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harris1 commited on Jul 17

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5bcd5c3

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1 Parent(s): 408ec72

Update app.py

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Files changed (1) hide show

app.py +249 -88

app.py CHANGED Viewed

@@ -285,12 +285,217 @@
 # 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")
@@ -331,80 +536,20 @@ html_content = """
     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)
@@ -424,7 +569,7 @@ html_content = """
         .data(goals)
         .enter().append("circle")
         .attr("r", 10)
-        .attr("fill", d => d.color)
         .call(d3.drag()
             .on("start", dragstarted)
             .on("drag", dragged)
@@ -478,15 +623,31 @@ html_content = """
 </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)

 # 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)
+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")
     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)
         .data(goals)
         .enter().append("circle")
         .attr("r", 10)
+        .attr("fill", d => d.color || "#69b3a2")
         .call(d3.drag()
             .on("start", dragstarted)
             .on("drag", dragged)
 </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()