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harris1 commited on Jul 21, 2024

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Update app.py

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@@ -1,497 +1,3 @@
-# import gradio as gr
-# import os
-# from mistralai.client import MistralClient
-# from mistralai.models.chat_completion import ChatMessage
-# # Ensure the environment variable for the API key is set
-# 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 10 specific, industry relevant goals for {input_var} using Python and Pandas. Each goal should include a brief name and a one-sentence description of the task or skill. Focus on practical applications in educational assessment, covering areas such as data processing, statistical analysis, visualization, and advanced techniques")
-#     ]
-#     try:
-#         response = client.chat(model=model, messages=messages)
-#         content = response.choices[0].message.content
-#         return content
-#     except Exception as e:
-#         return f"An error occurred: {str(e)}"
-# # HTML content
-# html_content = """
-# <!DOCTYPE html>
-# <html lang="en">
-# <head>
-#     <meta charset="UTF-8">
-#     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-#     <title>Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections</title>
-#     <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/7.8.5/d3.min.js"></script>
-#     <style>
-#         body { font-family: Arial, sans-serif; margin: 20px; }
-#         #goalSpace { border: 1px solid #ccc; margin-bottom: 20px; }
-#         .goal { cursor: pointer; }
-#         #info { margin-top: 20px; font-weight: bold; }
-#         #selectedGoal { margin-top: 10px; padding: 10px; border: 1px solid #ccc; background-color: #f0f0f0; }
-#         #hoverInfo {
-#             position: absolute;
-#             padding: 10px;
-#             background-color: rgba(255, 255, 255, 0.9);
-#             border: 1px solid #ccc;
-#             border-radius: 5px;
-#             font-size: 14px;
-#             max-width: 300px;
-#             display: none;
-#         }
-#         #responseBox {
-#             margin-top: 20px;
-#             padding: 10px;
-#             border: 1px solid #ccc;
-#             background-color: #e0f7fa;
-#         }
-#     </style>
-# </head>
-# <body>
-#     <h1>Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections</h1>
-#     <div id="goalSpace"></div>
-#     <div id="info"></div>
-#     <div id="selectedGoal"></div>
-#     <div id="hoverInfo"></div>
-#     <div id="responseBox"></div>
-#     <script>
-#         const width = 1200;
-#         const height = 800;
-#         // Define the goals and connections data
-#         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 }
-#         ];
-#         // Create the SVG container for the goals and connections
-#         const svg = d3.select("#goalSpace")
-#             .append("svg")
-#             .attr("width", width)
-#             .attr("height", height);
-#         // Draw connections between goals
-#         const links = svg.selectAll("line")
-#             .data(connections)
-#             .enter()
-#             .append("line")
-#             .attr("x1", d => goals.find(g => g.id === d.source).x)
-#             .attr("y1", d => goals.find(g => g.id === d.source).y)
-#             .attr("x2", d => goals.find(g => g.id === d.target).x)
-#             .attr("y2", d => goals.find(g => g.id === d.target).y)
-#             .attr("stroke", "#999")
-#             .attr("stroke-width", 1)
-#             .attr("stroke-opacity", 0.6);
-#         // Draw goal nodes
-#         const goalNodes = svg.selectAll("circle")
-#             .data(goals)
-#             .enter()
-#             .append("circle")
-#             .attr("cx", d => d.x)
-#             .attr("cy", d => d.y)
-#             .attr("r", 10)
-#             .attr("fill", d => {
-#                 if (d.id <= 10) return "blue";
-#                 if (d.id <= 20) return "green";
-#                 return "orange";
-#             })
-#             .attr("class", "goal");
-#         // Add labels to the goals
-#         const goalLabels = svg.selectAll("text")
-#             .data(goals)
-#             .enter()
-#             .append("text")
-#             .attr("x", d => d.x + 15)
-#             .attr("y", d => d.y)
-#             .text(d => d.name)
-#             .attr("font-size", "12px");
-#         // Hover info box
-#         const hoverInfo = d3.select("#hoverInfo");
-#         // Add hover effects on goal nodes
-#         goalNodes.on("mouseover", function(event, d) {
-#             d3.select(this).attr("r", 15);
-#             hoverInfo.style("display", "block")
-#                 .style("left", (event.pageX + 10) + "px")
-#                 .style("top", (event.pageY - 10) + "px")
-#                 .html(`<strong>${d.name}</strong><br>${d.description}`);
-#         }).on("mouseout", function() {
-#             d3.select(this).attr("r", 10);
-#             hoverInfo.style("display", "none");
-#         });
-#         // Handle click event on goal nodes
-#         goalNodes.on("click", async function(event, d) {
-#             updateSelectedGoalInfo(d);
-#             try {
-#                 const response = await fetch('generate_goals', {
-#                     method: 'POST',
-#                     headers: {
-#                         'Content-Type': 'application/json',
-#                     },
-#                     body: JSON.stringify({ input_var: d.name })
-#                 });
-#                 if (!response.ok) {
-#                     throw new Error(`HTTP error! status: ${response.status}`);
-#                 }
-#                 const data = await response.json();
-#                 displayResponse(data.content);
-#             } catch (error) {
-#                 console.error("There was an error fetching the response:", error);
-#                 displayResponse("An error occurred while generating the response.");
-#             }
-#         });
-#         // Function to update selected goal information
-#         function updateSelectedGoalInfo(goal) {
-#             const selectedGoalDiv = d3.select("#selectedGoal");
-#             selectedGoalDiv.html(`
-#                 <h3>${goal.name}</h3>
-#                 <p>${goal.description}</p>
-#             `);
-#         }
-#         // Function to display the response from the server
-#         function displayResponse(content) {
-#             const responseBox = d3.select("#responseBox");
-#             responseBox.html(`
-#                 <h2>Response</h2>
-#                 <p>${content}</p>
-#             `);
-#         }
-#         // Handle mouse move event to highlight the closest goal
-#         svg.on("mousemove", function(event) {
-#             const [x, y] = d3.pointer(event);
-#             const closest = findClosestGoal(x, y);
-#             highlightClosestGoal(closest);
-#         });
-#         // Function to find the closest goal to the mouse pointer
-#         function findClosestGoal(x, y) {
-#             return goals.reduce((closest, goal) => {
-#                 const distance = Math.sqrt(Math.pow(goal.x - x, 2) + Math.pow(goal.y - y, 2));
-#                 return distance < closest.distance ? { goal, distance } : closest;
-#             }, { goal: null, distance: Infinity }).goal;
-#         }
-#         // Function to highlight the closest goal
-#         function highlightClosestGoal(goal) {
-#             d3.select("#info").html(`Closest goal: ${goal.name}`);
-#         }
-#     </script>
-# </body>
-# </html>
-# """
-# # Gradio interface
-# iface = gr.Interface(
-#     fn=generate_goals,
-#     inputs=gr.Textbox(label="Goal Name"),
-#     outputs=gr.Textbox(label="Generated Goals"),
-#     title="Exam Data Analysis Goals Generator",
-#     description="Click on a goal in the visualization to generate related goals.",
-#     allow_flagging="never",
-#     theme="default",
-#     css=html_content
-# )
-# if __name__ == "__main__":
-#     iface.launch()
-# from flask import Flask, request, jsonify, render_template_string
-# import os
-# from mistralai.client import MistralClient
-# from mistralai.models.chat_completion import ChatMessage
-# app = Flask(__name__)
-# # 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)
-#         .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>
-# """
-# @app.route('/')
-# def index():
-#     return render_template_string(html_content)
-# @app.route('/generate_goals', methods=['POST'])
-# def generate_goals_api():
-#     input_var = request.json['input_var']
-#     goals = generate_goals(input_var)
-#     return jsonify({'goals': goals})
-# if __name__ == "__main__":
-#     app.run(host='0.0.0.0', port=7860)
-# imp
 from http.server import HTTPServer, SimpleHTTPRequestHandler
 # from pyngrok import ngrok
 import os
@@ -537,21 +43,81 @@ html_content = """
     <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." },
-        // Add more goals here...
-    ];
-    const connections = [
-        { source: 1, target: 2 },
-        { source: 2, target: 3 },
-        { source: 3, target: 4 },
-        { source: 4, target: 5 },
-        // Add more connections here...
-    ];
     const svg = d3.select("#visualization")
         .append("svg")
         .attr("width", width)
@@ -652,178 +218,4 @@ if __name__ == '__main__':
     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()
-# here
-# from http.server import HTTPServer, SimpleHTTPRequestHandler
-# 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." },
-#         // Add more goals here...
-#     ];
-#     const connections = [
-#         { source: 1, target: 2 },
-#         { source: 2, target: 3 },
-#         { source: 3, target: 4 },
-#         { source: 4, target: 5 },
-#         // Add more connections here...
-#     ];
-#     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':
-#             try:
-#                 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)
-#                 response = json.dumps({'goals': goals}).encode()
-#                 self.send_response(200)
-#                 self.send_header('Content-type', 'application/json')
-#                 self.send_header('Content-Length', str(len(response)))
-#                 self.end_headers()
-#                 self.wfile.write(response)
-#             except Exception as e:
-#                 logging.error(f"Error handling POST request: {str(e)}")
-#                 self.send_error(500, f"Internal server error: {str(e)}")
-#         else:
-#             self.send_error(404)
-# def run_server(port):
-#     try:
-#         server = ThreadingHTTPServer(('0.0.0.0', port), MyHandler)
-#         print(f"Server running on port {port}")
-#         server.serve_forever()
-#     except Exception as e:
-#         logging.error(f"Error running server: {str(e)}")
-#         traceback.print_exc()
-# if __name__ == '__main__':
-#     port = int(os.environ.get("PORT", 7860))
-#     run_server(port)

 from http.server import HTTPServer, SimpleHTTPRequestHandler
 # from pyngrok import ngrok
 import os
     <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)
     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()