Sentiment_Analysis_App_Deployment.ipynb

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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install dash"
   ]
  },
  {
   "cell_type": "code",
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   "id": "795c73c3-237b-4b2d-85b6-ab4aab1bfed9",
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     ]
    }
   ],
   "source": [
    "pip install spacy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6ad51a2-4b14-45c2-b39a-93f8f5112778",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dash\n",
    "from dash import dcc, html\n",
    "from dash.dependencies import Input, Output\n",
    "import pickle\n",
    "import os\n",
    "import spacy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0acec8c6-5638-46b8-b7f0-45ec4e793611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: spacy<3.8.0,>=3.7.2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from en-core-web-sm==3.7.1) (3.7.4)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.3)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.2)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.3.4)\n",
      "Requirement already satisfied: typer<0.10.0,>=0.3.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.9.0)\n",
      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (5.2.1)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.65.0)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.31.0)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.10.12)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (68.2.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (23.1)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.3.0)\n",
      "Requirement already satisfied: numpy>=1.19.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.26.4)\n",
      "Requirement already satisfied: typing-extensions>=4.2.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.9.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.2.2)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4)\n",
      "Requirement already satisfied: colorama in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from tqdm<5.0.0,>=4.38.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.4.6)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
      "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.3)\n",
      "\u001b[38;5;2m[+] Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download en_core_web_sm\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a78443c-97a3-4c55-84e9-153b14e07e5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: afinn in c:\\users\\great woman\\newfoldercmd\\lib\\site-packages (0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install afinn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b05b8c9-f046-4bdb-bd53-8c19eb8e909f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Initialize classifiers\n",
    "logreg_model = LogisticRegression()\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "mlp_model = MLPClassifier(random_state=42)\n",
    "svm_model = SVC(kernel='linear', random_state=42)\n",
    "\n",
    "\n",
    "# Save SVM model\n",
    "with open('svm_model.pkl', 'wb') as file:\n",
    "    pickle.dump(svm_model, file)\n",
    "\n",
    "# Save logistic regression model\n",
    "with open('logistic_regression_model.pkl', 'wb') as file:\n",
    "    pickle.dump(logreg_model, file)\n",
    "\n",
    "# Initialize results dictionary\n",
    "results = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "585565fb-6ad3-4ba7-a7f2-0044bda8b888",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DASH APP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ba0ca27-afa9-4854-b555-596f477a40ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "# Load the SVM model\n",
    "svm_model_path = \"C:\\\\Users\\\\Great Woman\\\\svm_model.pkl\"\n",
    "\n",
    "# Check if the file exists\n",
    "if os.path.exists(svm_model_path):\n",
    "    with open(svm_model_path, 'rb') as file:\n",
    "        svm_model = pickle.load(file)\n",
    "else:\n",
    "    print(f\"Error: File 'svm_model.pkl' not found in directory 'C:\\\\Users\\\\Great Woman'.\")\n",
    "    svm_model = None  # Assigning None to svm_model to handle the case when file loading fails\n",
    "\n",
    "# Load the logistic regression model\n",
    "logistic_model_path = \"C:\\\\Users\\\\Great Woman\\\\logistic_regression_model.pkl\"\n",
    "\n",
    "# Check if the file exists\n",
    "if os.path.exists(logistic_model_path):\n",
    "    with open(logistic_model_path, 'rb') as file:\n",
    "        logistic_model = pickle.load(file)\n",
    "else:\n",
    "    print(f\"Error: File 'logistic_regression_model.pkl' not found in directory 'C:\\\\Users\\\\Great Woman'.\")\n",
    "    logistic_model = None  # Assigning None to logistic_model to handle the case when file loading fails\n",
    "\n",
    "# Check if both models were loaded successfully\n",
    "if svm_model is not None and logistic_model is not None:\n",
    "    print(\"Models loaded successfully!\")\n",
    "else:\n",
    "    print(\"Error: Failed to load one or both models.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e341a25f-981a-4561-b26e-5e9153fb31b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\Great\n",
      "[nltk_data]     Woman\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to C:\\Users\\Great\n",
      "[nltk_data]     Woman\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\Great\n",
      "[nltk_data]     Woman\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  marketplace  customer_id       review_id  product_id  product_parent  \\\n",
      "0          US     11555559  R1QXC7AHHJBQ3O  B00IKPX4GY         2693241   \n",
      "1          US     31469372  R175VSRV6ZETOP  B00IKPYKWG         2693241   \n",
      "2          US     26843895  R2HRFF78MWGY19  B00IKPW0UA         2693241   \n",
      "3          US     19844868   R8Q39WPKYVSTX  B00LCHSHMS         2693241   \n",
      "4          US      1189852  R3RL4C8YP2ZCJL  B00IKPZ5V6         2693241   \n",
      "\n",
      "                           product_title product_category  star_rating  \\\n",
      "0  Fire HD 7, 7\" HD Display, Wi-Fi, 8 GB               PC            5   \n",
      "1  Fire HD 7, 7\" HD Display, Wi-Fi, 8 GB               PC            3   \n",
      "2  Fire HD 7, 7\" HD Display, Wi-Fi, 8 GB               PC            5   \n",
      "3  Fire HD 7, 7\" HD Display, Wi-Fi, 8 GB               PC            4   \n",
      "4  Fire HD 7, 7\" HD Display, Wi-Fi, 8 GB               PC            5   \n",
      "\n",
      "   helpful_votes  total_votes vine verified_purchase  \\\n",
      "0              0            0    N                 Y   \n",
      "1              0            0    N                 N   \n",
      "2              0            0    N                 Y   \n",
      "3              0            0    N                 N   \n",
      "4              0            0    N                 Y   \n",
      "\n",
      "                                     review_headline  \\\n",
      "0                                         Five Stars   \n",
      "1  Lots of ads Slow processing speed Occasionally...   \n",
      "2                            Well thought out device   \n",
      "3  Not all apps/games we were looking forward to ...   \n",
      "4                                         Five Stars   \n",
      "\n",
      "                                         review_body review_date  sentiment  \\\n",
      "0                                      Great love it  2015-08-31          1   \n",
      "1  Lots of ads<br />Slow processing speed<br />Oc...  2015-08-31          0   \n",
      "2  Excellent unit.  The versatility of this table...  2015-08-31          1   \n",
      "3  I bought this on Amazon Prime so I ended up bu...  2015-08-31          1   \n",
      "4  All Amazon products continue to meet my expect...  2015-08-31          1   \n",
      "\n",
      "                                 preprocessed_review  \n",
      "0                                         great love  \n",
      "1  lot ad < br / > slow processing speed < br / >...  \n",
      "2  excellent unit . versatility tablet , besides ...  \n",
      "3  bought amazon prime ended buying 16gb one $ 95...  \n",
      "4           amazon product continue meet expectation  \n",
      "Shape of TF-IDF matrix: (30842, 3950)\n",
      "Selected Feature Names: ['00' '000' '10' ... 'yesterday' 'youtube' 'zero']\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "\n",
    "# Download NLTK resources (only required once)\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "# Initialize NLTK's WordNet lemmatizer and stopwords\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Function to preprocess text\n",
    "def preprocess_text(text):\n",
    "    # Tokenization\n",
    "    tokens = word_tokenize(text)\n",
    "    # Stopword removal and lemmatization\n",
    "    filtered_tokens = [lemmatizer.lemmatize(token.lower()) for token in tokens if token.lower() not in stop_words]\n",
    "    # Join the tokens back into a single string\n",
    "    preprocessed_text = ' '.join(filtered_tokens)\n",
    "    return preprocessed_text\n",
    "\n",
    "# Define the file path\n",
    "file_path = r\"C:\\Users\\Great Woman\\Downloads\\Amazon Product Review.txt\"\n",
    "\n",
    "# Read the data into a DataFrame\n",
    "df = pd.read_csv(file_path, delimiter=',')\n",
    "\n",
    "# Drop rows with missing values in the 'review_body' column\n",
    "df.dropna(subset=['review_body'], inplace=True)\n",
    "\n",
    "# Apply the preprocessing function to the 'review' column\n",
    "df['preprocessed_review'] = df['review_body'].apply(preprocess_text)\n",
    "\n",
    "# Display the first few rows of the DataFrame with preprocessed text\n",
    "print(df.head())\n",
    "\n",
    "\n",
    "\n",
    "# Initialize the TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.8, min_df=5, stop_words='english')\n",
    "\n",
    "# Fit and transform the preprocessed text data\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(df['preprocessed_review'])\n",
    "\n",
    "# Define the number of features you want to select\n",
    "k_features = 2000\n",
    "\n",
    "# Initialize SelectKBest with the chi-square (chi2) statistical test\n",
    "selector = SelectKBest(score_func=chi2, k=k_features)\n",
    "\n",
    "# Fit the selector to the TF-IDF matrix\n",
    "tfidf_matrix_selected = selector.fit_transform(tfidf_matrix, df['sentiment'])\n",
    "\n",
    "# Get the indices of the selected features\n",
    "selected_feature_indices = selector.get_support(indices=True)\n",
    "\n",
    "# Get the selected feature names from the TfidfVectorizer\n",
    "selected_feature_names = tfidf_vectorizer.get_feature_names_out()[selected_feature_indices]\n",
    "\n",
    "# Save TF-IDF vectorizer\n",
    "with open('tfidf_vectorizer.pkl', 'wb') as file:\n",
    "    pickle.dump(tfidf_vectorizer, file)\n",
    "\n",
    "# Save selected features\n",
    "with open('selected_features.pkl', 'wb') as file:\n",
    "    pickle.dump(selected_feature_names, file)\n",
    "\n",
    "# Print the shape of the TF-IDF matrix\n",
    "print(\"Shape of TF-IDF matrix:\", tfidf_matrix.shape)\n",
    "# Print the selected feature names\n",
    "print(\"Selected Feature Names:\", selected_feature_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e640c0bd-5e0e-482e-af25-fdaacae1f391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x23993245310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dash\n",
    "from dash import dcc, html\n",
    "from dash.dependencies import Input, Output\n",
    "from afinn import Afinn\n",
    "\n",
    "# Initialize Dash app\n",
    "app = dash.Dash(__name__)\n",
    "\n",
    "# Load AFINN lexicon\n",
    "afinn = Afinn()\n",
    "\n",
    "# Define app layout\n",
    "app.layout = html.Div([\n",
    "    html.H1('Amazon Sentiment Analysis App', style={'color': 'navy'}),\n",
    "    html.Div([\n",
    "        html.P('Enter your Amazon product review below:', style={'margin-bottom': '5px'}),\n",
    "        dcc.Textarea(\n",
    "            id='input-text',\n",
    "            placeholder='Type here...',\n",
    "            style={'width': '100%', 'height': 200},\n",
    "            value=''  # Provide a default value to ensure the component exists\n",
    "        ),\n",
    "        html.Button('Analyze', id='analyze-btn', style={'margin-top': '10px'}),\n",
    "    ], style={'margin-bottom': '20px'}),\n",
    "    html.Div(id='output', style={'font-size': '18px', 'font-weight': 'bold'})\n",
    "])\n",
    "\n",
    "# Define callback to handle analysis button click\n",
    "@app.callback(\n",
    "    Output('output', 'children'),\n",
    "    [Input('analyze-btn', 'n_clicks')],\n",
    "    [dash.dependencies.State('input-text', 'value')]\n",
    ")\n",
    "def analyze_sentiment(n_clicks, user_input):\n",
    "    if n_clicks is None:\n",
    "        return ''\n",
    "    \n",
    "    if not user_input:\n",
    "        return html.Div('Please enter a product review.', style={'color': 'red'})\n",
    "    \n",
    "    # Calculate sentiment score using AFINN\n",
    "    sentiment_score = afinn.score(user_input)\n",
    "    \n",
    "    # Determine sentiment category\n",
    "    if sentiment_score > 0:\n",
    "        sentiment_category = 'Positive'\n",
    "        color = 'green'\n",
    "    elif sentiment_score < 0:\n",
    "        sentiment_category = 'Negative'\n",
    "        color = 'red'\n",
    "    else:\n",
    "        sentiment_category = 'Neutral'\n",
    "        color = 'blue'\n",
    "    \n",
    "    # Display sentiment score and category\n",
    "    return html.Div([\n",
    "        html.Div(f'Sentiment Score: {sentiment_score}', style={'color': color}),\n",
    "        html.Div(f'Sentiment Category: {sentiment_category}', style={'color': color})\n",
    "    ])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "785af576-251f-4772-ab96-921fa7cd4b54",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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