harris1 commited on
Commit
61ba1ac
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1 Parent(s): 6f1e80f

Update app.py

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  1. app.py +251 -249
app.py CHANGED
@@ -285,12 +285,219 @@
285
 
286
  # if __name__ == "__main__":
287
  # iface.launch()
288
- # from flask import Flask, request, jsonify, render_template_string
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
289
  # import os
290
  # from mistralai.client import MistralClient
291
  # from mistralai.models.chat_completion import ChatMessage
292
-
293
- # app = Flask(__name__)
294
 
295
  # # Mistral AI setup
296
  # api_key = os.getenv("MISTRAL_API_KEY")
@@ -331,80 +538,20 @@
331
  # const width = 1200;
332
  # const height = 800;
333
  # const goals = [
334
- # { 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." },
335
- # { 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()." },
336
- # { 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." },
337
- # { 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." },
338
- # { 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." },
339
- # { 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." },
340
- # { 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." },
341
- # { 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." },
342
- # { 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." },
343
- # { 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." },
344
- # { 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." },
345
- # { 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." },
346
- # { 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." },
347
- # { 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." },
348
- # { 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." },
349
- # { 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." },
350
- # { 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." },
351
- # { 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." },
352
- # { 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." },
353
- # { 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." },
354
- # { 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." },
355
- # { 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." },
356
- # { 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." },
357
- # { 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." },
358
- # { 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." },
359
- # { 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." },
360
- # { 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." },
361
- # { 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." },
362
- # { 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." },
363
- # { 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." }
364
- # ];
365
- # const connections = [
366
- # { source: 1, target: 2 },
367
- # { source: 2, target: 3 },
368
- # { source: 3, target: 4 },
369
- # { source: 4, target: 5 },
370
- # { source: 5, target: 7 },
371
- # { source: 6, target: 7 },
372
- # { source: 7, target: 8 },
373
- # { source: 8, target: 9 },
374
- # { source: 9, target: 16 },
375
- # { source: 10, target: 13 },
376
- # { source: 11, target: 12 },
377
- # { source: 12, target: 20 },
378
- # { source: 13, target: 16 },
379
- # { source: 14, target: 21 },
380
- # { source: 15, target: 17 },
381
- # { source: 16, target: 18 },
382
- # { source: 17, target: 19 },
383
- # { source: 18, target: 22 },
384
- # { source: 19, target: 21 },
385
- # { source: 20, target: 29 },
386
- # { source: 21, target: 30 },
387
- # { source: 22, target: 23 },
388
- # { source: 23, target: 25 },
389
- # { source: 24, target: 12 },
390
- # { source: 25, target: 23 },
391
- # { source: 26, target: 15 },
392
- # { source: 27, target: 15 },
393
- # { source: 28, target: 22 },
394
- # { source: 29, target: 23 },
395
- # { source: 30, target: 21 },
396
- # // Additional connections for more interconnectivity
397
- # { source: 1, target: 10 },
398
- # { source: 2, target: 6 },
399
- # { source: 3, target: 13 },
400
- # { source: 4, target: 15 },
401
- # { source: 5, target: 28 },
402
- # { source: 8, target: 23 },
403
- # { source: 11, target: 25 },
404
- # { source: 14, target: 30 },
405
- # { source: 24, target: 17 },
406
- # { source: 26, target: 29 }
407
- # ];
408
  # const svg = d3.select("#visualization")
409
  # .append("svg")
410
  # .attr("width", width)
@@ -424,7 +571,7 @@
424
  # .data(goals)
425
  # .enter().append("circle")
426
  # .attr("r", 10)
427
- # .attr("fill", d => d.color)
428
  # .call(d3.drag()
429
  # .on("start", dragstarted)
430
  # .on("drag", dragged)
@@ -478,179 +625,34 @@
478
  # </html>
479
  # """
480
 
481
- # @app.route('/')
482
- # def index():
483
- # return render_template_string(html_content)
484
-
485
- # @app.route('/generate_goals', methods=['POST'])
486
- # def generate_goals_api():
487
- # input_var = request.json['input_var']
488
- # goals = generate_goals(input_var)
489
- # return jsonify({'goals': goals})
490
-
491
- # if __name__ == "__main__":
492
- # app.run(host='0.0.0.0', port=7860)
493
- from http.server import HTTPServer, SimpleHTTPRequestHandler
494
- from pyngrok import ngrok
495
- import os
496
- from mistralai.client import MistralClient
497
- from mistralai.models.chat_completion import ChatMessage
498
- import json
499
-
500
- # Mistral AI setup
501
- api_key = os.getenv("MISTRAL_API_KEY")
502
- if not api_key:
503
- raise ValueError("MISTRAL_API_KEY environment variable not set")
504
-
505
- model = "mistral-tiny"
506
- client = MistralClient(api_key=api_key)
507
-
508
- def generate_goals(input_var):
509
- messages = [
510
- 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.")
511
- ]
512
- try:
513
- response = client.chat(model=model, messages=messages)
514
- return response.choices[0].message.content
515
- except Exception as e:
516
- return f"An error occurred: {str(e)}"
517
-
518
- html_content = """
519
- <!DOCTYPE html>
520
- <html lang="en">
521
- <head>
522
- <meta charset="UTF-8">
523
- <meta name="viewport" content="width=device-width, initial-scale=1.0">
524
- <title>Exam Data Analysis Goals Generator</title>
525
- <script src="https://d3js.org/d3.v7.min.js"></script>
526
- <style>
527
- #visualization { width: 100%; height: 600px; border: 1px solid #ccc; }
528
- #generatedGoals { margin-top: 20px; padding: 10px; border: 1px solid #ccc; }
529
- </style>
530
- </head>
531
- <body>
532
- <h1>Exam Data Analysis Goals Generator</h1>
533
- <div id="visualization"></div>
534
- <div id="generatedGoals"></div>
535
- <script>
536
- const width = 1200;
537
- const height = 800;
538
- const goals = [
539
- { 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." },
540
- { 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()." },
541
- { 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." },
542
- { 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." },
543
- { 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." },
544
- // Add more goals here...
545
- ];
546
- const connections = [
547
- { source: 1, target: 2 },
548
- { source: 2, target: 3 },
549
- { source: 3, target: 4 },
550
- { source: 4, target: 5 },
551
- // Add more connections here...
552
- ];
553
- const svg = d3.select("#visualization")
554
- .append("svg")
555
- .attr("width", width)
556
- .attr("height", height);
557
- const simulation = d3.forceSimulation(goals)
558
- .force("link", d3.forceLink(connections).id(d => d.id))
559
- .force("charge", d3.forceManyBody().strength(-400))
560
- .force("center", d3.forceCenter(width / 2, height / 2));
561
- const link = svg.append("g")
562
- .selectAll("line")
563
- .data(connections)
564
- .enter().append("line")
565
- .attr("stroke", "#999")
566
- .attr("stroke-opacity", 0.6);
567
- const node = svg.append("g")
568
- .selectAll("circle")
569
- .data(goals)
570
- .enter().append("circle")
571
- .attr("r", 10)
572
- .attr("fill", d => d.color || "#69b3a2")
573
- .call(d3.drag()
574
- .on("start", dragstarted)
575
- .on("drag", dragged)
576
- .on("end", dragended));
577
- const text = svg.append("g")
578
- .selectAll("text")
579
- .data(goals)
580
- .enter().append("text")
581
- .text(d => d.name)
582
- .attr("font-size", "12px")
583
- .attr("dx", 12)
584
- .attr("dy", 4);
585
- node.on("click", async function(event, d) {
586
- const response = await fetch('/generate_goals', {
587
- method: 'POST',
588
- headers: { 'Content-Type': 'application/json' },
589
- body: JSON.stringify({ input_var: d.name })
590
- });
591
- const data = await response.json();
592
- document.getElementById("generatedGoals").innerHTML = `<h2>Generated Goals for ${d.name}</h2><pre>${data.goals}</pre>`;
593
- });
594
- simulation.on("tick", () => {
595
- link
596
- .attr("x1", d => d.source.x)
597
- .attr("y1", d => d.source.y)
598
- .attr("x2", d => d.target.x)
599
- .attr("y2", d => d.target.y);
600
- node
601
- .attr("cx", d => d.x)
602
- .attr("cy", d => d.y);
603
- text
604
- .attr("x", d => d.x)
605
- .attr("y", d => d.y);
606
- });
607
- function dragstarted(event) {
608
- if (!event.active) simulation.alphaTarget(0.3).restart();
609
- event.subject.fx = event.subject.x;
610
- event.subject.fy = event.subject.y;
611
- }
612
- function dragged(event) {
613
- event.subject.fx = event.x;
614
- event.subject.fy = event.y;
615
- }
616
- function dragended(event) {
617
- if (!event.active) simulation.alphaTarget(0);
618
- event.subject.fx = null;
619
- event.subject.fy = null;
620
- }
621
- </script>
622
- </body>
623
- </html>
624
- """
625
 
626
- class MyHandler(SimpleHTTPRequestHandler):
627
- def do_GET(self):
628
- self.send_response(200)
629
- self.send_header('Content-type', 'text/html')
630
- self.end_headers()
631
- self.wfile.write(html_content.encode())
632
-
633
- def do_POST(self):
634
- if self.path == '/generate_goals':
635
- content_length = int(self.headers['Content-Length'])
636
- post_data = self.rfile.read(content_length)
637
- data = json.loads(post_data.decode('utf-8'))
638
- input_var = data['input_var']
639
- goals = generate_goals(input_var)
640
 
641
- self.send_response(200)
642
- self.send_header('Content-type', 'application/json')
643
- self.end_headers()
644
- self.wfile.write(json.dumps({'goals': goals}).encode())
645
- else:
646
- self.send_error(404)
647
-
648
- if __name__ == '__main__':
649
- port = 7860
650
- server = HTTPServer(('', port), MyHandler)
651
- public_url = ngrok.connect(port).public_url
652
- print(f" * ngrok tunnel \"{public_url}\" -> \"http://127.0.0.1:{port}\"")
653
- server.serve_forever()
654
 
655
  # here
656
  # from http.server import HTTPServer, SimpleHTTPRequestHandler
 
285
 
286
  # if __name__ == "__main__":
287
  # iface.launch()
288
+ from flask import Flask, request, jsonify, render_template_string
289
+ import os
290
+ from mistralai.client import MistralClient
291
+ from mistralai.models.chat_completion import ChatMessage
292
+
293
+ app = Flask(__name__)
294
+
295
+ # Mistral AI setup
296
+ api_key = os.getenv("MISTRAL_API_KEY")
297
+ if not api_key:
298
+ raise ValueError("MISTRAL_API_KEY environment variable not set")
299
+
300
+ model = "mistral-tiny"
301
+ client = MistralClient(api_key=api_key)
302
+
303
+ def generate_goals(input_var):
304
+ messages = [
305
+ 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.")
306
+ ]
307
+ try:
308
+ response = client.chat(model=model, messages=messages)
309
+ return response.choices[0].message.content
310
+ except Exception as e:
311
+ return f"An error occurred: {str(e)}"
312
+
313
+ html_content = """
314
+ <!DOCTYPE html>
315
+ <html lang="en">
316
+ <head>
317
+ <meta charset="UTF-8">
318
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
319
+ <title>Exam Data Analysis Goals Generator</title>
320
+ <script src="https://d3js.org/d3.v7.min.js"></script>
321
+ <style>
322
+ #visualization { width: 100%; height: 600px; border: 1px solid #ccc; }
323
+ #generatedGoals { margin-top: 20px; padding: 10px; border: 1px solid #ccc; }
324
+ </style>
325
+ </head>
326
+ <body>
327
+ <h1>Exam Data Analysis Goals Generator</h1>
328
+ <div id="visualization"></div>
329
+ <div id="generatedGoals"></div>
330
+ <script>
331
+ const width = 1200;
332
+ const height = 800;
333
+ const goals = [
334
+ { 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." },
335
+ { 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()." },
336
+ { 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." },
337
+ { 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." },
338
+ { 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." },
339
+ { 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." },
340
+ { 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." },
341
+ { 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." },
342
+ { 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." },
343
+ { 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." },
344
+ { 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." },
345
+ { 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." },
346
+ { 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." },
347
+ { 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." },
348
+ { 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." },
349
+ { 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." },
350
+ { 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." },
351
+ { 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." },
352
+ { 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." },
353
+ { 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." },
354
+ { 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." },
355
+ { 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." },
356
+ { 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." },
357
+ { 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." },
358
+ { 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." },
359
+ { 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." },
360
+ { 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." },
361
+ { 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." },
362
+ { 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." },
363
+ { 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." }
364
+ ];
365
+ const connections = [
366
+ { source: 1, target: 2 },
367
+ { source: 2, target: 3 },
368
+ { source: 3, target: 4 },
369
+ { source: 4, target: 5 },
370
+ { source: 5, target: 7 },
371
+ { source: 6, target: 7 },
372
+ { source: 7, target: 8 },
373
+ { source: 8, target: 9 },
374
+ { source: 9, target: 16 },
375
+ { source: 10, target: 13 },
376
+ { source: 11, target: 12 },
377
+ { source: 12, target: 20 },
378
+ { source: 13, target: 16 },
379
+ { source: 14, target: 21 },
380
+ { source: 15, target: 17 },
381
+ { source: 16, target: 18 },
382
+ { source: 17, target: 19 },
383
+ { source: 18, target: 22 },
384
+ { source: 19, target: 21 },
385
+ { source: 20, target: 29 },
386
+ { source: 21, target: 30 },
387
+ { source: 22, target: 23 },
388
+ { source: 23, target: 25 },
389
+ { source: 24, target: 12 },
390
+ { source: 25, target: 23 },
391
+ { source: 26, target: 15 },
392
+ { source: 27, target: 15 },
393
+ { source: 28, target: 22 },
394
+ { source: 29, target: 23 },
395
+ { source: 30, target: 21 },
396
+ // Additional connections for more interconnectivity
397
+ { source: 1, target: 10 },
398
+ { source: 2, target: 6 },
399
+ { source: 3, target: 13 },
400
+ { source: 4, target: 15 },
401
+ { source: 5, target: 28 },
402
+ { source: 8, target: 23 },
403
+ { source: 11, target: 25 },
404
+ { source: 14, target: 30 },
405
+ { source: 24, target: 17 },
406
+ { source: 26, target: 29 }
407
+ ];
408
+ const svg = d3.select("#visualization")
409
+ .append("svg")
410
+ .attr("width", width)
411
+ .attr("height", height);
412
+ const simulation = d3.forceSimulation(goals)
413
+ .force("link", d3.forceLink(connections).id(d => d.id))
414
+ .force("charge", d3.forceManyBody().strength(-400))
415
+ .force("center", d3.forceCenter(width / 2, height / 2));
416
+ const link = svg.append("g")
417
+ .selectAll("line")
418
+ .data(connections)
419
+ .enter().append("line")
420
+ .attr("stroke", "#999")
421
+ .attr("stroke-opacity", 0.6);
422
+ const node = svg.append("g")
423
+ .selectAll("circle")
424
+ .data(goals)
425
+ .enter().append("circle")
426
+ .attr("r", 10)
427
+ .attr("fill", d => d.color)
428
+ .call(d3.drag()
429
+ .on("start", dragstarted)
430
+ .on("drag", dragged)
431
+ .on("end", dragended));
432
+ const text = svg.append("g")
433
+ .selectAll("text")
434
+ .data(goals)
435
+ .enter().append("text")
436
+ .text(d => d.name)
437
+ .attr("font-size", "12px")
438
+ .attr("dx", 12)
439
+ .attr("dy", 4);
440
+ node.on("click", async function(event, d) {
441
+ const response = await fetch('/generate_goals', {
442
+ method: 'POST',
443
+ headers: { 'Content-Type': 'application/json' },
444
+ body: JSON.stringify({ input_var: d.name })
445
+ });
446
+ const data = await response.json();
447
+ document.getElementById("generatedGoals").innerHTML = `<h2>Generated Goals for ${d.name}</h2><pre>${data.goals}</pre>`;
448
+ });
449
+ simulation.on("tick", () => {
450
+ link
451
+ .attr("x1", d => d.source.x)
452
+ .attr("y1", d => d.source.y)
453
+ .attr("x2", d => d.target.x)
454
+ .attr("y2", d => d.target.y);
455
+ node
456
+ .attr("cx", d => d.x)
457
+ .attr("cy", d => d.y);
458
+ text
459
+ .attr("x", d => d.x)
460
+ .attr("y", d => d.y);
461
+ });
462
+ function dragstarted(event) {
463
+ if (!event.active) simulation.alphaTarget(0.3).restart();
464
+ event.subject.fx = event.subject.x;
465
+ event.subject.fy = event.subject.y;
466
+ }
467
+ function dragged(event) {
468
+ event.subject.fx = event.x;
469
+ event.subject.fy = event.y;
470
+ }
471
+ function dragended(event) {
472
+ if (!event.active) simulation.alphaTarget(0);
473
+ event.subject.fx = null;
474
+ event.subject.fy = null;
475
+ }
476
+ </script>
477
+ </body>
478
+ </html>
479
+ """
480
+
481
+ @app.route('/')
482
+ def index():
483
+ return render_template_string(html_content)
484
+
485
+ @app.route('/generate_goals', methods=['POST'])
486
+ def generate_goals_api():
487
+ input_var = request.json['input_var']
488
+ goals = generate_goals(input_var)
489
+ return jsonify({'goals': goals})
490
+
491
+ if __name__ == "__main__":
492
+ app.run(host='0.0.0.0', port=7860)
493
+
494
+ # imp
495
+ # from http.server import HTTPServer, SimpleHTTPRequestHandler
496
+ # from pyngrok import ngrok
497
  # import os
498
  # from mistralai.client import MistralClient
499
  # from mistralai.models.chat_completion import ChatMessage
500
+ # import json
 
501
 
502
  # # Mistral AI setup
503
  # api_key = os.getenv("MISTRAL_API_KEY")
 
538
  # const width = 1200;
539
  # const height = 800;
540
  # const goals = [
541
+ # { 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." },
542
+ # { 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()." },
543
+ # { 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." },
544
+ # { 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." },
545
+ # { 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." },
546
+ # // Add more goals here...
547
+ # ];
548
+ # const connections = [
549
+ # { source: 1, target: 2 },
550
+ # { source: 2, target: 3 },
551
+ # { source: 3, target: 4 },
552
+ # { source: 4, target: 5 },
553
+ # // Add more connections here...
554
+ # ];
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
555
  # const svg = d3.select("#visualization")
556
  # .append("svg")
557
  # .attr("width", width)
 
571
  # .data(goals)
572
  # .enter().append("circle")
573
  # .attr("r", 10)
574
+ # .attr("fill", d => d.color || "#69b3a2")
575
  # .call(d3.drag()
576
  # .on("start", dragstarted)
577
  # .on("drag", dragged)
 
625
  # </html>
626
  # """
627
 
628
+ # class MyHandler(SimpleHTTPRequestHandler):
629
+ # def do_GET(self):
630
+ # self.send_response(200)
631
+ # self.send_header('Content-type', 'text/html')
632
+ # self.end_headers()
633
+ # self.wfile.write(html_content.encode())
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
634
 
635
+ # def do_POST(self):
636
+ # if self.path == '/generate_goals':
637
+ # content_length = int(self.headers['Content-Length'])
638
+ # post_data = self.rfile.read(content_length)
639
+ # data = json.loads(post_data.decode('utf-8'))
640
+ # input_var = data['input_var']
641
+ # goals = generate_goals(input_var)
 
 
 
 
 
 
 
642
 
643
+ # self.send_response(200)
644
+ # self.send_header('Content-type', 'application/json')
645
+ # self.end_headers()
646
+ # self.wfile.write(json.dumps({'goals': goals}).encode())
647
+ # else:
648
+ # self.send_error(404)
649
+
650
+ # if __name__ == '__main__':
651
+ # port = 7860
652
+ # server = HTTPServer(('', port), MyHandler)
653
+ # public_url = ngrok.connect(port).public_url
654
+ # print(f" * ngrok tunnel \"{public_url}\" -> \"http://127.0.0.1:{port}\"")
655
+ # server.serve_forever()
656
 
657
  # here
658
  # from http.server import HTTPServer, SimpleHTTPRequestHandler