{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis Model Demo\n",
    "This notebook demonstrates how to use the sentiment analysis models to predict sentiment for new text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the models\n",
    "model1 = joblib.load('model1.joblib')\n",
    "model2 = joblib.load('model2.joblib')\n",
    "\n",
    "# Load the embedder\n",
    "embedder = SentenceTransformer('BAAI/bge-large-en-v1.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_sentiment(text):\n",
    "    # Generate embedding\n",
    "    embedding = embedder.encode([text])\n",
    "    \n",
    "    # Make predictions\n",
    "    pred1 = model1.predict(embedding)[0]\n",
    "    pred2 = model2.predict(embedding)[0]\n",
    "    \n",
    "    # Average and round\n",
    "    final_prediction = np.round((pred1 + pred2) / 2).astype(int)\n",
    "    \n",
    "    return final_prediction, pred1, pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try with a sample text\n",
    "sample_text = \"I absolutely loved this movie! The actors were amazing and the plot was fantastic.\"\n",
    "final_score, score1, score2 = predict_sentiment(sample_text)\n",
    "\n",
    "print(f\"Text: {sample_text}\")\n",
    "print(f\"Final sentiment score: {final_score}\")\n",
    "print(f\"Model 1 score: {score1}\")\n",
    "print(f\"Model 2 score: {score2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try with multiple texts\n",
    "texts = [\n",
    "    \"This product is terrible. Complete waste of money.\",\n",
    "    \"The service was okay, nothing special.\",\n",
    "    \"Absolutely fantastic experience! Would highly recommend.\",\n",
    "    \"Not what I expected, but it wasn't bad either.\"\n",
    "]\n",
    "\n",
    "results = []\n",
    "for text in texts:\n",
    "    final_score, score1, score2 = predict_sentiment(text)\n",
    "    results.append({\n",
    "        'Text': text,\n",
    "        'Final Score': final_score,\n",
    "        'Expert 1 Score': score1,\n",
    "        'Expert 2 Score': score2\n",
    "    })\n",
    "\n",
    "pd.DataFrame(results)"
   ]
  }
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