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| from http.server import HTTPServer, SimpleHTTPRequestHandler | |
| # from pyngrok import ngrok | |
| import os | |
| from mistralai.client import MistralClient | |
| from mistralai.models.chat_completion import ChatMessage | |
| import json | |
| # Mistral AI setup | |
| api_key = os.getenv("MISTRAL_API_KEY") | |
| if not api_key: | |
| raise ValueError("MISTRAL_API_KEY environment variable not set") | |
| model = "mistral-tiny" | |
| client = MistralClient(api_key=api_key) | |
| def generate_goals(input_var): | |
| messages = [ | |
| ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.") | |
| ] | |
| try: | |
| response = client.chat(model=model, messages=messages) | |
| return response.choices[0].message.content | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| html_content = """ | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Exam Data Analysis Goals Generator</title> | |
| <script src="https://d3js.org/d3.v7.min.js"></script> | |
| <style> | |
| #visualization { width: 100%; height: 600px; border: 1px solid #ccc; } | |
| #generatedGoals { margin-top: 20px; padding: 10px; border: 1px solid #ccc; } | |
| </style> | |
| </head> | |
| <body> | |
| <h1>Exam Data Analysis Goals Generator</h1> | |
| <div id="visualization"></div> | |
| <div id="generatedGoals"></div> | |
| <script> | |
| const width = 1200; | |
| const height = 800; | |
| const goals = [ | |
| { id: 1, x: 100, y: 400, name: "Automate Data Import", description: "Develop scripts to automate exam data extraction from various sources (CSV, Excel, databases) using Pandas read_* functions." }, | |
| { id: 2, x: 200, y: 300, name: "Data Cleaning", description: "Implement robust data cleaning processes to handle missing values, outliers, and inconsistencies in exam data using Pandas methods like dropna(), fillna(), and apply()." }, | |
| { id: 3, x: 300, y: 200, name: "Data Transformation", description: "Utilize Pandas for complex data transformations such as pivoting exam results, melting question-wise scores, and creating derived features for analysis." }, | |
| { id: 4, x: 400, y: 300, name: "Statistical Analysis", description: "Develop functions to automate statistical analysis of exam results, including descriptive statistics, hypothesis testing, and correlation analysis using Pandas and SciPy." }, | |
| { id: 5, x: 500, y: 400, name: "Performance Metrics", description: "Create custom functions to calculate industry-standard exam performance metrics like item difficulty, discrimination index, and reliability coefficients using Pandas operations." }, | |
| { id: 6, x: 200, y: 500, name: "Data Filtering", description: "Implement advanced filtering techniques to segment exam data based on various criteria (e.g., demographic info, score ranges) using boolean indexing and query() method in Pandas." }, | |
| { id: 7, x: 300, y: 600, name: "Reporting Automation", description: "Develop automated reporting systems that use Pandas groupby() and agg() functions to generate summary statistics and performance reports for different exam cohorts." }, | |
| { id: 8, x: 400, y: 500, name: "Data Visualization", description: "Create interactive dashboards for exam data visualization using Pandas with Plotly or Bokeh, allowing stakeholders to explore results dynamically." }, | |
| { id: 9, x: 500, y: 600, name: "Time Series Analysis", description: "Implement time series analysis techniques using Pandas datetime functionality to track and forecast exam performance trends over multiple test administrations." }, | |
| { id: 10, x: 300, y: 400, name: "Data Integration", description: "Develop processes to merge exam data with other relevant datasets (e.g., student information systems, learning management systems) using Pandas merge() and join() operations." }, | |
| { id: 11, x: 600, y: 300, name: "Performance Optimization", description: "Improve the efficiency of Pandas operations on large exam datasets by utilizing techniques like chunking, multiprocessing, and query optimization." }, | |
| { id: 12, x: 700, y: 400, name: "Machine Learning Integration", description: "Integrate machine learning models with Pandas for predictive analytics, such as predicting exam success or identifying at-risk students based on historical data." }, | |
| { id: 13, x: 800, y: 500, name: "Custom Indexing", description: "Implement custom indexing strategies in Pandas to efficiently handle hierarchical exam data structures and improve data access patterns." }, | |
| { id: 14, x: 900, y: 400, name: "Data Anonymization", description: "Develop Pandas-based workflows to anonymize sensitive exam data, ensuring compliance with privacy regulations while maintaining data utility for analysis." }, | |
| { id: 15, x: 1000, y: 300, name: "Exam Item Analysis", description: "Create specialized functions using Pandas to perform detailed item analysis, including distractor analysis and reliability calculations for individual exam questions." }, | |
| { id: 16, x: 600, y: 500, name: "Longitudinal Analysis", description: "Implement Pandas-based methods for tracking student performance across multiple exams over time, identifying learning trends and progress patterns." }, | |
| { id: 17, x: 700, y: 600, name: "Adaptive Testing Analysis", description: "Develop analysis pipelines using Pandas to evaluate and optimize adaptive testing algorithms, including item selection strategies and scoring methods." }, | |
| { id: 18, x: 800, y: 700, name: "Exam Equating", description: "Create Pandas workflows to perform exam equating, ensuring comparability of scores across different versions or administrations of an exam." }, | |
| { id: 19, x: 900, y: 600, name: "Response Time Analysis", description: "Utilize Pandas to analyze exam response times, identifying patterns that may indicate guessing, test-taking strategies, or item difficulty." }, | |
| { id: 20, x: 1000, y: 500, name: "Collaborative Filtering", description: "Implement collaborative filtering techniques using Pandas to recommend study materials or practice questions based on exam performance patterns." }, | |
| { id: 21, x: 400, y: 700, name: "Exam Fraud Detection", description: "Develop anomaly detection algorithms using Pandas to identify potential exam fraud or unusual response patterns in large-scale testing programs." }, | |
| { id: 22, x: 500, y: 800, name: "Standard Setting", description: "Create Pandas-based tools to assist in standard setting processes, analyzing expert judgments and examinee data to establish performance standards." }, | |
| { id: 23, x: 600, y: 700, name: "Automated Reporting", description: "Implement automated report generation using Pandas and libraries like Jinja2 to create customized, data-driven exam reports for various stakeholders." }, | |
| { id: 24, x: 700, y: 800, name: "Cross-validation", description: "Develop cross-validation frameworks using Pandas to assess the reliability and generalizability of predictive models in educational assessment contexts." }, | |
| { id: 25, x: 800, y: 300, name: "API Integration", description: "Create Pandas-based interfaces to integrate exam data analysis workflows with external APIs, facilitating real-time data exchange and reporting." }, | |
| { id: 26, x: 900, y: 200, name: "Natural Language Processing", description: "Implement NLP techniques using Pandas and libraries like NLTK to analyze free-text responses in exams, enabling automated scoring and content analysis." }, | |
| { id: 27, x: 1000, y: 100, name: "Exam Blueprint Analysis", description: "Develop Pandas workflows to analyze exam blueprints, ensuring content coverage and alignment with learning objectives across multiple test forms." }, | |
| { id: 28, x: 100, y: 600, name: "Differential Item Functioning", description: "Implement statistical methods using Pandas to detect and analyze differential item functioning (DIF) in exams, ensuring fairness across different demographic groups." }, | |
| { id: 29, x: 200, y: 700, name: "Automated Feedback Generation", description: "Create Pandas-based systems to generate personalized feedback for test-takers based on their exam performance and identified areas for improvement." }, | |
| { id: 30, x: 300, y: 800, name: "Exam Security Analysis", description: "Develop analytical tools using Pandas to assess and enhance exam security, including analysis of item exposure rates and detection of potential security breaches." } | |
| ]; | |
| const connections = [ | |
| { source: 1, target: 2 }, | |
| { source: 2, target: 3 }, | |
| { source: 3, target: 4 }, | |
| { source: 4, target: 5 }, | |
| { source: 5, target: 7 }, | |
| { source: 6, target: 7 }, | |
| { source: 7, target: 8 }, | |
| { source: 8, target: 9 }, | |
| { source: 9, target: 16 }, | |
| { source: 10, target: 13 }, | |
| { source: 11, target: 12 }, | |
| { source: 12, target: 20 }, | |
| { source: 13, target: 16 }, | |
| { source: 14, target: 21 }, | |
| { source: 15, target: 17 }, | |
| { source: 16, target: 18 }, | |
| { source: 17, target: 19 }, | |
| { source: 18, target: 22 }, | |
| { source: 19, target: 21 }, | |
| { source: 20, target: 29 }, | |
| { source: 21, target: 30 }, | |
| { source: 22, target: 23 }, | |
| { source: 23, target: 25 }, | |
| { source: 24, target: 12 }, | |
| { source: 25, target: 23 }, | |
| { source: 26, target: 15 }, | |
| { source: 27, target: 15 }, | |
| { source: 28, target: 22 }, | |
| { source: 29, target: 23 }, | |
| { source: 30, target: 21 }, | |
| // Additional connections for more interconnectivity | |
| { source: 1, target: 10 }, | |
| { source: 2, target: 6 }, | |
| { source: 3, target: 13 }, | |
| { source: 4, target: 15 }, | |
| { source: 5, target: 28 }, | |
| { source: 8, target: 23 }, | |
| { source: 11, target: 25 }, | |
| { source: 14, target: 30 }, | |
| { source: 24, target: 17 }, | |
| { source: 26, target: 29 } | |
| ]; | |
| const svg = d3.select("#visualization") | |
| .append("svg") | |
| .attr("width", width) | |
| .attr("height", height); | |
| const simulation = d3.forceSimulation(goals) | |
| .force("link", d3.forceLink(connections).id(d => d.id)) | |
| .force("charge", d3.forceManyBody().strength(-400)) | |
| .force("center", d3.forceCenter(width / 2, height / 2)); | |
| const link = svg.append("g") | |
| .selectAll("line") | |
| .data(connections) | |
| .enter().append("line") | |
| .attr("stroke", "#999") | |
| .attr("stroke-opacity", 0.6); | |
| const node = svg.append("g") | |
| .selectAll("circle") | |
| .data(goals) | |
| .enter().append("circle") | |
| .attr("r", 10) | |
| .attr("fill", d => d.color || "#69b3a2") | |
| .call(d3.drag() | |
| .on("start", dragstarted) | |
| .on("drag", dragged) | |
| .on("end", dragended)); | |
| const text = svg.append("g") | |
| .selectAll("text") | |
| .data(goals) | |
| .enter().append("text") | |
| .text(d => d.name) | |
| .attr("font-size", "12px") | |
| .attr("dx", 12) | |
| .attr("dy", 4); | |
| node.on("click", async function(event, d) { | |
| const response = await fetch('/generate_goals', { | |
| method: 'POST', | |
| headers: { 'Content-Type': 'application/json' }, | |
| body: JSON.stringify({ input_var: d.name }) | |
| }); | |
| const data = await response.json(); | |
| document.getElementById("generatedGoals").innerHTML = `<h2>Generated Goals for ${d.name}</h2><pre>${data.goals}</pre>`; | |
| }); | |
| simulation.on("tick", () => { | |
| link | |
| .attr("x1", d => d.source.x) | |
| .attr("y1", d => d.source.y) | |
| .attr("x2", d => d.target.x) | |
| .attr("y2", d => d.target.y); | |
| node | |
| .attr("cx", d => d.x) | |
| .attr("cy", d => d.y); | |
| text | |
| .attr("x", d => d.x) | |
| .attr("y", d => d.y); | |
| }); | |
| function dragstarted(event) { | |
| if (!event.active) simulation.alphaTarget(0.3).restart(); | |
| event.subject.fx = event.subject.x; | |
| event.subject.fy = event.subject.y; | |
| } | |
| function dragged(event) { | |
| event.subject.fx = event.x; | |
| event.subject.fy = event.y; | |
| } | |
| function dragended(event) { | |
| if (!event.active) simulation.alphaTarget(0); | |
| event.subject.fx = null; | |
| event.subject.fy = null; | |
| } | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| class MyHandler(SimpleHTTPRequestHandler): | |
| def do_GET(self): | |
| self.send_response(200) | |
| self.send_header('Content-type', 'text/html') | |
| self.end_headers() | |
| self.wfile.write(html_content.encode()) | |
| def do_POST(self): | |
| if self.path == '/generate_goals': | |
| content_length = int(self.headers['Content-Length']) | |
| post_data = self.rfile.read(content_length) | |
| data = json.loads(post_data.decode('utf-8')) | |
| input_var = data['input_var'] | |
| goals = generate_goals(input_var) | |
| self.send_response(200) | |
| self.send_header('Content-type', 'application/json') | |
| self.end_headers() | |
| self.wfile.write(json.dumps({'goals': goals}).encode()) | |
| else: | |
| self.send_error(404) | |
| if __name__ == '__main__': | |
| port = 7860 | |
| server = HTTPServer(('', port), MyHandler) | |
| # public_url = ngrok.connect(port).public_url | |
| # print(f" * ngrok tunnel \"{public_url}\" -> \"http://127.0.0.1:{port}\"") | |
| server.serve_forever() |