diff --git a/Website/faq.html b/Website/faq.html index dcbb09694..3fdc4f9e7 100644 --- a/Website/faq.html +++ b/Website/faq.html @@ -1,233 +1,267 @@ - + - - + + - - - + + - - - - - - -
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- Frequently Asked Questions(FAQs) + Frequently Asked Questions(FAQs)

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- - What is machine learning? -
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Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve their performance over time without explicit programming.

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- - How does your machine learning model work? -
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Our model uses algorithms to analyze and learn patterns from data, which allows it to make predictions or decisions based on new, unseen data.

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- - What kind of data do you use? -
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We use diverse datasets, including structured and unstructured data, to train our models, ensuring they can generalize well across different scenarios.

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- - How accurate is your model? -
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+ + What is machine learning?
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The accuracy of our model varies by application and is evaluated using metrics like precision, recall, and F1 score. Detailed performance statistics are provided on our results page.

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- - Can I use your model for my own project? -
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Yes, our model is available for use through API access or by downloading the code, depending on the licensing agreement.

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- - What programming languages and tools do you use? -
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We primarily use Python with libraries like TensorFlow, Keras, and scikit-learn for model development, along with tools like Jupyter Notebooks for experimentation.

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+ Machine learning is a subset of artificial intelligence that + enables computers to learn from data and improve their performance + over time without explicit programming. +

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+ +
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+ + How does your machine learning model work?
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- - How do you handle data privacy? -
- + +
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+ Our model uses algorithms to analyze and learn patterns from data, + which allows it to make predictions or decisions based on new, + unseen data. +

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+ + What kind of data do you use?
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We prioritize data privacy and comply with relevant regulations. All data is anonymized, and sensitive information is securely managed.

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+ We use diverse datasets, including structured and unstructured + data, to train our models, ensuring they can generalize well + across different scenarios. +

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+ +
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+ + How accurate is your model?
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- - What types of problems can your model solve? -
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+ The accuracy of our model varies by application and is evaluated + using metrics like precision, recall, and F1 score. Detailed + performance statistics are provided on our results page. +

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+ +
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+ + Can I use your model for my own project?
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Our models are designed to tackle various problems, including classification, regression, clustering, and anomaly detection across multiple domains.

+ +
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+ Yes, our model is available for use through API access or by + downloading the code, depending on the licensing agreement. +

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+ +
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+ + What programming languages and tools do you use?
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- - Is there a community or support available? -
- + +
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+ We primarily use Python with libraries like TensorFlow, Keras, and + scikit-learn for model development, along with tools like Jupyter + Notebooks for experimentation. +

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+ +
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+ + How do you handle data privacy?
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Yes, we offer community support through forums and documentation. Users can also reach out for direct assistance via our contact page.

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+ We prioritize data privacy and comply with relevant regulations. + All data is anonymized, and sensitive information is securely + managed. +

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+ +
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+ + What types of problems can your model solve?
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- - How can I contribute to the project? -
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+ Our models are designed to tackle various problems, including + classification, regression, clustering, and anomaly detection + across multiple domains. +

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+ +
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+ + Is there a community or support available?
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We welcome contributions! You can participate by submitting code improvements, reporting issues, or suggesting features through our GitHub repository.

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+ Yes, we offer community support through forums and documentation. + Users can also reach out for direct assistance via our contact + page. +

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+ + How can I contribute to the project?
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+ We welcome contributions! You can participate by submitting code + improvements, reporting issues, or suggesting features through our + GitHub repository. +

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+ + + + diff --git a/Website/server/server.js b/Website/server/server.js index ca13b2e62..3631321f8 100644 --- a/Website/server/server.js +++ b/Website/server/server.js @@ -1,9 +1,9 @@ -import express from 'express'; -import fetch from 'node-fetch'; -import path from 'path'; -import { fileURLToPath } from 'url'; -import dotenv from 'dotenv'; -import router from './route.js'; +import express from "express"; +import fetch from "node-fetch"; +import path from "path"; +import { fileURLToPath } from "url"; +import dotenv from "dotenv"; +import router from "./route.js"; dotenv.config(); @@ -12,52 +12,64 @@ const __dirname = path.dirname(__filename); const GITHUB_TOKEN = process.env.GITHUB_TOKEN; const app = express(); -app.use(express.static(path.join(__dirname, '../'))); +app.use(express.static(path.join(__dirname, "../"))); -app.get('/api/github/repos/subdir', async (req, res) => { - const dirName = req.query.dir; - if (!dirName) { - return res.status(400).json({ error: "Directory name is required" }); - } +app.get("/api/github/repos/subdir", async (req, res) => { + const dirName = req.query.dir; + if (!dirName) { + return res.status(400).json({ error: "Directory name is required" }); + } - try { - const response = await fetch(`https://api.github.com/repos/recodehive/machine-learning-repos/contents/${dirName}`, { - headers: { - Authorization: `Bearer ${GITHUB_TOKEN}`, - }, - }); - if (!response.ok) { - const errorDetails = await response.text(); - throw new Error(`GitHub API error: ${response.status} - ${response.statusText}: ${errorDetails}`); - } - - const data = await response.json(); - res.json(data); - } catch (error) { - console.error(`Error fetching GitHub subdirectory contents for ${dirName}:`, error); - res.status(500).json({ error: error.message }); + try { + const response = await fetch( + `https://api.github.com/repos/recodehive/machine-learning-repos/contents/${dirName}`, + { + headers: { + Authorization: `Bearer ${GITHUB_TOKEN}`, + }, + } + ); + if (!response.ok) { + const errorDetails = await response.text(); + throw new Error( + `GitHub API error: ${response.status} - ${response.statusText}: ${errorDetails}` + ); } + + const data = await response.json(); + res.json(data); + } catch (error) { + console.error( + `Error fetching GitHub subdirectory contents for ${dirName}:`, + error + ); + res.status(500).json({ error: error.message }); + } }); -app.get('/api/github/repos', async (req, res) => { - try { - const response = await fetch('https://api.github.com/repos/recodehive/machine-learning-repos/contents/', { - headers: { - Authorization: `Bearer ${GITHUB_TOKEN}`, - }, - }); - if (!response.ok) { - const errorDetails = await response.text(); - throw new Error(`GitHub API error: ${response.status} - ${response.statusText}: ${errorDetails}`); - } - - const data = await response.json(); - res.json(data); - } catch (error) { - console.error('Error fetching GitHub directories:', error); - res.status(500).json({ error: error.message }); +app.get("/api/github/repos", async (req, res) => { + try { + const response = await fetch( + "https://api.github.com/repos/recodehive/machine-learning-repos/contents/", + { + headers: { + Authorization: `Bearer ${GITHUB_TOKEN}`, + }, + } + ); + if (!response.ok) { + const errorDetails = await response.text(); + throw new Error( + `GitHub API error: ${response.status} - ${response.statusText}: ${errorDetails}` + ); } -}); + const data = await response.json(); + res.json(data); + } catch (error) { + console.error("Error fetching GitHub directories:", error); + res.status(500).json({ error: error.message }); + } +}); export default app;