipe = pipeline(\"text-classification\", model=\"blanchefort/rubert-base-cased-sentiment-rurewiews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
+import tensorflow as tf
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import opendatasets as od
+import gradio as gr
+import pandas as pd
+import plotly.express as plt
+
+tf.get_logger().setLevel("ERROR")
+
+# create tokenizer from pre-trained model
+tokenizer = AutoTokenizer.from_pretrained(
+    "blanchefort/rubert-base-cased-sentiment-rurewiews"
+)
+# Load the model
+model = AutoModelForSequenceClassification.from_pretrained(
+    "blanchefort/rubert-base-cased-sentiment-rurewiews"
+)
+# Create a pipeline for the model
+pipe = pipeline(
+    "text-classification", model="blanchefort/rubert-base-cased-sentiment-rurewiews"
+)
+
+# load review from open dataset
+od.download_kaggle_dataset("vigneshwarsofficial/reviews", data_dir="restaurent_review")
+prediction_data = pd.read_csv(
+    "restaurent_review/reviews/Restaurant_Reviews.tsv", delimiter="\t"
+)
+# popping irrelevant coloumn
+prediction_data.pop("Liked")
+# making a list
+data = list(prediction_data["Review"])
+# making prediction using pipe
+results = pipe(data)
+
+# Categorizing result
+positive_counter = 0
+negative_counter = 0
+neutral_counter = 0
+for x in results:
+    if x["label"] == "POSITIVE":
+        positive_counter = positive_counter + 1
+    elif x["label"] == "NEGATIVE":
+        negative_counter = negative_counter + 1
+    else:
+        neutral_counter = neutral_counter + 1
+
+result_data = pd.DataFrame(
     {
-     "data": {
-      "text/plain": [
-       "[{'label': 'NEUTRAL', 'score': 0.5040543079376221}]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
+        "count": [positive_counter, negative_counter, neutral_counter],
+        "sentiment": ["Positive", "Negative", "Neutral"],
     }
-   ],
-   "source": [
-    "pipe(\"Product is ok ok sounds also good but I am not feeling like 525 watts and weight also very less 525 watts means atleast 8kg or 10kg will be there and in paper they mentioned 5 modes are different on speaker what they given modes are different totally confused with this product\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skipping, found downloaded files in \"restaurent_review\\reviews\" (use force=True to force download)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "import pandas as pd\n",
-    "import opendatasets as od\n",
-    "\n",
-    "od.download_kaggle_dataset('vigneshwarsofficial/reviews', data_dir='restaurent_review')\n",
-    "prediction_data = pd.read_csv('restaurent_review/reviews/Restaurant_Reviews.tsv',  delimiter='\\t')\n",
-    "\n",
-    "prediction_data.pop(\"Liked\")\n",
-    "\n",
-    "data = list(prediction_data[\"Review\"])\n",
-    "results = pipe(data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Wow... Loved this place.',\n",
-       " 'Crust is not good.',\n",
-       " 'Not tasty and the texture was just nasty.',\n",
-       " 'Stopped by during the late May bank holiday off Rick Steve recommendation and loved it.',\n",
-       " 'The selection on the menu was great and so were the prices.']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>sentiment</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>283</td>\n",
-       "      <td>Positive</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>419</td>\n",
-       "      <td>Negative</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>298</td>\n",
-       "      <td>Neutral</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   count sentiment\n",
-       "0    283  Positive\n",
-       "1    419  Negative\n",
-       "2    298   Neutral"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "positive_counter = 0 \n",
-    "negative_counter = 0\n",
-    "neutral_counter = 0\n",
-    "for x in results:\n",
-    "    if x['label'] == 'POSITIVE':\n",
-    "        positive_counter = positive_counter + 1\n",
-    "    elif x['label'] == 'NEGATIVE':\n",
-    "        negative_counter = negative_counter + 1\n",
-    "    else:\n",
-    "        neutral_counter = neutral_counter + 1\n",
-    "\n",
-    "result_data = pd.DataFrame({\n",
-    "    'count': [positive_counter, negative_counter, neutral_counter],\n",
-    "    'sentiment': ['Positive', 'Negative', 'Neutral']\n",
-    "})\n",
-    "\n",
-    "result_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7866\n",
-      "Running on public URL: https://af39480688d32c192a.gradio.live\n",
-      "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"https://af39480688d32c192a.gradio.live\" 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": [
-    "single_input_demo = gr.Interface.from_pipeline(pipe, title=\"Sentiment Analysis\")\n",
-    "single_input_demo.launch(share=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7867\n",
-      "Running on public URL: https://4ce90a546387218f07.gradio.live\n",
-      "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"https://4ce90a546387218f07.gradio.live\" 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": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import plotly.express as plt\n",
-    "\n",
-    "def plotly_plot(): \n",
-    "    p = plt.bar(result_data, x='sentiment', y='count', title='Restaurent Review Analysis', color=\"count\")\n",
-    "    return p\n",
-    "\n",
-    "# show the results\n",
-    "outputs = gr.Plot()\n",
-    "\n",
-    "demo = gr.Interface(fn=plotly_plot, inputs=None, outputs=outputs, title=\"Restaurant Customer Review Sentiment Analysis\")\n",
-    "\n",
-    "demo.launch(share=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+)
+
+
+# create bar chart interface on gradio
+def plotly_plot():
+    p = plt.bar(
+        result_data,
+        x="sentiment",
+        y="count",
+        title="Restaurent Review Analysis",
+        color="count",
+    )
+    return p
+
+
+# show the results
+outputs = gr.Plot()
+
+demo = gr.Interface(
+    fn=plotly_plot,
+    inputs=None,
+    outputs=outputs,
+    title="Restaurant Customer Review Sentiment Analysis",
+)
+
+demo.launch()