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+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "collapsed": true,
+ "id": "Cx6PJFWUXNfW",
+ "outputId": "21cd010b-050a-4cf1-966a-d696f485a95f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ " Downloading langchain_google_genai-2.0.0-py3-none-any.whl.metadata (3.9 kB)\n",
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+ "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4,>=0.3.0->langchain_google_genai) (6.0.2)\n",
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+ "Downloading langchain_google_genai-2.0.0-py3-none-any.whl (39 kB)\n",
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+ "Building wheels for collected packages: sentence-transformers\n",
+ " Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125924 sha256=e64435dc5242863ced748f20301a428567e7a0dc6ab6205a8f1db318b95734fc\n",
+ " Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
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+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install langchain -qU\n",
+ "!pip install langchain-community -qU\n",
+ "!pip install langchain-chroma -qU\n",
+ "!pip install duckduckgo-search -qU\n",
+ "!pip install langchainhub -qU\n",
+ "!pip install langchain_google_genai\n",
+ "!pip install InstructorEmbedding\n",
+ "!pip install sentence-transformers==2.2.2\n",
+ "!pip install pypdf -qU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_google_genai import ChatGoogleGenerativeAI,GoogleGenerativeAIEmbeddings\n",
+ "from google.colab import userdata\n",
+ "GOOGLE_API_KEY = userdata.get('GEMINI_API_KEY')\n",
+ "\n",
+ "\n",
+ "llm=ChatGoogleGenerativeAI(\n",
+ " model=\"gemini-1.5-flash\",\n",
+ " temperature=0,\n",
+ " api_key=GOOGLE_API_KEY\n",
+ ")\n",
+ "\n",
+ "embedding_model= GoogleGenerativeAIEmbeddings(\n",
+ " model=\"models/embedding-001\"\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "7SA3hgx6Xlz1"
+ },
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+ "\n",
+ "# Used HuggingFace embedding model as the embedding model\n",
+ "embedding_model_2 = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 797,
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+ "483e148d68d5442193e7ae3acc8b9e69",
+ "d017ac0908324a67a4f9300b9c63b745",
+ "ac408ffd067d4ef9bd7621b7b79d1b8f",
+ "07b9616ade0a46aa83738fd390e15f9b",
+ "1863302a407f4262bf840177e08560e7",
+ "d39f923783d8481fba7c5a89f4927b89",
+ "c8337e54fd1a46ee87ca62840680e4e1",
+ "014b9b9a8e9e4a6c8b17baa99646de96",
+ "7ddaeabc67354c9980513291d62fa74a",
+ "0ef019cfe2c3404993327279645215d3",
+ "276a8b3f003e4b37854567b9ae8ea103",
+ "a8e8b205451d4a4dbe7c74de278ee38d",
+ "ad818ed322694c0998a104e83fe6c1bb",
+ "4157671a177641d59633c7a116606beb",
+ "19e197d18d994b67bcf88bf922a6d203",
+ "d60341fd92e041c988ed073e7c09d952",
+ "4ad0cd596dff4f3cb9f01c2fb8029bc1",
+ "791657685db04e8b8282a25499c85944",
+ "7996e0055b6e41709614f373df63a610",
+ "daa1759ee3304730ab3544bc060317d7",
+ "bfbdafc8e9c9404fb6bc09bd7e6466ab",
+ "9e6cf78e52344a36baf86263f3e4d62f",
+ "ab2925ad844a479eb686bee7c53802fe",
+ "0f21e8f0aad34ab687d397994f120edb",
+ "b59f909499324c358d1748377ec5b6a4"
+ ]
+ },
+ "collapsed": true,
+ "id": "XAhBT1p5hO21",
+ "outputId": "698f7232-e66f-41e4-d644-a23ab4f441b1"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/pydantic/_internal/_fields.py:132: UserWarning: Field \"model_name\" in HuggingFaceInferenceAPIEmbeddings has conflict with protected namespace \"model_\".\n",
+ "\n",
+ "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
+ " warnings.warn(\n",
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n",
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'cached_download' (from 'huggingface_hub.file_download') is deprecated and will be removed from version '0.26'. Use `hf_hub_download` instead.\n",
+ " warnings.warn(warning_message, FutureWarning)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ".gitattributes: 0%| | 0.00/1.48k [00:00=5.1 in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.2)\n",
+ "Downloading pyngrok-7.2.0-py3-none-any.whl (22 kB)\n",
+ "Installing collected packages: pyngrok\n",
+ "Successfully installed pyngrok-7.2.0\n",
+ "Collecting flask_cors\n",
+ " Downloading Flask_Cors-5.0.0-py2.py3-none-any.whl.metadata (5.5 kB)\n",
+ "Requirement already satisfied: Flask>=0.9 in /usr/local/lib/python3.10/dist-packages (from flask_cors) (2.2.5)\n",
+ "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask>=0.9->flask_cors) (3.0.4)\n",
+ "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=0.9->flask_cors) (3.1.4)\n",
+ "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=0.9->flask_cors) (2.2.0)\n",
+ "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=0.9->flask_cors) (8.1.7)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->Flask>=0.9->flask_cors) (2.1.5)\n",
+ "Downloading Flask_Cors-5.0.0-py2.py3-none-any.whl (14 kB)\n",
+ "Installing collected packages: flask_cors\n",
+ "Successfully installed flask_cors-5.0.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set your ngrok authtoken\n",
+ "ngrok.set_auth_token(\"2mBRk3uw5rr7B5Iq67MqHt8fXFy_4aANS3yjSViVbqYaxCfZ3\") # Replace with your actual token\n",
+ "port_no = 5000\n",
+ "# Create Flask app\n",
+ "app = Flask(__name__)\n",
+ "CORS(app)\n",
+ "@app.route('/ask', methods=['POST'])\n",
+ "def ask():\n",
+ " try:\n",
+ " user_input = request.json.get(\"question\")\n",
+ " if not user_input:\n",
+ " return jsonify({\"error\": \"No question provided\"}), 400\n",
+ "\n",
+ " response = agent_executor.invoke({\"input\": user_input})\n",
+ " return jsonify({\"answer\": response['output']})\n",
+ "\n",
+ " except Exception as e:\n",
+ " return jsonify({\"error\": str(e)}), 500\n",
+ "\n",
+ "def run_flask():\n",
+ " app.run(port=port_no)\n",
+ "\n",
+ "# Run Flask app in a separate thread\n",
+ "flask_thread = threading.Thread(target=run_flask)\n",
+ "flask_thread.start()\n",
+ "\n",
+ "# Expose the Flask app using ngrok\n",
+ "public_url = ngrok.connect(port_no).public_url\n",
+ "print(f\"Public URL: {public_url}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "collapsed": true,
+ "id": "ffmhxlxi1tDu",
+ "outputId": "76052c06-47e2-4940-923f-dc79c4637185"
+ },
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " * Serving Flask app '__main__'\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
+ " * Running on http://127.0.0.1:5000\n",
+ "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Public URL: https://5687-34-125-112-232.ngrok-free.app\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 1692e06c488b1f31132c79bb7009e562b21cb9e7..88d310a75d132d5e8fc6dc89041a1ca5d08ed306 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,103 @@
----
-title: Llminators
-emoji: 🐨
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 5.36.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Right Vote
+
+# Manifesto Comparator with RAG Chatbot and Win Predictor for the 2024 Presidential Election in Sri Lanka
+
+This project aims to create a manifesto comparator for each leading candidates in presidential election with a chatbot using a Retrieval-Augmented Generation (RAG) model and Win Predictor model using Official Poll data and social media sentiments. The system allows users to query political manifestos and retrieve relevant information of each candidate and ask the general information on the election as well. The backend is built using Flask, Firebase and the frontend is built using Flutter.
+
+## Features
+
+- **Chatbot using LangChain**: Provides fast and relevant document retrieval related to the Sri Lanka's President Election 2024 from vectorstores using Hugging Face embedding model and Gemini-1.5-flash LLM.
+- **Flutter Web App**: Allows users to query manifestos and view retrieved information in an interactive UI.
+- **Flask API**: Handles queries from the frontend, retrieves relevant documents from the FAISS vector store, and returns results.
+
+## Tech Stack
+- **ChatBot**: Python, LangChain, Chroma, Hugging Face, Gemini-1.5-flash
+- **Backend**: Python, Flask, FireBase
+- **Frontend**: Flutter
+- **Hosting**: Google Colab (for testing) and cloud platforms (for deployment)
+
+## Prerequisites
+
+- Python 3.x
+- Google Colab (for testing)
+- Flutter installed on your machine
+- `ngrok` for exposing the Flask API in Google Colab
+
+
+## Win Predictor using LSTM
+
+This project focuses on analyzing pre-election poll data and predicting final election results using an LSTM (Long Short-Term Memory) neural network model. The project also simulates the redistribution of second-preference votes based on pre-defined assumptions and calculates the final vote counts for the election.
+
+### 1. Web Scraping Poll Results
+We extracted the latest poll data from a specified URL using `BeautifulSoup` to scrape the necessary information, including support percentages for the candidates. This information is then used to train a model and make predictions.
+
+### 2. Data Analysis and Preprocessing
+A sample dataset of election polls from 2024 is created and preprocessed for analysis. The data is one-hot encoded, and relevant features like candidate support and demographics (age group, education level) are considered. The model also uses historical data from the 2019 election for comparison.
+
+### 3. LSTM Model for Prediction
+The model is trained using poll data across several months (April 2024 to September 2024). A scaled and reshaped dataset is fed into an LSTM model to predict the potential election outcome. The model outputs predicted percentages for each candidate.
+
+### 4. Second Preference Redistribution
+In cases where no candidate crosses the 50% mark, we simulate the redistribution of second-preference votes based on a predefined redistribution model. The final vote shares are recalculated to determine the election outcome.
+
+### 5. Final Vote Calculation
+After determining the final percentages, the total vote count is adjusted based on the estimated population size (17.1 million). This provides a clearer understanding of the number of votes each candidate is expected to receive.
+
+## Sentiment Analysis
+To assess public sentiment towards candidates by analyzing text data from social media and news sources. This helps to understand the emotional tone of public discourse, which can be crucial for interpreting polling data.
+
+### Techniques Used
+
+#### Data Collection & text preprocessing
+
+- **Tool:** Twitter API
+- **Process:**
+ - Collected recent tweets mentioning the candidates of interest.
+ - Used the `tweepy` library to fetch tweets based on specific search queries related to each candidate.
+ - Ensured that the sentiment analysis is not influenced by specific users or external links mentioned in the tweets.
+
+#### Sentiment Analysis Model
+
+- **Tool:** RoBERTa model
+- **Process:**
+ - **Tokenization:** Converted text into a format suitable for model input using the RoBERTa tokenizer.
+ - **Model Inference:** Passed tokenized text through the RoBERTa model to obtain sentiment scores. The model was pre-trained on Twitter data to effectively classify sentiments in social media contexts.
+ - **Softmax Function:** Applied the softmax function to the model's output to convert raw scores into probabilities, representing the likelihood of each sentiment class (positive, neutral, negative).
+
+#### Sentiment Classification
+
+- **Objective:** To categorize each tweet into one of three sentiment classes: positive, neutral, or negative.
+- **Process:**
+ - Assigned sentiment labels based on the highest probability from the model’s output.
+
+
+## ChatBot
+
+This feature is a chatbot that allows users to query the manifestos and policies of prominent Sri Lankan candidates, view pre-election poll results, and get general information on the election. Built with Langchain and RAG (Retrieval-Augmented Generation) architecture, the model uses PDF documents of candidates' manifestos, web search tools, and poll predictions to provide insightful answers.
+
+### 1. Reading Multiple PDFs
+We utilized the `pypdf` library in Python to extract text from PDF documents. The chatbot reads manifestos from multiple candidates including Ranil Wickremesinghe, Anura Kumara Dissanayake, and Sajith Premadasa, as well as pre-election poll data and general election questions.
+
+### 2.Embedding the Text and Storing in a Vectorstore
+The extracted text from each PDF is converted into embeddings using the HuggingFace `hkunlp/instructor-xl` model. These embeddings are then stored in a `Chroma` vectorstore. By persisting the vectorstore, we efficiently reuse the data for answering future queries.
+
+### 3. Agent Architecture
+We implemented tools for each candidate's manifesto using a RAG (Retrieval-Augmented Generation) setup. Each tool is responsible for retrieving and answering questions about a specific candidate’s manifesto. Additionally, a DuckDuckGo search tool is integrated to fetch real-time web data, and a poll prediction tool is included for querying pre-election polls and their forecasts.
+
+### 4. LLM Integration
+The chatbot uses the `gemini-1.5-flash` model from Google Generative AI for generating responses. This LLM is deployed within the RAG architecture to provide accurate and context-aware answers based on both document retrieval and external web search.
+
+### 5. Interactive Querying
+Users can ask the chatbot detailed questions about individual manifestos, compare policies between candidates, or inquire about pre-election predictions and general election information. The system uses a mix of retrieval from vectorstores and real-time web search to deliver comprehensive responses.
+
+## Requirements
+
+- Python 3.x
+- `langchain` for building the RAG-based model and managing agent architectures
+- `langchain-community` for using additional tools from the Langchain community
+- `langchain-chroma` for managing and storing document embeddings in Chroma's vectorstore
+- `pypdf` for reading and extracting text from PDFs
+- `InstructorEmbedding` for generating text embeddings using HuggingFace models
+- `sentence-transformers==2.2.2` for text preprocessing and vectorization
+- `langchain_google_genai` for connecting to Google’s generative AI models like `gemini-1.5-flash`
+
diff --git a/Social_Media_Sentiment.ipynb b/Social_Media_Sentiment.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..86bc814aaeadbd2a569607fc7066b20a50e23815
--- /dev/null
+++ b/Social_Media_Sentiment.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "296aa3df",
+ "metadata": {
+ "id": "296aa3df"
+ },
+ "source": [
+ "### Twitter Sentiment Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c9a7dc7a",
+ "metadata": {
+ "id": "c9a7dc7a"
+ },
+ "outputs": [],
+ "source": [
+ "import tweepy\n",
+ "import re\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
+ "from scipy.special import softmax\n",
+ "\n",
+ "# Twitter API credentials\n",
+ "API_KEY = 'MCu2BiFyXXXXXXX'\n",
+ "API_SECRET_KEY = 'Cl8FYiqoVl82KyXXXXXXXXXX'\n",
+ "BEARER_TOKEN = 'AAAAAAAAAAAAAAAAAAAAXXXXXXX'\n",
+ "ACCESS_TOKEN = '1709435XXXXXXXX'\n",
+ "ACCESS_TOKEN_SECRET = 'Lp50yuMNJjg5SDZXXXXXXXX'\n",
+ "\n",
+ "# Set up Twitter API client\n",
+ "client = tweepy.Client(bearer_token=BEARER_TOKEN)\n",
+ "\n",
+ "# Initialize sentiment analysis model and tokenizer\n",
+ "roberta = \"cardiffnlp/twitter-roberta-base-sentiment\"\n",
+ "model = TFAutoModelForSequenceClassification.from_pretrained(roberta)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(roberta)\n",
+ "\n",
+ "# Labels for sentiment analysis\n",
+ "labels = ['Negative', 'Neutral', 'Positive']\n",
+ "\n",
+ "def preprocess_tweet(tweet):\n",
+ " \"\"\"\n",
+ " Preprocess the tweet by replacing mentions and URLs with placeholders.\n",
+ " \"\"\"\n",
+ " tweet_words = []\n",
+ " for word in tweet.split(' '):\n",
+ " if word.startswith('@') and len(word) > 1:\n",
+ " word = '@user'\n",
+ " elif word.startswith('http'):\n",
+ " word = \"http\"\n",
+ " tweet_words.append(word)\n",
+ " return \" \".join(tweet_words)\n",
+ "\n",
+ "def fetch_tweets(query, max_results=100):\n",
+ " \"\"\"\n",
+ " Fetch recent tweets containing the specified query.\n",
+ " \"\"\"\n",
+ " tweet_data = []\n",
+ " try:\n",
+ " tweets = client.search_recent_tweets(query=query, max_results=max_results, tweet_fields=['created_at', 'text'])\n",
+ " if tweets.data:\n",
+ " for tweet in tweets.data:\n",
+ " tweet_data.append({\n",
+ " 'created_at': tweet.created_at,\n",
+ " 'text': tweet.text\n",
+ " })\n",
+ " except Exception as e:\n",
+ " print(f\"Error fetching tweets: {e}\")\n",
+ " return tweet_data\n",
+ "\n",
+ "def analyze_sentiment(text):\n",
+ " \"\"\"\n",
+ " Analyze the sentiment of the given text using the pre-trained model.\n",
+ " \"\"\"\n",
+ " tweet_proc = preprocess_tweet(text)\n",
+ " encoded_tweet = tokenizer(tweet_proc, return_tensors='tf')\n",
+ " output = model(**encoded_tweet)\n",
+ " scores = output[0][0].numpy()\n",
+ " scores = softmax(scores)\n",
+ " return dict(zip(labels, scores))\n",
+ "\n",
+ "def plot_sentiment_distribution(sentiment_counts):\n",
+ " \"\"\"\n",
+ " Plot a bar chart of sentiment distribution.\n",
+ " \"\"\"\n",
+ " sentiments = list(sentiment_counts.keys())\n",
+ " counts = list(sentiment_counts.values())\n",
+ " plt.bar(sentiments, counts, color=['red', 'gray', 'green'])\n",
+ " plt.xlabel('Sentiment')\n",
+ " plt.ylabel('Count')\n",
+ " plt.title('Sentiment Distribution')\n",
+ " plt.show()\n",
+ "\n",
+ "def main():\n",
+ " # Example candidate names\n",
+ " candidates = [\"Sajith Premadasa\", \"Ranil Wickremesinghe\", \"Anura Kumara\"]\n",
+ "\n",
+ " all_tweet_data = []\n",
+ "\n",
+ " for candidate in candidates:\n",
+ " print(f\"Fetching tweets for {candidate}...\")\n",
+ " tweets = fetch_tweets(candidate, max_results=100)\n",
+ " for tweet in tweets:\n",
+ " tweet['candidate'] = candidate\n",
+ " all_tweet_data.append(tweet)\n",
+ "\n",
+ " # Convert tweet data to DataFrame\n",
+ " df = pd.DataFrame(all_tweet_data)\n",
+ "\n",
+ " # Analyze sentiment for each tweet\n",
+ " sentiment_counts = {label: 0 for label in labels}\n",
+ "\n",
+ " for text in df['text']:\n",
+ " sentiment = analyze_sentiment(text)\n",
+ " for label, score in sentiment.items():\n",
+ " sentiment_counts[label] += score\n",
+ "\n",
+ " # Print sentiment distribution\n",
+ " print(\"Sentiment Distribution:\")\n",
+ " for label, count in sentiment_counts.items():\n",
+ " print(f\"{label}: {count}\")\n",
+ "\n",
+ " # Plot sentiment distribution\n",
+ " plot_sentiment_distribution(sentiment_counts)\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " main()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "815021d8",
+ "metadata": {
+ "id": "815021d8"
+ },
+ "source": [
+ "### Facebook Sentiment Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cb529b29",
+ "metadata": {
+ "id": "cb529b29"
+ },
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "from transformers import pipeline\n",
+ "\n",
+ "# Facebook API credentials\n",
+ "ACCESS_TOKEN = 'EAAjjdeAxAF1eT5ZBbibXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n",
+ "PAGE_ID = 'XXXXXXXXXXXXX'\n",
+ "\n",
+ "# Initialize sentiment analysis pipeline\n",
+ "sentiment_pipeline = pipeline('sentiment-analysis')\n",
+ "\n",
+ "def fetch_facebook_posts(page_id, access_token, limit=10):\n",
+ " \"\"\"\n",
+ " Fetch recent posts from a Facebook page.\n",
+ " \"\"\"\n",
+ " url = f'https://graph.facebook.com/{page_id}/posts'\n",
+ " params = {\n",
+ " 'access_token': access_token,\n",
+ " 'limit': limit,\n",
+ " }\n",
+ " response = requests.get(url, params=params)\n",
+ " response.raise_for_status()\n",
+ " return response.json().get('data', [])\n",
+ "\n",
+ "def analyze_sentiment(text):\n",
+ " \"\"\"\n",
+ " Analyze the sentiment of the given text using Hugging Face's sentiment analysis model.\n",
+ " \"\"\"\n",
+ " return sentiment_pipeline(text)\n",
+ "\n",
+ "def main():\n",
+ " # Fetch recent posts\n",
+ " posts = fetch_facebook_posts(PAGE_ID, ACCESS_TOKEN, limit=5)\n",
+ "\n",
+ " if not posts:\n",
+ " print(\"No posts found.\")\n",
+ " return\n",
+ "\n",
+ " # Analyze sentiment for each post\n",
+ " for post in posts:\n",
+ " post_message = post.get('message', '')\n",
+ " if post_message:\n",
+ " sentiment = analyze_sentiment(post_message)\n",
+ " print(f\"Post: {post_message}\")\n",
+ " print(f\"Sentiment: {sentiment[0]['label']} (Confidence: {sentiment[0]['score']:.2f})\")\n",
+ " print()\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " main()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.3"
+ },
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/Win_Predictor.ipynb b/Win_Predictor.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..caaa9629d65ba8dbbcb42819f77165f7bb52aba8
--- /dev/null
+++ b/Win_Predictor.ipynb
@@ -0,0 +1,998 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ecdf2ae",
+ "metadata": {
+ "id": "1ecdf2ae"
+ },
+ "source": [
+ "### Web Scraping"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af20daad",
+ "metadata": {
+ "id": "af20daad"
+ },
+ "source": [
+ "The data was collected from IHP Sri Lanka(https://www.ihp.lk/taxonomy/term/135) and Numbers.lk(https://numbers.lk/analysis\n",
+ ")."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5e515be",
+ "metadata": {
+ "id": "d5e515be",
+ "outputId": "88580cac-44ce-413f-d500-63de8a3c0dc8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\"Numbers.lk's first pre-election poll results point to Anura Kumara Dissanayake as the frontrunner in Sri Lanka's upcoming presidential election, though no candidate has secured a clear majority yet. The poll surveyed 2,048 adults across all 22 electoral districts online from April 4-18, and it has a margin of error of +/- 3.5%.\", 'According to the survey finding, Anura Kumara Dissanayaka leads the presidential race with 46% support among Sri Lankan adults. He holds a substantial lead over Sajith Premadasa, who has 22% support, and the incumbent President Ranil Wickremesinghe, who trails with 18%. However, with 10% of voters still undecided and another 4% backing other candidates, the race remains open for potential shifts before election day.', \"Anuna Kumara Dissanayake's core strengths lie with Sri Lanka's youngest voters, securing a staggering 67% support among first-time voters. He also leads comfortably among the under-40 crowd. Premadasa counters with a base among older generations whose highest educational attainment is below A/L levels.\\nHowever, Premadasa significantly lags with first-time voters compared to the other top candidates. Only 3% of these crucial first time voters say they plan to vote for the SJB's Sajith Premadasa. At the same time, incumbent President Ranil Wickremesinghe captures 10% approval in this group.\\nAnother 10% of first-time voters report intending to vote for other candidates outside the top three. And a further 10% remain undecided, leaving a substantial pool of young votes still up for grabs.\", \"When it comes to older voters, support for the traditional candidates rises along with age. Sajith Premadasa's strongest base comes from voters aged 41 and above whose highest educational attainment is below A/L levels. In this demographic, Sajith Premadasa leads with 38% support, while Anura Kumara Dissanayake could muster only 28% backing. Ranil Wickremesinghe captures 21% of these older, comparatively less educated demographic while 14% remain undecided.\", \"This cohort represents by far the largest voter block in the country, with over 40% of Sri Lanka's total electorate falling into the category of being 41 or older with education below A/L level. Their sheer numbers give outsized importance to whichever candidate can most effectively court these more traditionalist voters with modest educational credentials.\", 'The sampling procedure and stratification weighting yield an overall margin of sampling error of ±3.5 percentage points at the 95% confidence level for the full unweighted sample of 2,048 Sri Lankan adults. This margin of error accounts for design effects from the weighting process.']\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "import csv\n",
+ "\n",
+ "# URL of the page with the poll results\n",
+ "url = \"https://numbers.lk/analysis/numbers-lk-s-first-pre-election-poll-results-akd-emerges-as-frontrunner-but-falls-short-of-the-crucial-50\"\n",
+ "\n",
+ "# Fetch the content of the page\n",
+ "response = requests.get(url)\n",
+ "html_content = response.content\n",
+ "\n",
+ "# Parse the page using BeautifulSoup\n",
+ "soup = BeautifulSoup(html_content, 'html.parser')\n",
+ "\n",
+ "# Find all relevant text sections\n",
+ "poll_results = []\n",
+ "\n",
+ "for paragraph in soup.find_all('p'):\n",
+ " text = paragraph.get_text().strip()\n",
+ " if \"AKD\" in text or \"%\" in text: # Filter based on content to capture poll data\n",
+ " poll_results.append(text)\n",
+ "\n",
+ "print(poll_results)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "50194477",
+ "metadata": {
+ "id": "50194477",
+ "outputId": "19149067-60a7-4bb5-be5a-ad666ee93034"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['In first half of 2024, the most notable changes in voting intent were the rise in support for President Ranil Wickremasinghe as a potential Presidential candidate at the apparent expense of support for likely candidates from the NPP, SJB and SLPP. In IHP’s MRP (Multilevel Regression and Post-Stratification) model projections, support for NPP leader Anura Dissanayake, SJB leader Sajith Premadasa, and a generic SLPP candidate declined by 7%, 1% and 5% respectively from the first two months of the year to June/July 2024. The main beneficiary was President Wickremesinghe, whose support more than doubled, increasing 13% to 22% during the same period, although this still left him trailing in third place.', 'NPP leader, Anura Dissanayake, registered losses in support across most demographics, but the biggest losses were in his core base of the better-off, Sinhala and youth (18–29 years) voters, where support fell 12, 9, and 17 points respectively. Support amongst voters with a favourable view of the Aragalaya fell 9%–a bigger fall than Sajith Premadasa experienced, although he retained a strong lead over his opponents in these voters. He also lost support amongst voters who voted for Gotabaya Rajapakse or Sajith Premadasa in 2019, although the MRP estimates suggest he managed to consolidate his 2019 Presidential Election voters.', 'The shifts away from both Anura Dissanayake and Sajith Premadasa seem to have been largely to the gain of President Ranil Wickremesinghe. His support rose across all demographics, except in Muslims. This included a larger gain in women (+17% vs. +8% in men), improving his better performance in women. Shifts in his favour were biggest in better-off (+17), Sinhala (+16), older (+24), and former Gotabaya Rajapakse (+20) voters. But notably the biggest shifts in his favour were in anti-Aragalaya voters, where he gained 26 points to build a lead over all other likely candidates. To a considerable extent, President Wickremesinghe has been a major beneficiary of the core pro-government/Rajapakse/SLPP vote, which in the first half of the year largely collapsed across all demographics.', 'Unlike in conventional opinion surveys, IHP’s SLOTS MRP voting intent estimates are not weighted tabulations of the raw survey responses. First, non-responses and refusals to the voting history and voting intent questions are handled by multiple imputation models that consider a wide range of respondent characteristics and impute the probability distribution of who the respondent might have voted for or would vote for. Second, voting intent is estimated using Multilevel Regression and Post-Stratification (MRP) modelling. MRP involves modelling the voting choices of respondents in relation to their characteristics, and then using the estimated model parameters to predict how the full adult population would vote. As MRP does not lend itself to standard computations of survey error, IHP uses a multiple imputation approach to estimate the uncertainty in its model predictions, and this is reported as the margin of error. Specifically, the margin of error represents IHP’s best estimates of the range in which the true levels of voting intent would lie 95% of the time. The MoE here represents uncertainty related to how well the MRP model fits the observed data, as well as traditional sampling noise. However, it does not capture any systematic errors that might arise due to non-sample bias, such as when supporters of one party are less likely to be reached by phone or agree to be interviewed.', 'The analysis reported here are based on modelling of responses from 18,206 interviews conducted from 1 December 2021 to 19 August 2024, including 1,077 interviews conducted in January/February 2024 and 1,146 interviews conducted in June/July 2024. The margins of error are assessed as 2–4% for Ranil Wickremesinghe, Sajith Premadasa, and AK Dissanayake.']\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "import csv\n",
+ "\n",
+ "# URL of the page with the poll results\n",
+ "url = \"https://www.ihp.lk/research-updates/changes-support-leading-presidential-candidates-during-2024-lead-campaign-period\"\n",
+ "\n",
+ "# Fetch the content of the page\n",
+ "response = requests.get(url)\n",
+ "html_content = response.content\n",
+ "\n",
+ "# Parse the page using BeautifulSoup\n",
+ "soup = BeautifulSoup(html_content, 'html.parser')\n",
+ "\n",
+ "# Find all relevant text sections\n",
+ "poll_results = []\n",
+ "\n",
+ "for paragraph in soup.find_all('p'):\n",
+ " text = paragraph.get_text().strip()\n",
+ " if \"AKD\" in text or \"%\" in text: # Filter based on content to capture poll data\n",
+ " poll_results.append(text)\n",
+ "\n",
+ "print(poll_results)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b32deb3d",
+ "metadata": {
+ "id": "b32deb3d"
+ },
+ "source": [
+ "### Data Collection and Pre Processing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f753cdb3",
+ "metadata": {
+ "id": "f753cdb3",
+ "outputId": "2b4b3232-aa2d-4fe7-8c1b-48d0c8fe945e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | date | \n", + "age_group | \n", + "education | \n", + "AKD_support | \n", + "Premadasa_support | \n", + "Ranil_support | \n", + "undecided | \n", + "other_support | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "04.05.2024 | \n", + "adults | \n", + "common | \n", + "67 | \n", + "3 | \n", + "10 | \n", + "10 | \n", + "10 | \n", + "
| 1 | \n", + "04.05.2024 | \n", + "over_40 | \n", + "below_AL | \n", + "28 | \n", + "38 | \n", + "21 | \n", + "14 | \n", + "0 | \n", + "
| 2 | \n", + "04.05.2024 | \n", + "young | \n", + "common | \n", + "46 | \n", + "22 | \n", + "18 | \n", + "10 | \n", + "4 | \n", + "
| 3 | \n", + "25.08.2024 | \n", + "adults | \n", + "common | \n", + "43 | \n", + "27 | \n", + "22 | \n", + "2 | \n", + "2 | \n", + "
| \n", + " | Candidate | \n", + "July_2024_Support | \n", + "August_2024_Support | \n", + "July_2024_Favorability | \n", + "August_2024_Favorability | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "AK Dissanayake | \n", + "37 | \n", + "36 | \n", + "3 | \n", + "-21 | \n", + "
| 1 | \n", + "Sajith Premadasa | \n", + "36 | \n", + "32 | \n", + "-44 | \n", + "-32 | \n", + "
| 2 | \n", + "Ranil Wickremesinghe | \n", + "23 | \n", + "33 | \n", + "-24 | \n", + "-33 | \n", + "
| 3 | \n", + "Namal Rajapaksa | \n", + "4 | \n", + "3 | \n", + "-89 | \n", + "-89 | \n", + "
| \n", + " | Candidate | \n", + "2019_polls | \n", + "Final_result | \n", + "
|---|---|---|---|
| 0 | \n", + "Candidate 1 | \n", + "54 | \n", + "52.25 | \n", + "
| 1 | \n", + "Candidate 2 | \n", + "39 | \n", + "41.99 | \n", + "
| 2 | \n", + "Candidate 3 | \n", + "5 | \n", + "3.16 | \n", + "
| \n", + " | Candidate | \n", + "April_2024 | \n", + "May_2024 | \n", + "June_2024 | \n", + "July_2024 | \n", + "August_2024 | \n", + "September_2024 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "AK Dissanayake | \n", + "46 | \n", + "39 | \n", + "38 | \n", + "37 | \n", + "36 | \n", + "43 | \n", + "
| 1 | \n", + "Sajith Premadasa | \n", + "22 | \n", + "39 | \n", + "38 | \n", + "36 | \n", + "32 | \n", + "27 | \n", + "
| 2 | \n", + "Ranil Wickremesinghe | \n", + "18 | \n", + "15 | \n", + "18 | \n", + "23 | \n", + "33 | \n", + "22 | \n", + "
| 3 | \n", + "other | \n", + "14 | \n", + "7 | \n", + "6 | \n", + "4 | \n", + "3 | \n", + "8 | \n", + "
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+ + + + diff --git a/webapp/pubspec.lock b/webapp/pubspec.lock new file mode 100644 index 0000000000000000000000000000000000000000..05e041a47b0cf1ca916dbc5b2d3112d64485dc08 --- /dev/null +++ b/webapp/pubspec.lock @@ -0,0 +1,346 @@ +# Generated by pub +# See https://dart.dev/tools/pub/glossary#lockfile +packages: + _flutterfire_internals: + dependency: transitive + description: + name: _flutterfire_internals + sha256: "37a42d06068e2fe3deddb2da079a8c4d105f241225ba27b7122b37e9865fd8f7" + url: "https://pub.dev" + source: hosted + version: "1.3.35" + async: + dependency: transitive + description: + name: async + sha256: "947bfcf187f74dbc5e146c9eb9c0f10c9f8b30743e341481c1e2ed3ecc18c20c" + url: "https://pub.dev" + source: hosted + version: "2.11.0" + boolean_selector: + dependency: transitive + description: + name: boolean_selector + sha256: "6cfb5af12253eaf2b368f07bacc5a80d1301a071c73360d746b7f2e32d762c66" + url: "https://pub.dev" + source: hosted + version: "2.1.1" + characters: + dependency: transitive + description: + name: characters + sha256: "04a925763edad70e8443c99234dc3328f442e811f1d8fd1a72f1c8ad0f69a605" + url: "https://pub.dev" + source: hosted + version: "1.3.0" + clock: + dependency: transitive + description: + name: clock + sha256: cb6d7f03e1de671e34607e909a7213e31d7752be4fb66a86d29fe1eb14bfb5cf + url: "https://pub.dev" + source: hosted + version: "1.1.1" + cloud_firestore: + dependency: "direct main" + description: + name: cloud_firestore + sha256: a0f161b92610e078b4962d7e6ebeb66dc9cce0ada3514aeee442f68165d78185 + url: "https://pub.dev" + source: hosted + version: "4.17.5" + cloud_firestore_platform_interface: + dependency: transitive + description: + name: cloud_firestore_platform_interface + sha256: "6a55b319f8d33c307396b9104512e8130a61904528ab7bd8b5402678fca54b81" + url: "https://pub.dev" + source: hosted + version: "6.2.5" + cloud_firestore_web: + dependency: transitive + description: + name: cloud_firestore_web + sha256: "89dfa1304d3da48b3039abbb2865e3d30896ef858e569a16804a99f4362283a9" + url: "https://pub.dev" + source: hosted + version: "3.12.5" + collection: + dependency: transitive + description: + name: collection + sha256: ee67cb0715911d28db6bf4af1026078bd6f0128b07a5f66fb2ed94ec6783c09a + url: "https://pub.dev" + source: hosted + version: "1.18.0" + cupertino_icons: + dependency: "direct main" + description: + name: cupertino_icons + sha256: ba631d1c7f7bef6b729a622b7b752645a2d076dba9976925b8f25725a30e1ee6 + url: "https://pub.dev" + source: hosted + version: "1.0.8" + fake_async: + dependency: transitive + description: + name: fake_async + sha256: "511392330127add0b769b75a987850d136345d9227c6b94c96a04cf4a391bf78" + url: "https://pub.dev" + source: hosted + version: "1.3.1" + firebase_core: + dependency: "direct main" + description: + name: firebase_core + sha256: "26de145bb9688a90962faec6f838247377b0b0d32cc0abecd9a4e43525fc856c" + url: "https://pub.dev" + source: hosted + version: "2.32.0" + firebase_core_platform_interface: + dependency: transitive + description: + name: firebase_core_platform_interface + sha256: e30da58198a6d4b49d5bce4e852f985c32cb10db329ebef9473db2b9f09ce810 + url: "https://pub.dev" + source: hosted + version: "5.3.0" + firebase_core_web: + dependency: "direct main" + description: + name: firebase_core_web + sha256: f967a7138f5d2ffb1ce15950e2a382924239eaa521150a8f144af34e68b3b3e5 + url: "https://pub.dev" + source: hosted + version: "2.18.1" + flutter: + dependency: "direct main" + description: flutter + source: sdk + version: "0.0.0" + flutter_lints: + dependency: "direct dev" + description: + name: flutter_lints + sha256: "9e8c3858111da373efc5aa341de011d9bd23e2c5c5e0c62bccf32438e192d7b1" + url: "https://pub.dev" + source: hosted + version: "3.0.2" + flutter_test: + dependency: "direct dev" + description: flutter + source: sdk + version: "0.0.0" + flutter_web_plugins: + dependency: transitive + description: flutter + source: sdk + version: "0.0.0" + gradient_borders: + dependency: "direct main" + description: + name: gradient_borders + sha256: b1cd969552c83f458ff755aa68e13a0327d09f06c3f42f471b423b01427f21f8 + url: "https://pub.dev" + source: hosted + version: "1.0.1" + http: + dependency: "direct main" + description: + name: http + sha256: b9c29a161230ee03d3ccf545097fccd9b87a5264228c5d348202e0f0c28f9010 + url: "https://pub.dev" + source: hosted + version: "1.2.2" + http_parser: + dependency: transitive + description: + name: http_parser + sha256: "2aa08ce0341cc9b354a498388e30986515406668dbcc4f7c950c3e715496693b" + url: "https://pub.dev" + source: hosted + version: "4.0.2" + leak_tracker: + dependency: transitive + description: + name: leak_tracker + sha256: "3f87a60e8c63aecc975dda1ceedbc8f24de75f09e4856ea27daf8958f2f0ce05" + url: "https://pub.dev" + source: hosted + version: "10.0.5" + leak_tracker_flutter_testing: + dependency: transitive + description: + name: leak_tracker_flutter_testing + sha256: "932549fb305594d82d7183ecd9fa93463e9914e1b67cacc34bc40906594a1806" + url: "https://pub.dev" + source: hosted + version: "3.0.5" + leak_tracker_testing: + dependency: transitive + description: + name: leak_tracker_testing + sha256: "6ba465d5d76e67ddf503e1161d1f4a6bc42306f9d66ca1e8f079a47290fb06d3" + url: "https://pub.dev" + source: hosted + version: "3.0.1" + lints: + dependency: transitive + description: + name: lints + sha256: cbf8d4b858bb0134ef3ef87841abdf8d63bfc255c266b7bf6b39daa1085c4290 + url: "https://pub.dev" + source: hosted + version: "3.0.0" + matcher: + dependency: transitive + description: + name: matcher + sha256: d2323aa2060500f906aa31a895b4030b6da3ebdcc5619d14ce1aada65cd161cb + url: "https://pub.dev" + source: hosted + version: "0.12.16+1" + material_color_utilities: + dependency: transitive + description: + name: material_color_utilities + sha256: f7142bb1154231d7ea5f96bc7bde4bda2a0945d2806bb11670e30b850d56bdec + url: "https://pub.dev" + source: hosted + version: "0.11.1" + meta: + dependency: transitive + description: + name: meta + sha256: bdb68674043280c3428e9ec998512fb681678676b3c54e773629ffe74419f8c7 + url: "https://pub.dev" + source: hosted + version: "1.15.0" + nested: + dependency: transitive + description: + name: nested + sha256: "03bac4c528c64c95c722ec99280375a6f2fc708eec17c7b3f07253b626cd2a20" + url: "https://pub.dev" + source: hosted + version: "1.0.0" + path: + dependency: transitive + description: + name: path + sha256: "087ce49c3f0dc39180befefc60fdb4acd8f8620e5682fe2476afd0b3688bb4af" + url: "https://pub.dev" + source: hosted + version: "1.9.0" + pie_chart: + dependency: "direct main" + description: + name: pie_chart + sha256: "58e6a46999ac938bfa1c3e5be414d6e149f037647197dca03ba3614324c12c82" + url: "https://pub.dev" + source: hosted + version: "5.4.0" + plugin_platform_interface: + dependency: transitive + description: + name: plugin_platform_interface + sha256: "4820fbfdb9478b1ebae27888254d445073732dae3d6ea81f0b7e06d5dedc3f02" + url: "https://pub.dev" + source: hosted + version: "2.1.8" + provider: + dependency: "direct main" + description: + name: provider + sha256: c8a055ee5ce3fd98d6fc872478b03823ffdb448699c6ebdbbc71d59b596fd48c + url: "https://pub.dev" + source: hosted + version: "6.1.2" + sky_engine: + dependency: transitive + description: flutter + source: sdk + version: "0.0.99" + source_span: + dependency: transitive + description: + name: source_span + sha256: "53e943d4206a5e30df338fd4c6e7a077e02254531b138a15aec3bd143c1a8b3c" + url: "https://pub.dev" + source: hosted + version: "1.10.0" + stack_trace: + dependency: transitive + description: + name: stack_trace + sha256: "73713990125a6d93122541237550ee3352a2d84baad52d375a4cad2eb9b7ce0b" + url: "https://pub.dev" + source: hosted + version: "1.11.1" + stream_channel: + dependency: transitive + description: + name: stream_channel + sha256: ba2aa5d8cc609d96bbb2899c28934f9e1af5cddbd60a827822ea467161eb54e7 + url: "https://pub.dev" + source: hosted + version: "2.1.2" + string_scanner: + dependency: transitive + description: + name: string_scanner + sha256: "556692adab6cfa87322a115640c11f13cb77b3f076ddcc5d6ae3c20242bedcde" + url: "https://pub.dev" + source: hosted + version: "1.2.0" + term_glyph: + dependency: transitive + description: + name: term_glyph + sha256: a29248a84fbb7c79282b40b8c72a1209db169a2e0542bce341da992fe1bc7e84 + url: "https://pub.dev" + source: hosted + version: "1.2.1" + test_api: + dependency: transitive + description: + name: test_api + sha256: "5b8a98dafc4d5c4c9c72d8b31ab2b23fc13422348d2997120294d3bac86b4ddb" + url: "https://pub.dev" + source: hosted + version: "0.7.2" + typed_data: + dependency: transitive + description: + name: typed_data + sha256: facc8d6582f16042dd49f2463ff1bd6e2c9ef9f3d5da3d9b087e244a7b564b3c + url: "https://pub.dev" + source: hosted + version: "1.3.2" + vector_math: + dependency: transitive + description: + name: vector_math + sha256: "80b3257d1492ce4d091729e3a67a60407d227c27241d6927be0130c98e741803" + url: "https://pub.dev" + source: hosted + version: "2.1.4" + vm_service: + dependency: transitive + description: + name: vm_service + sha256: "5c5f338a667b4c644744b661f309fb8080bb94b18a7e91ef1dbd343bed00ed6d" + url: "https://pub.dev" + source: hosted + version: "14.2.5" + web: + dependency: transitive + description: + name: web + sha256: d43c1d6b787bf0afad444700ae7f4db8827f701bc61c255ac8d328c6f4d52062 + url: "https://pub.dev" + source: hosted + version: "1.0.0" +sdks: + dart: ">=3.4.0 <4.0.0" + flutter: ">=3.22.0" diff --git a/webapp/pubspec.yaml b/webapp/pubspec.yaml new file mode 100644 index 0000000000000000000000000000000000000000..21c8bf7d32ed5f0cd9a2f207afcb933638864d8b --- /dev/null +++ b/webapp/pubspec.yaml @@ -0,0 +1,101 @@ +name: llminator +description: "A new Flutter project." +# The following line prevents the package from being accidentally published to +# pub.dev using `flutter pub publish`. This is preferred for private packages. +publish_to: 'none' # Remove this line if you wish to publish to pub.dev + +# The following defines the version and build number for your application. +# A version number is three numbers separated by dots, like 1.2.43 +# followed by an optional build number separated by a +. +# Both the version and the builder number may be overridden in flutter +# build by specifying --build-name and --build-number, respectively. +# In Android, build-name is used as versionName while build-number used as versionCode. +# Read more about Android versioning at https://developer.android.com/studio/publish/versioning +# In iOS, build-name is used as CFBundleShortVersionString while build-number is used as CFBundleVersion. +# Read more about iOS versioning at +# https://developer.apple.com/library/archive/documentation/General/Reference/InfoPlistKeyReference/Articles/CoreFoundationKeys.html +# In Windows, build-name is used as the major, minor, and patch parts +# of the product and file versions while build-number is used as the build suffix. +version: 1.0.0+1 + +environment: + sdk: '>=3.3.3 <4.0.0' + +# Dependencies specify other packages that your package needs in order to work. +# To automatically upgrade your package dependencies to the latest versions +# consider running `flutter pub upgrade --major-versions`. Alternatively, +# dependencies can be manually updated by changing the version numbers below to +# the latest version available on pub.dev. To see which dependencies have newer +# versions available, run `flutter pub outdated`. +dependencies: + flutter: + sdk: flutter + + provider: ^6.0.1 + http: ^1.2.2 + gradient_borders: ^1.0.0 + # firebase_core: ^1.10.0 + # fl_chart: ^0.36.0 + pie_chart: ^5.4.0 + firebase_core: ^2.4.0 + cloud_firestore: ^4.17.5 + firebase_core_web: ^2.18.0 + + + # The following adds the Cupertino Icons font to your application. + # Use with the CupertinoIcons class for iOS style icons. + cupertino_icons: ^1.0.6 + +dev_dependencies: + flutter_test: + sdk: flutter + + # The "flutter_lints" package below contains a set of recommended lints to + # encourage good coding practices. The lint set provided by the package is + # activated in the `analysis_options.yaml` file located at the root of your + # package. See that file for information about deactivating specific lint + # rules and activating additional ones. + flutter_lints: ^3.0.0 + +# For information on the generic Dart part of this file, see the +# following page: https://dart.dev/tools/pub/pubspec + +# The following section is specific to Flutter packages. +flutter: + + # The following line ensures that the Material Icons font is + # included with your application, so that you can use the icons in + # the material Icons class. + uses-material-design: true + + # To add assets to your application, add an assets section, like this: + assets: + - assets/bgimg.jpg + # - images/a_dot_burr.jpeg + # - images/a_dot_ham.jpeg + + # An image asset can refer to one or more resolution-specific "variants", see + # https://flutter.dev/assets-and-images/#resolution-aware + + # For details regarding adding assets from package dependencies, see + # https://flutter.dev/assets-and-images/#from-packages + + # To add custom fonts to your application, add a fonts section here, + # in this "flutter" section. Each entry in this list should have a + # "family" key with the font family name, and a "fonts" key with a + # list giving the asset and other descriptors for the font. For + # example: + # fonts: + # - family: Schyler + # fonts: + # - asset: fonts/Schyler-Regular.ttf + # - asset: fonts/Schyler-Italic.ttf + # style: italic + # - family: Trajan Pro + # fonts: + # - asset: fonts/TrajanPro.ttf + # - asset: fonts/TrajanPro_Bold.ttf + # weight: 700 + # + # For details regarding fonts from package dependencies, + # see https://flutter.dev/custom-fonts/#from-packages diff --git a/webapp/test/widget_test.dart b/webapp/test/widget_test.dart new file mode 100644 index 0000000000000000000000000000000000000000..b2bd32f2958c6b0faf72e73f28ffe53bc3b078dd --- /dev/null +++ b/webapp/test/widget_test.dart @@ -0,0 +1,30 @@ +// This is a basic Flutter widget test. +// +// To perform an interaction with a widget in your test, use the WidgetTester +// utility in the flutter_test package. For example, you can send tap and scroll +// gestures. You can also use WidgetTester to find child widgets in the widget +// tree, read text, and verify that the values of widget properties are correct. + +import 'package:flutter/material.dart'; +import 'package:flutter_test/flutter_test.dart'; + +import 'package:llminator/main.dart'; + +void main() { + testWidgets('Counter increments smoke test', (WidgetTester tester) async { + // Build our app and trigger a frame. + await tester.pumpWidget(const MyApp()); + + // Verify that our counter starts at 0. + expect(find.text('0'), findsOneWidget); + expect(find.text('1'), findsNothing); + + // Tap the '+' icon and trigger a frame. + await tester.tap(find.byIcon(Icons.add)); + await tester.pump(); + + // Verify that our counter has incremented. + expect(find.text('0'), findsNothing); + expect(find.text('1'), findsOneWidget); + }); +} diff --git a/webapp/web/favicon.png b/webapp/web/favicon.png new file mode 100644 index 0000000000000000000000000000000000000000..8aaa46ac1ae21512746f852a42ba87e4165dfdd1 Binary files /dev/null and b/webapp/web/favicon.png differ diff --git a/webapp/web/icons/Icon-192.png b/webapp/web/icons/Icon-192.png new file mode 100644 index 0000000000000000000000000000000000000000..b749bfef07473333cf1dd31e9eed89862a5d52aa Binary files /dev/null and b/webapp/web/icons/Icon-192.png differ diff --git a/webapp/web/icons/Icon-512.png b/webapp/web/icons/Icon-512.png new file mode 100644 index 0000000000000000000000000000000000000000..88cfd48dff1169879ba46840804b412fe02fefd6 Binary files /dev/null and b/webapp/web/icons/Icon-512.png differ diff --git a/webapp/web/icons/Icon-maskable-192.png b/webapp/web/icons/Icon-maskable-192.png new file mode 100644 index 0000000000000000000000000000000000000000..eb9b4d76e525556d5d89141648c724331630325d Binary files /dev/null and b/webapp/web/icons/Icon-maskable-192.png differ diff --git a/webapp/web/icons/Icon-maskable-512.png b/webapp/web/icons/Icon-maskable-512.png new file mode 100644 index 0000000000000000000000000000000000000000..d69c56691fbdb0b7efa65097c7cc1edac12a6d3e Binary files /dev/null and b/webapp/web/icons/Icon-maskable-512.png differ diff --git a/webapp/web/index.html b/webapp/web/index.html new file mode 100644 index 0000000000000000000000000000000000000000..d13a0f6347197dd4907c828271f70e42fa9397a3 --- /dev/null +++ b/webapp/web/index.html @@ -0,0 +1,82 @@ + + + + +