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| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| import pickle | |
| from gradio import Progress | |
| import numpy as np | |
| import subprocess | |
| import shutil | |
| import matplotlib.pyplot as plt | |
| from sklearn.metrics import roc_curve, auc | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| from sklearn.metrics import roc_auc_score | |
| from matplotlib.figure import Figure | |
| import csv | |
| # import os | |
| # Define the function to process the input file and model selection | |
| def process_file(model_name,inc_slider,progress=Progress(track_tqdm=True)): | |
| # progress = gr.Progress(track_tqdm=True) | |
| progress(0, desc="Pre-Processing...") | |
| # with open(file.name, 'r') as f: | |
| # content = f.read() | |
| # saved_test_dataset = "train.txt" | |
| # saved_test_label = "train_label.txt" | |
| # saved_train_info="train_info.txt" | |
| # Save the uploaded file content to a specified location | |
| # shutil.copyfile(file.name, saved_test_dataset) | |
| # shutil.copyfile(label.name, saved_test_label) | |
| # shutil.copyfile(info.name, saved_train_info) | |
| parent_location="ratio_proportion_change3_2223/sch_largest_100-coded/finetuning/" | |
| test_info_location=parent_location+"overallTestData/test_info.txt" | |
| test_location=parent_location+"overallTestData/test.txt" | |
| label_location=parent_location+"overallTestData/test_label.txt" | |
| # "ASTRA-FT-HGR-RANDOM10", "ASTRA-FT-FIRST10-WSKILLS", "ASTRA-FT-FIRST10-WTIME", "ASTRA-FT-FIRST10-WSKILLS_WTIME" | |
| checkpoint = "ratio_proportion_change3_2223/sch_largest_100-coded/output/" | |
| if(model_name=="ASTRA-FT-HGR"): | |
| finetune_task="highGRschool10" | |
| checkpoint += "highGRschool10/bert_fine_tuned.model.ep42" | |
| model_task="ASTRA-FT-HGR-RANDOM10" | |
| elif(model_name== "ASTRA-FT-LGR" ): | |
| finetune_task="lowGRschoolAll" | |
| model_task="ASTRA-FT-LGR" | |
| elif(model_name=="ASTRA-FT-FULL"): | |
| finetune_task="fullTest" | |
| model_task="ASTRA-FT-FULL" | |
| elif(model_name in ["ASTRA-FT-Skills", "ASTRA-FT-Temporal", "ASTRA-FT-Unified"]): | |
| finetune_task="first10" | |
| if model_name == "ASTRA-FT-Skills": | |
| checkpoint += "first10/bert_fine_tuned.model.first10%.wskills.ep24" | |
| model_task="ASTRA-FT-FIRST10-WSKILLS" | |
| elif model_name == "ASTRA-FT-Temporal": | |
| checkpoint += "first10/bert_fine_tuned.model.first10%.wfaopttime.wttime.wttopttime.wttnoopttime.ep23" | |
| model_task="ASTRA-FT-FIRST10-WTIME" | |
| elif model_name == "ASTRA-FT-Unified": | |
| checkpoint += "first10/bert_fine_tuned.model.first10%.wskills.wfaopttime.wttime.wttopttime.wttnoopttime.ep40" | |
| model_task="ASTRA-FT-FIRST10-WSKILLS_WTIME" | |
| else: | |
| finetune_task=None | |
| # Load the test_info file and the graduation rate file | |
| test_info = pd.read_csv(test_info_location, sep=',', header=None, engine='python') | |
| def convert_etalon(x): | |
| means_and_extremes = 1 | |
| if x.is_integer(): | |
| means_and_extremes = 0 | |
| return means_and_extremes | |
| test_info[8] = test_info[7].apply(convert_etalon) # 7th column contains etalon of factor which decides the ER/ME problem type | |
| grad_rate_data = pd.DataFrame(pd.read_pickle('assests/school_grduation_rate.pkl'),columns=['school_number','grad_rate']) # Load the grad_rate data | |
| # Step 1: Extract unique school numbers from test_info | |
| unique_schools = test_info[0].unique() | |
| # Step 2: Filter the grad_rate_data using the unique school numbers | |
| schools = grad_rate_data[grad_rate_data['school_number'].isin(unique_schools)] | |
| # Define a threshold for high and low graduation rates (adjust as needed) | |
| grad_rate_threshold = 0.8 #0.9 | |
| # Step 4: Divide schools into high and low graduation rate groups | |
| high_grad_schools = schools[schools['grad_rate'] >= grad_rate_threshold]['school_number'].unique() | |
| low_grad_schools = schools[schools['grad_rate'] < grad_rate_threshold]['school_number'].unique() | |
| # Step 5: Sample percentage of schools from each group | |
| high_sample = pd.Series(high_grad_schools).sample(frac=inc_slider/100).tolist() | |
| low_sample = pd.Series(low_grad_schools).sample(frac=inc_slider/100).tolist() | |
| # Step 6: Combine the sampled schools | |
| random_schools = high_sample + low_sample | |
| # Step 7: Get indices for the sampled schools | |
| indices = test_info[test_info[0].isin(random_schools)].index.tolist() | |
| high_indices = test_info[(test_info[0].isin(high_sample))].index.tolist() | |
| low_indices = test_info[(test_info[0].isin(low_sample))].index.tolist() | |
| # Load the test file and select rows based on indices | |
| test = pd.read_csv(test_location, sep=',', header=None, engine='python') | |
| selected_rows_df2 = test.loc[indices] | |
| label=pd.read_csv(label_location, header=None, engine='python') | |
| test_label=label.loc[indices] | |
| test_label.to_csv(parent_location+finetune_task+'/test_label.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') | |
| # Save the selected rows to a file | |
| selected_rows_df2.to_csv('fileHandler/selected_rows.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') | |
| # ✅ Get the first 20% and last 20% of instances for each student ID within selected schools | |
| selected_test_info = test_info.loc[indices] | |
| selected_test_info.to_csv(parent_location+finetune_task+'/test_info.txt', sep=',', index=False, header=False, quoting=3, escapechar=' ') | |
| selected_rows_df2.to_csv(parent_location+finetune_task+'/test.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') | |
| # # First 20% | |
| # first_20_percent_indices = selected_test_info.groupby(3).apply( | |
| # lambda x: x.head(int(len(x) * 0.2)) | |
| # ).index.get_level_values(1).tolist() | |
| # # Last 20% | |
| # last_20_percent_indices = selected_test_info.groupby(3).apply( | |
| # lambda x: x.tail(int(len(x) * 0.2)) | |
| # ).index.get_level_values(1).tolist() | |
| # # Select the corresponding rows from the test file | |
| # first_20_percent_rows = test.loc[first_20_percent_indices] | |
| # last_20_percent_rows = test.loc[last_20_percent_indices] | |
| # # Save the first 20% instances per student to a file | |
| # first_20_percent_rows.to_csv('fileHandler/selected_rows_first20.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') | |
| # # Save the last 20% instances per student to a file | |
| # last_20_percent_rows.to_csv('fileHandler/selected_rows_last20.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') | |
| # select the graduation groups | |
| graduation_groups = [ | |
| 'high' if idx in high_indices else 'low' for idx in selected_rows_df2.index | |
| ] | |
| # Group data by opt_task1 and opt_task2 based on test_info[6] | |
| opt_task_groups = ['opt_task1' if test_info.loc[idx, 8] == 0 else 'opt_task2' for idx in selected_rows_df2.index] | |
| progress(0.2, desc="Running fine-tuned models...") | |
| print("finetuned task: ",finetune_task) | |
| subprocess.run([ | |
| "python", "new_test_saved_finetuned_model.py", | |
| "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded", | |
| "-model_name", model_task, | |
| "-finetune_task", finetune_task, | |
| "-test_dataset_path","../../../../fileHandler/selected_rows.txt", | |
| # "-test_label_path","../../../../train_label.txt", | |
| "-finetuned_bert_classifier_checkpoint", checkpoint, | |
| "-s",str(128), | |
| "-e",str(1), | |
| "-b",str(1000) | |
| ]) | |
| progress(0.5,desc="Saving output files..") | |
| # Load tlb and plb | |
| with open("fileHandler/tlabels_plabels.pkl", "rb") as f: | |
| tlb, plb = pickle.load(f) | |
| print("t==p = 0: ", sum([t==p for t,p in zip(tlb, plb) if t==0])) | |
| print("t==p = 1: ", sum([t==p for t,p in zip(tlb, plb) if t==1])) | |
| # Define function to filter and write CSV | |
| def process_and_write_csv(filtered_data, filename): | |
| headers = [ | |
| "Row", "Sample Name", "Transaction Id", "Anon Student Id", "Session Id", "Time Zone", "Duration (sec)", | |
| "Student Response Type", "Student Response Subtype", "Tutor Response Type", "Tutor Response Subtype", | |
| "Level (Workspace Id)", "Problem Name", "Problem View", "Problem Start Time", "Step Name", | |
| "Attempt At Step", "Is Last Attempt", "Outcome", "Selection", "Action", "Input", "Feedback Text", | |
| "Feedback Classification", "Help Level", "Total Num Hints", "KC (MATHia)", "KC Category (MATHia)", | |
| "KC (Single-KC)", "KC Category (Single-KC)", "KC (Unique-step)", "KC Category (Unique-step)", | |
| "School", "Class", "CF (Ruleid)", "CF (Semantic Event Id)", "CF (Skill New p-Known)", | |
| "CF (Skill Previous p-Known)", "CF (Workspace Progress Status)", "Event Type" | |
| ] | |
| with open("fileHandler/" + filename, 'w', newline='') as outfile: | |
| writer = csv.writer(outfile) | |
| writer.writerow(headers) | |
| row_num = 1 | |
| for _, row in filtered_data.iterrows(): | |
| # school, class_id, student_id, status, problem, _, time_zone, duration, attempts = row[:9] | |
| # sch_NPHBD11809,17,stu_CRJBA61379,GRADUATED,ratio_proportion_change3-134,[strategygame],1,4.0,4.0,10, | |
| # PercentChange-Attempt-1-0-OK-1667479255281 NumeratorQuantity1-Attempt-1-0-JIT-1667479268893 NumeratorQuantity1-Attempt-2-0-ERROR-1667479284199 NumeratorQuantity1-Attempt-3-0-OK-1667479294890 DenominatorQuantity1-Attempt-1-0-OK-1667479298749 NumeratorQuantity2-Attempt-1-0-OK-1667479301999 OptionalTask_1-Attempt-1-0-OK-1667479304886 DenominatorFactor-Attempt-1-0-OK-1667479314566 NumeratorFactor-Attempt-1-0-OK-1667479315579 EquationAnswer-Attempt-1-0-OK-1667479323750 FinalAnswerDirection-Attempt-1-0-OK-1667479333439 FinalAnswer-Attempt-1-0-OK-1667479338185, | |
| # 1, | |
| # 0 0.999767840033168 0 0 0.999996274310286 0 0.321529253998353 0.999722748307354 0.999840947031115, | |
| # 0 -0.0002057730279919623 0 0 -3.302306839980673e-06 0 -0.41429892410820995 -0.00022392554103201068 -0.00012846367037400164, | |
| # 0 0.999767840033168 0 0 0 0 0 0 0, | |
| # 1667479255281 1667479294890 1667479298749 1667479301999 1667479304886 1667479314566 1667479315579 1667479323750 1667479333439 1667479338185, | |
| # 0 39609 3859 3250 2887 9680 1013 8171 9689 4746, | |
| # 2887 9680 1013 8171,0 39609 3859 3250 9689 4746, | |
| # 14435, | |
| # 82904 | |
| school, prob_solved, student_id, status, problem, prob_type, opt_type, _, _ = row[:9] | |
| steps_data = row[10] | |
| # if row_num == 1: | |
| # print(row) | |
| for step in steps_data.split('\t'): | |
| step_parts = step.split('-') | |
| step_name = step_parts[0] | |
| action = step_parts[1] | |
| attempt = step_parts[2] | |
| help_level = step_parts[3] | |
| outcome = step_parts[4] | |
| curr_time = step_parts[5] | |
| row_data = [ | |
| row_num, "", "", student_id, "", curr_time, "", "", "", "", "", | |
| "ratio_proportion_change3", problem, "", "", step_name, attempt, "", outcome, "", action, "", | |
| "", "", help_level, "", "", "", "", "", "", "", | |
| school, "", "", "", "", "", status, "" | |
| ] | |
| writer.writerow(row_data) | |
| row_num += 1 | |
| print(f"CSV file '{filename}' created successfully.") | |
| # Find indices where conditions match | |
| for task_type in [0, 1]: # test_info[6] = 1 or 2 | |
| for label in [0, 1]: # tlb = plb = 0 or 1 | |
| matching_indices = [i for i in range(len(tlb)) if tlb[i] == plb[i] == label] | |
| # Filter the data | |
| filtered_data = selected_test_info.iloc[matching_indices] | |
| # new data contains etalon instead of 0/1 for ER/ME | |
| filtered_data = filtered_data[filtered_data[8] == task_type] # Ensure test_info[6] matches | |
| # Define filename dynamically | |
| task_type_map = {0: "ER", 1: "ME"} | |
| label_map = {0: "unsuccessful", 1: "successful"} | |
| filename = f"{task_type_map[task_type]}-{label_map[label]}-strategies.csv" | |
| # Write to CSV | |
| process_and_write_csv(filtered_data, filename) | |
| with open("fileHandler/roc_data2.pkl", 'rb') as file: | |
| data = pickle.load(file) | |
| t_label=data[0] | |
| p_label=data[1] | |
| # Step 1: Align graduation_group, t_label, and p_label | |
| aligned_labels = list(zip(graduation_groups, t_label, p_label)) | |
| opt_task_aligned = list(zip(opt_task_groups, t_label, p_label)) | |
| # Step 2: Separate the labels for high and low groups | |
| high_t_labels = [t for grad, t, p in aligned_labels if grad == 'high'] | |
| low_t_labels = [t for grad, t, p in aligned_labels if grad == 'low'] | |
| high_p_labels = [p for grad, t, p in aligned_labels if grad == 'high'] | |
| low_p_labels = [p for grad, t, p in aligned_labels if grad == 'low'] | |
| opt_task1_t_labels = [t for task, t, p in opt_task_aligned if task == 'opt_task1'] | |
| opt_task1_p_labels = [p for task, t, p in opt_task_aligned if task == 'opt_task1'] | |
| opt_task2_t_labels = [t for task, t, p in opt_task_aligned if task == 'opt_task2'] | |
| opt_task2_p_labels = [p for task, t, p in opt_task_aligned if task == 'opt_task2'] | |
| high_roc_auc = roc_auc_score(high_t_labels, high_p_labels) if len(set(high_t_labels)) > 1 else None | |
| low_roc_auc = roc_auc_score(low_t_labels, low_p_labels) if len(set(low_t_labels)) > 1 else None | |
| opt_task1_roc_auc = roc_auc_score(opt_task1_t_labels, opt_task1_p_labels) if len(set(opt_task1_t_labels)) > 1 else None | |
| opt_task2_roc_auc = roc_auc_score(opt_task2_t_labels, opt_task2_p_labels) if len(set(opt_task2_t_labels)) > 1 else None | |
| # For demonstration purposes, we'll just return the content with the selected model name | |
| # print(checkpoint) | |
| # if (inc_val<5): | |
| # model_name="highGRschool10" | |
| # elif(inc_val>=5 & inc_val<10): | |
| # model_name="highGRschool10" | |
| # else: | |
| # model_name="highGRschool10" | |
| # Function to analyze each row | |
| def analyze_row(row): | |
| # Split the row into fields | |
| fields = row.split("\t") | |
| # Define tasks for OptionalTask_1, OptionalTask_2, and FinalAnswer | |
| optional_task_1_subtasks = ["DenominatorFactor", "NumeratorFactor", "EquationAnswer"] | |
| optional_task_2_subtasks = [ | |
| "FirstRow2:1", "FirstRow2:2", "FirstRow1:1", "FirstRow1:2", | |
| "SecondRow", "ThirdRow" | |
| ] | |
| # Helper function to evaluate task attempts | |
| def evaluate_tasks(fields, tasks): | |
| task_status = {} | |
| total_attempts = 0 # Counter for total number of attempts | |
| attempted_tasks = 0 # Counter for tasks attempted at least once | |
| successful_first_attempts = 0 # Counter for tasks successful on the first try | |
| for task in tasks: | |
| relevant_attempts = [f for f in fields if task in f] | |
| # if (task=="FinalAnswer"): print(relevant_attempts) | |
| attempt_count = len(relevant_attempts) | |
| total_attempts += attempt_count # Add to the total attempts | |
| if attempt_count > 0: | |
| attempted_tasks += 1 # Increment attempted tasks count | |
| # Check the first attempt | |
| first_attempt = relevant_attempts[0] | |
| if "OK" in first_attempt and "ERROR" not in first_attempt and "JIT" not in first_attempt: | |
| successful_first_attempts += 1 | |
| if any("OK" in attempt for attempt in relevant_attempts): | |
| task_status[task] = "Attempted (Successful)" | |
| elif any("ERROR" in attempt for attempt in relevant_attempts): | |
| task_status[task] = "Attempted (Error)" | |
| elif any("JIT" in attempt for attempt in relevant_attempts): | |
| task_status[task] = "Attempted (JIT)" | |
| else: | |
| task_status[task] = "Unattempted" | |
| return task_status,attempted_tasks, total_attempts,successful_first_attempts | |
| # Evaluate tasks for each category | |
| optional_task_1_status, opt1_attempted, opt1_total_attempts, opt1_successful_first_attempts = evaluate_tasks(fields, optional_task_1_subtasks) | |
| optional_task_2_status, opt2_attempted, opt2_total_attempts, opt2_successful_first_attempts = evaluate_tasks(fields, optional_task_2_subtasks) | |
| final_status, final_attempted, final_total_attempts,final_sucessful_first_attempts=evaluate_tasks(fields,["FinalAnswer-Attempt"]) | |
| # print("/n",evaluate_tasks(fields,["FinalAnswer-Attempt"])) | |
| # Check if tasks have any successful attempt | |
| opt1_done = any(status == "Attempted (Successful)" for status in optional_task_1_status.values()) | |
| opt2_done = any(status == "Attempted (Successful)" for status in optional_task_2_status.values()) | |
| return (opt1_done, opt2_done, | |
| opt1_attempted, opt2_attempted, | |
| opt1_total_attempts, opt2_total_attempts, | |
| opt1_successful_first_attempts, opt2_successful_first_attempts,final_total_attempts) | |
| # Read data from test_info.txt | |
| with open(test_info_location, "r") as file: | |
| data = file.readlines() | |
| selected_data = [data[i] for i in indices if i < len(data)] | |
| # Assuming test_info[7] is a list with ideal tasks for each instance | |
| ideal_tasks = test_info[8] # A list where each element is either 1 or 2 | |
| # Initialize counters | |
| task_counts = { | |
| 1: {"ER": 0, "ME": 0, "both": 0}, | |
| 2: {"ER": 0, "ME": 0, "both": 0} | |
| } | |
| # To store task completion counts per row | |
| # Analyze rows | |
| row_summary = [] | |
| opt1_ratios = [] | |
| opt2_ratios = [] | |
| final_total=[] | |
| opt1_total=[] | |
| for i, row in enumerate(selected_data): | |
| row = row.strip() | |
| if not row: | |
| continue | |
| ideal_task = ideal_tasks[i] # Get the ideal task for the current row | |
| ( | |
| opt1_done, opt2_done, | |
| opt1_attempted, opt2_attempted, | |
| opt1_total_attempts, opt2_total_attempts, | |
| opt1_successful_first_attempts, opt2_successful_first_attempts,final_total_attemps | |
| ) = analyze_row(row) | |
| opt1_ratios.append( opt1_total_attempts / opt1_attempted if opt1_total_attempts > 0 else 0) | |
| opt2_ratios.append( opt2_total_attempts / opt2_attempted if opt2_total_attempts > 0 else 0) | |
| final_total.append(final_total_attemps) | |
| opt1_total.append(opt1_total_attempts) | |
| # create a summey for task:attempted, total attempts and succesful attempts for each row | |
| # row_summary.append({ | |
| # "row_index": i + 1, | |
| # "opt1": { | |
| # "tasks_attempted": opt1_attempted, | |
| # "total_attempts": opt1_total_attempts, | |
| # "successful_attempts": opt1_successful_first_attempts, | |
| # }, | |
| # "opt2": { | |
| # "tasks_attempted": opt2_attempted, | |
| # "total_attempts": opt2_total_attempts, | |
| # "successful_attempts": opt2_successful_first_attempts, | |
| # } | |
| # }) | |
| if ideal_task == 0: | |
| if opt1_done and not opt2_done: | |
| task_counts[1]["ER"] += 1 | |
| elif not opt1_done and opt2_done: | |
| task_counts[1]["ME"] += 1 | |
| elif opt1_done and opt2_done: | |
| task_counts[1]["both"] += 1 | |
| else: | |
| print(i) | |
| elif ideal_task == 1: | |
| if opt1_done and not opt2_done: | |
| task_counts[2]["ER"] += 1 | |
| elif not opt1_done and opt2_done: | |
| task_counts[2]["ME"] += 1 | |
| elif opt1_done and opt2_done: | |
| task_counts[2]["both"] += 1 | |
| else: | |
| print(i) | |
| # Print a summary of task completions | |
| # for summary in row_summary: | |
| # print(f"\nRow {summary['row_index']}:") | |
| # print(f" OptionalTask_1 - Tasks Attempted: {summary['opt1']['tasks_attempted']}, " | |
| # f"Total Attempts: {summary['opt1']['total_attempts']}, " | |
| # f"Successful Attempts: {summary['opt1']['successful_attempts']}") | |
| # print(f" OptionalTask_2 - Tasks Attempted: {summary['opt2']['tasks_attempted']}, " | |
| # f"Total Attempts: {summary['opt2']['total_attempts']}, " | |
| # f"Successful Attempts: {summary['opt2']['successful_attempts']}") | |
| # Create a string output for results | |
| # output_summary = "Task Analysis Summary:\n" | |
| # output_summary += "-----------------------\n" | |
| # for ideal_task, counts in task_counts.items(): | |
| # output_summary += f"Ideal Task = OptionalTask_{ideal_task}:\n" | |
| # output_summary += f" Only OptionalTask_1 done: {counts['ER']}\n" | |
| # output_summary += f" Only OptionalTask_2 done: {counts['ME']}\n" | |
| # output_summary += f" Both done: {counts['both']}\n" | |
| # Create figure | |
| fig_hist1 = go.Figure() | |
| # Add histogram for OptionalTask_1 (ER) | |
| fig_hist1.add_trace(go.Histogram( | |
| x=opt1_ratios, | |
| name="ER", | |
| marker=dict(color='blue'), | |
| opacity=1, | |
| xbins=dict( | |
| start=1.0, | |
| end=max(opt1_ratios) if max(opt1_ratios) < 15 else 15, | |
| size=1 # Bin width set to 0.1 for 10 bins | |
| ) | |
| )) | |
| fig_hist2=go.Figure() | |
| # Add histogram for OptionalTask_2 (ME) | |
| fig_hist2.add_trace(go.Histogram( | |
| x=opt2_ratios, | |
| name="ME", | |
| marker=dict(color='red'), | |
| opacity=1, | |
| xbins=dict( | |
| start=1.0, | |
| end=max(opt1_ratios) if max(opt1_ratios) < 15 else 15, | |
| size=1 # Bin width set to 0.1 for 10 bins | |
| ) | |
| )) | |
| # Update layout | |
| fig_hist1.update_layout( | |
| title="ER: Histogram of Attempts required per task", | |
| title_x=0.5, | |
| xaxis=dict( | |
| title="Success Ratio ( Total Attempts / Tasks Attempted )", | |
| tickmode="array", | |
| tickvals=list(range(1, 11)) + [15], # 1,2,3,...10, 15+ | |
| ticktext=[str(i) for i in range(1, 11)] + ["10+"], | |
| ), | |
| yaxis=dict( | |
| title="Number of Instances" | |
| ), | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| legend=dict( | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ) | |
| ), | |
| barmode='overlay' # Overlapping bars | |
| ) | |
| fig_hist2.update_layout( | |
| title="ME: Histogram of Attempts required per task", | |
| title_x=0.5, | |
| xaxis=dict( | |
| title="Success Ratio (Total Attempts / Tasks Attempted)", | |
| tickmode="array", | |
| tickvals=list(range(1, 11)) + [15], # 1,2,3,...10, 15+ | |
| ticktext=[str(i) for i in range(1, 11)] + ["10+"], | |
| ), | |
| yaxis=dict( | |
| title="Number of Instances" | |
| ), | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| legend=dict( | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ) | |
| ), | |
| barmode='overlay' # Overlapping bars | |
| ) | |
| # colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'] | |
| colors = ["#FF6F61", "#6B5B95", "#88B04B", "#F7CAC9"] | |
| # print(opt1_ratios,opt2_ratios) | |
| # fig_scatter = go.Figure() | |
| # fig_scatter.add_trace(go.Scatter( | |
| # x=final_total, | |
| # y=opt1_total, | |
| # mode='markers', | |
| # marker=dict(size=8, color='blue', opacity=0.7), | |
| # name="Student Data" | |
| # )) | |
| # # Update layout | |
| # fig_scatter.update_layout( | |
| # title="Scatter Plot: Final Total Attempts vs OptionalTask_1 Attempts", | |
| # title_x=0.5, | |
| # xaxis=dict(title="Final Total Attempts"), | |
| # yaxis=dict(title="OptionalTask_1 Total Attempts"), | |
| # font=dict(family="sans-serif", size=12, color="black"), | |
| # showlegend=True | |
| # ) | |
| # fig_scatter.show() | |
| # Generate pie chart for Task 1 | |
| task1_labels = list(task_counts[1].keys()) | |
| task1_values = list(task_counts[1].values()) | |
| # fig_task1 = Figure() | |
| # ax1 = fig_task1.add_subplot(1, 1, 1) | |
| # ax1.pie(task1_values, labels=task1_labels, autopct='%1.1f%%', startangle=90) | |
| # ax1.set_title('Ideal Task 1 Distribution') | |
| fig_task1 = go.Figure(data=[go.Pie( | |
| labels=task1_labels, | |
| values=task1_values, | |
| textinfo='percent+label', | |
| textposition='auto', | |
| marker=dict(colors=colors), | |
| sort=False | |
| )]) | |
| fig_task1.update_layout( | |
| title='Problem Type: ER', | |
| title_x=0.5, | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| ) | |
| fig_task1.update_layout( | |
| legend=dict( | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| ) | |
| ) | |
| # fig.show() | |
| # Generate pie chart for Task 2 | |
| task2_labels = list(task_counts[2].keys()) | |
| task2_values = list(task_counts[2].values()) | |
| fig_task2 = go.Figure(data=[go.Pie( | |
| labels=task2_labels, | |
| values=task2_values, | |
| textinfo='percent+label', | |
| textposition='auto', | |
| marker=dict(colors=colors), | |
| sort=False | |
| # pull=[0, 0.2, 0, 0] # for pulling part of pie chart out (depends on position) | |
| )]) | |
| fig_task2.update_layout( | |
| title='Problem Type: ME', | |
| title_x=0.5, | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| ) | |
| fig_task2.update_layout( | |
| legend=dict( | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| ) | |
| ) | |
| # fig_task2 = Figure() | |
| # ax2 = fig_task2.add_subplot(1, 1, 1) | |
| # ax2.pie(task2_values, labels=task2_labels, autopct='%1.1f%%', startangle=90) | |
| # ax2.set_title('Ideal Task 2 Distribution') | |
| # print(output_summary) | |
| result = {} | |
| with open("fileHandler/result.txt", 'r') as file: | |
| for line in file: | |
| key, value = line.strip().split(': ', 1) | |
| # print(type(key)) | |
| if key=='epoch': | |
| result[key]=value | |
| else: | |
| result[key]=float(value) | |
| result["ROC score of HGR"]=high_roc_auc | |
| result["ROC score of LGR"]=low_roc_auc | |
| # Create a plot | |
| with open("fileHandler/roc_data.pkl", "rb") as f: | |
| fpr, tpr, _ = pickle.load(f) | |
| # print(fpr,tpr) | |
| roc_auc = auc(fpr, tpr) | |
| # Create a matplotlib figure | |
| # fig = Figure() | |
| # ax = fig.add_subplot(1, 1, 1) | |
| # ax.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})') | |
| # ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') | |
| # ax.set(xlabel='False Positive Rate', ylabel='True Positive Rate', title=f'Receiver Operating Curve (ROC)') | |
| # ax.legend(loc="lower right") | |
| # ax.grid() | |
| fig = go.Figure() | |
| # Create and style traces | |
| fig.add_trace(go.Line(x = list(fpr), y = list(tpr), name=f'ROC curve (area = {roc_auc:.2f})', | |
| line=dict(color='royalblue', width=3, | |
| ) # dash options include 'dash', 'dot', and 'dashdot' | |
| )) | |
| fig.add_trace(go.Line(x = [0,1], y = [0,1], showlegend = False, | |
| line=dict(color='firebrick', width=2, | |
| dash='dash',) # dash options include 'dash', 'dot', and 'dashdot' | |
| )) | |
| # Edit the layout | |
| fig.update_layout( | |
| showlegend = True, | |
| title_x=0.5, | |
| title=dict( | |
| text='Receiver Operating Curve (ROC)' | |
| ), | |
| xaxis=dict( | |
| title=dict( | |
| text='False Positive Rate' | |
| ) | |
| ), | |
| yaxis=dict( | |
| title=dict( | |
| text='False Negative Rate' | |
| ) | |
| ), | |
| font=dict( | |
| family="sans-serif", | |
| color="black" | |
| ), | |
| ) | |
| fig.update_layout( | |
| legend=dict( | |
| x=0.75, | |
| y=0, | |
| traceorder="normal", | |
| font=dict( | |
| family="sans-serif", | |
| size=12, | |
| color="black" | |
| ), | |
| ) | |
| ) | |
| # Save plot to a file | |
| # plot_path = "plot.png" | |
| # fig.savefig(plot_path) | |
| # plt.close(fig) | |
| progress(1.0) | |
| # Prepare text output | |
| text_output = f"Model: {model_name}\nResult:\n{result}" | |
| # Prepare text output with HTML formatting | |
| text_output = f""" | |
| --------------------------- | |
| Model: {model_name} | |
| ---------------------------\n | |
| Number of schools sampled: {len(random_schools)} / {len(unique_schools)} | |
| Total number of instances from HGR schools : {len(high_indices)} | |
| Total number of instances from LGR schools: {len(low_indices)} | |
| ROC score of HGR: {high_roc_auc:.4f} | |
| ROC score of LGR: {low_roc_auc:.4f} | |
| ROC-AUC for problems of type ER: {opt_task1_roc_auc:.4f} | |
| ROC-AUC for problems of type ME: {opt_task2_roc_auc:.4f} | |
| """ | |
| # progress(0.5,desc="first k '%' sampling") | |
| # subprocess.run([ | |
| # "python", "new_test_saved_finetuned_model.py", | |
| # "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded", | |
| # "-finetune_task", finetune_task, | |
| # "-test_dataset_path","../../../../fileHandler/selected_rows_first20.txt", | |
| # # "-test_label_path","../../../../train_label.txt", | |
| # "-finetuned_bert_classifier_checkpoint", | |
| # "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42", | |
| # "-e",str(1), | |
| # "-b",str(1000) | |
| # ]) | |
| # with open("fileHandler/roc_data.pkl", "rb") as f: | |
| # fpr, tpr, _ = pickle.load(f) | |
| # # print(fpr,tpr) | |
| # roc_auc_first_k = auc(fpr, tpr) | |
| # print(roc_auc_first_k) | |
| # progress(0.5,desc="last '%' sampling") | |
| # subprocess.run([ | |
| # "python", "new_test_saved_finetuned_model.py", | |
| # "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded", | |
| # "-finetune_task", finetune_task, | |
| # "-test_dataset_path","../../../../fileHandler/selected_rows_last20.txt", | |
| # # "-test_label_path","../../../../train_label.txt", | |
| # "-finetuned_bert_classifier_checkpoint", | |
| # "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42", | |
| # "-e",str(1), | |
| # "-b",str(1000) | |
| # ]) | |
| # with open("fileHandler/roc_data.pkl", "rb") as f: | |
| # fpr, tpr, _ = pickle.load(f) | |
| # # print(fpr,tpr) | |
| # roc_auc_last_k = auc(fpr, tpr) | |
| # print(roc_auc_last_k) | |
| # text_output_sampled_auc = f""" | |
| # --------------------------- | |
| # Model: {model_name} | |
| # ---------------------------\n | |
| # ROC score of first 20% of problems: {roc_auc_first_k:.4f} | |
| # ROC score of last 20% of problems: {roc_auc_last_k:.4f} | |
| # """ | |
| # return text_output,text_output_sampled_auc,fig,fig_task1,fig_task2,fig_hist1,fig_hist2 | |
| return text_output,fig,fig_task1,fig_task2 | |
| # List of models for the dropdown menu | |
| # models = ["ASTRA-FT-HGR", "ASTRA-FT-LGR", "ASTRA-FT-FULL"] | |
| # models = ["ASTRA-FT-HGR", "ASTRA-FT-FULL"] | |
| models = ["ASTRA-FT-HGR", "ASTRA-FT-Skills", "ASTRA-FT-Temporal", "ASTRA-FT-Unified"] | |
| content = """ | |
| <h1 style="color: black;">A S T R A</h1> | |
| <h2 style="color: black;">An AI Model for Analyzing Math Strategies</h2> | |
| <h3 style="color: white; text-align: center"> | |
| <a href="https://drive.google.com/file/d/1lbEpg8Se1ugTtkjreD8eXIg7qrplhWan/view" style="color: gr.themes.colors.red; text-decoration: none;">Link To Paper</a> | | |
| <a href="https://github.com/Syudu41/ASTRA---Gates-Project" style="color: #1E90FF; text-decoration: none;">GitHub</a> | | |
| <a href="https://drive.google.com/file/d/1wussSAYV5WfRSgYNEDgxWNqMUNoiu65u/view" style="color: #1E90FF; text-decoration: none;">User Guide</a> | |
| </h3> | |
| <p style="color: white;">Welcome to a demo of ASTRA. ASTRA is a collaborative research project between researchers at the | |
| <a href="https://sites.google.com/site/dvngopal/" style="color: #1E90FF; text-decoration: none;">University of Memphis</a> and | |
| <a href="https://www.carnegielearning.com" style="color: #1E90FF; text-decoration: none;">Carnegie Learning</a> | |
| to utilize AI to improve our understanding of math learning strategies.</p> | |
| <p style="color: white;">This demo has been developed with a pre-trained model (based on an architecture similar to BERT ) that learns math strategies using data | |
| collected from hundreds of schools in the U.S. who have used Carnegie Learning’s MATHia (formerly known as Cognitive Tutor), the flagship Intelligent Tutor that is part of a core, blended math curriculum. | |
| For this demo, we have used data from a specific domain (teaching ratio and proportions) within 7th grade math. The fine-tuning based on the pre-trained model learns to predict which strategies lead to correct vs incorrect solutions. | |
| </p> | |
| <p style="color: white;">In this math domain, students were given word problems related to ratio and proportions. Further, the students | |
| were given a choice of optional tasks to work on in parallel to the main problem to demonstrate their thinking (metacognition). | |
| The optional tasks are designed based on solving problems using Equivalent Ratios (ER) and solving using Means and Extremes/cross-multiplication (ME). | |
| When the equivalent ratios are easy to compute (integral values), ER is much more efficient compared to ME and switching between the tasks appropriately demonstrates cognitive flexibility. | |
| </p> | |
| <p style="color: white;">To use the demo, please follow these steps:</p> | |
| <ol style="color: white;"> | |
| <li style="color: white;">Select a fine-tuned model: | |
| <ul style="color: white;"> | |
| <li style="color: white;">ASTRA-FT-HGR: Fine-tuned with 10 % data from schools that have a high graduation rate.</li> | |
| <li style="color: white;">ASTRA-FT-Skills: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills encoded using Bayesian Knowledge Tracing</li> | |
| <li style="color: white;">ASTRA-FT-Temporal: Fine-tuned with 10% of initial problems from both HGR and LGR schools + temporal features measuring student engagement with MATHia</li> | |
| <li style="color: white;">ASTRA-FT-Unified: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills + temporal features</li> | |
| </ul> | |
| </li> | |
| <li style="color: white;">Select a percentage of schools to analyze (selecting a large percentage may take a long time). Note that the selected percentage is applied to both High Graduation Rate (HGR) schools and Low Graduation Rate (LGR schools). | |
| </li> | |
| <li style="color: white;">The results from the fine-tuned model are displayed in the dashboard: | |
| <ul> | |
| <li style="color: white;">The model accuracy is computed using the ROC-AUC metric. | |
| </li> | |
| <li style="color: white;">The results are shown for HGR, LGR schools and for different problem types (ER/ME). | |
| </li> | |
| <li style="color: white;">The distribution over how students utilized the optional tasks (whether they utilized ER/ME, used both of them or none of them) is shown for each problem type. | |
| </li> | |
| </ul> | |
| </li> | |
| <li style="color: white;">Generate strategies from the AI model and visualize the strategies .csv files using the path analysis web-app. | |
| Refer to the <a href="https://drive.google.com/file/d/1wussSAYV5WfRSgYNEDgxWNqMUNoiu65u/view" style="color: #1E90FF; text-decoration: none;">user guide </a> for more details on how to interpret the strategy visualization. | |
| </ol> | |
| """ | |
| # CSS styling for white text | |
| # Create the Gradio interface | |
| available_themes = { | |
| "default": gr.themes.Default(), | |
| "soft": gr.themes.Soft(), | |
| "monochrome": gr.themes.Monochrome(), | |
| "glass": gr.themes.Glass(), | |
| "base": gr.themes.Base(), | |
| } | |
| # Comprehensive CSS for all HTML elements | |
| custom_css = ''' | |
| /* Import Fira Sans font */ | |
| @import url('https://fonts.googleapis.com/css2?family=Fira+Sans:wght@400;500;600;700&family=Inter:wght@400;500;600;700&display=swap'); | |
| @import url('https://fonts.googleapis.com/css2?family=Libre+Caslon+Text:ital,wght@0,400;0,700;1,400&family=Spectral+SC:wght@600&display=swap'); | |
| /* Container modifications for centering */ | |
| .gradio-container { | |
| color: var(--block-label-text-color) !important; | |
| max-width: 1000px !important; | |
| margin: 0 auto !important; | |
| padding: 2rem !important; | |
| font-family: Arial, sans-serif !important; | |
| } | |
| /* Main title (ASTRA) */ | |
| #title { | |
| text-align: center !important; | |
| margin: 1rem auto !important; /* Reduced margin */ | |
| font-size: 2.5em !important; | |
| font-weight: 600 !important; | |
| font-family: "Spectral SC", 'Fira Sans', sans-serif !important; | |
| padding-bottom: 0 !important; /* Remove bottom padding */ | |
| } | |
| /* Subtitle (An AI Model...) */ | |
| h1 { | |
| text-align: center !important; | |
| font-size: 30pt !important; | |
| font-weight: 600 !important; | |
| font-family: "Spectral SC", 'Fira Sans', sans-serif !important; | |
| margin-top: 0.5em !important; /* Reduced top margin */ | |
| margin-bottom: 0.3em !important; | |
| } | |
| h2 { | |
| text-align: center !important; | |
| font-size: 22pt !important; | |
| font-weight: 600 !important; | |
| font-family: "Spectral SC",'Fira Sans', sans-serif !important; | |
| margin-top: 0.2em !important; /* Reduced top margin */ | |
| margin-bottom: 0.3em !important; | |
| } | |
| /* Links container styling */ | |
| .links-container { | |
| text-align: center !important; | |
| margin: 1em auto !important; | |
| font-family: 'Inter' ,'Fira Sans', sans-serif !important; | |
| } | |
| /* Links */ | |
| a { | |
| color: #2563eb !important; | |
| text-decoration: none !important; | |
| font-family:'Inter' , 'Fira Sans', sans-serif !important; | |
| } | |
| a:hover { | |
| text-decoration: underline !important; | |
| opacity: 0.8; | |
| } | |
| /* Regular text */ | |
| p, li, .description, .markdown-text { | |
| font-family: 'Inter', Arial, sans-serif !important; | |
| color: black !important; | |
| font-size: 11pt; | |
| line-height: 1.6; | |
| font-weight: 500 !important; | |
| color: var(--block-label-text-color) !important; | |
| } | |
| /* Other headings */ | |
| h3, h4, h5 { | |
| font-family: 'Fira Sans', sans-serif !important; | |
| color: var(--block-label-text-color) !important; | |
| margin-top: 1.5em; | |
| margin-bottom: 0.75em; | |
| } | |
| h3 { font-size: 1.5em; font-weight: 600; } | |
| h4 { font-size: 1.25em; font-weight: 500; } | |
| h5 { font-size: 1.1em; font-weight: 500; } | |
| /* Form elements */ | |
| .select-wrap select, .wrap select, | |
| input, textarea { | |
| font-family: 'Inter' ,Arial, sans-serif !important; | |
| color: var(--block-label-text-color) !important; | |
| } | |
| /* Lists */ | |
| ul, ol { | |
| margin-left: 0 !important; | |
| margin-bottom: 1.25em; | |
| padding-left: 2em; | |
| } | |
| li { | |
| margin-bottom: 0.75em; | |
| } | |
| /* Form container */ | |
| .form-container { | |
| max-width: 1000px !important; | |
| margin: 0 auto !important; | |
| padding: 1rem !important; | |
| } | |
| /* Dashboard */ | |
| .dashboard { | |
| margin-top: 2rem !important; | |
| padding: 1rem !important; | |
| border-radius: 8px !important; | |
| } | |
| /* Slider styling */ | |
| .gradio-slider-row { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| margin: 1.5em 0; | |
| max-width: 100% !important; | |
| } | |
| .gradio-slider { | |
| flex-grow: 1; | |
| margin-right: 15px; | |
| } | |
| .slider-percentage { | |
| font-family: 'Inter', Arial, sans-serif !important; | |
| flex-shrink: 0; | |
| min-width: 60px; | |
| font-size: 1em; | |
| font-weight: bold; | |
| text-align: center; | |
| background-color: #f0f8ff; | |
| border: 1px solid #004080; | |
| border-radius: 5px; | |
| padding: 5px 10px; | |
| } | |
| .progress-bar-wrap.progress-bar-wrap.progress-bar-wrap | |
| { | |
| border-radius: var(--input-radius); | |
| height: 1.25rem; | |
| margin-top: 1rem; | |
| overflow: hidden; | |
| width: 70%; | |
| font-family: 'Inter', Arial, sans-serif !important; | |
| } | |
| /* Add these new styles after your existing CSS */ | |
| /* Card-like appearance for the dashboard */ | |
| .dashboard { | |
| background: #ffffff !important; | |
| box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06) !important; | |
| border-radius: 12px !important; | |
| padding: 2rem !important; | |
| margin-top: 2.5rem !important; | |
| } | |
| /* Enhance ROC graph container */ | |
| #roc { | |
| background: #ffffff !important; | |
| padding: 1.5rem !important; | |
| border-radius: 8px !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05) !important; | |
| margin: 1.5rem 0 !important; | |
| } | |
| /* Style the dropdown select */ | |
| select { | |
| background-color: #ffffff !important; | |
| border: 1px solid #e2e8f0 !important; | |
| border-radius: 8px !important; | |
| padding: 0.5rem 1rem !important; | |
| transition: all 0.2s ease-in-out !important; | |
| box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| select:hover { | |
| border-color: #cbd5e1 !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important; | |
| } | |
| /* Enhance slider appearance */ | |
| .progress-bar-wrap { | |
| background: #f8fafc !important; | |
| border: 1px solid #e2e8f0 !important; | |
| box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| /* Style metrics in dashboard */ | |
| .dashboard p { | |
| padding: 0.5rem 0 !important; | |
| border-bottom: 1px solid #f1f5f9 !important; | |
| } | |
| /* Add spacing between sections */ | |
| .dashboard > div { | |
| margin-bottom: 1.5rem !important; | |
| } | |
| /* Style the ROC curve title */ | |
| .dashboard h4 { | |
| color: #1e293b !important; | |
| font-weight: 600 !important; | |
| margin-bottom: 1rem !important; | |
| padding-bottom: 0.5rem !important; | |
| border-bottom: 2px solid #e2e8f0 !important; | |
| } | |
| /* Enhance link appearances */ | |
| a { | |
| position: relative !important; | |
| padding-bottom: 2px !important; | |
| transition: all 0.2s ease-in-out !important; | |
| } | |
| a:after { | |
| content: '' !important; | |
| position: absolute !important; | |
| width: 0 !important; | |
| height: 1px !important; | |
| bottom: 0 !important; | |
| left: 0 !important; | |
| background-color: #2563eb !important; | |
| transition: width 0.3s ease-in-out !important; | |
| } | |
| a:hover:after { | |
| width: 100% !important; | |
| } | |
| /* Add subtle dividers between sections */ | |
| .form-container > div { | |
| padding-bottom: 1.5rem !important; | |
| margin-bottom: 1.5rem !important; | |
| border-bottom: 1px solid #f1f5f9 !important; | |
| } | |
| /* Style model selection section */ | |
| .select-wrap { | |
| background: #ffffff !important; | |
| padding: 1.5rem !important; | |
| border-radius: 8px !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05) !important; | |
| margin-bottom: 2rem !important; | |
| } | |
| /* Style the metrics display */ | |
| .dashboard span { | |
| font-family: 'Inter', sans-serif !important; | |
| font-weight: 500 !important; | |
| color: #334155 !important; | |
| } | |
| /* Add subtle animation to interactive elements */ | |
| button, select, .slider-percentage { | |
| transition: all 0.2s ease-in-out !important; | |
| } | |
| /* Style the ROC curve container */ | |
| .plot-container { | |
| background: #ffffff !important; | |
| border-radius: 8px !important; | |
| padding: 1rem !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| /* Add container styles for opt1 and opt2 sections */ | |
| #opt1, #opt2 { | |
| background: #ffffff !important; | |
| border-radius: 8px !important; | |
| padding: 1.5rem !important; | |
| margin-top: 1.5rem !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| /* Style the distribution titles */ | |
| .distribution-title { | |
| font-family: 'Inter', sans-serif !important; | |
| font-weight: 600 !important; | |
| color: #1e293b !important; | |
| margin-bottom: 1rem !important; | |
| text-align: center !important; | |
| } | |
| ''' | |
| # Define the file directory | |
| FILE_DIR = "fileHandler" | |
| # Function to get list of files | |
| def list_files(): | |
| return ['Unsuccessful Strategies (ER)', 'Successful Strategies (ER)', 'Unsuccessful Strategies (ME)', 'Successful Strategies (ME)'] | |
| label_to_filename = { | |
| 'Unsuccessful Strategies (ER)': 'ER-unsuccessful-strategies.csv', | |
| 'Successful Strategies (ER)': 'ER-successful-strategies.csv', | |
| 'Unsuccessful Strategies (ME)': 'ME-unsuccessful-strategies.csv', | |
| 'Successful Strategies (ME)': 'ME-successful-strategies.csv' | |
| } | |
| # Function to provide the selected file path | |
| def provide_file_path(label_name): | |
| file_name = label_to_filename.get(label_name, None) | |
| if file_name: | |
| file_path = f"{FILE_DIR}/{file_name}" | |
| dynamic_text = "🔍 [Visualize the strategies](https://path-analysis.vercel.app/)" | |
| return file_path, gr.update(value=dynamic_text, visible=True) | |
| return None, gr.update(visible=False) | |
| with gr.Blocks(theme='gstaff/sketch', css=custom_css) as demo: | |
| gr.Markdown(content) | |
| with gr.Row(): | |
| model_dropdown = gr.Dropdown( | |
| choices=models, | |
| label="Select Fine-tuned Model", | |
| elem_classes="dropdown-menu" | |
| ) | |
| increment_slider = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| label="Schools Percentage", | |
| value=1, | |
| elem_id="increment-slider", | |
| elem_classes="gradio-slider" | |
| ) | |
| with gr.Row(): | |
| btn = gr.Button("Submit") | |
| gr.Markdown("<p class='description'>Dashboard</p>") | |
| with gr.Row(): | |
| output_text = gr.Textbox(label="") | |
| # output_image = gr.Image(label="ROC") | |
| with gr.Row(): | |
| plot_output = gr.Plot(label="ROC") | |
| with gr.Row(): | |
| opt1_pie = gr.Plot(label="ER") | |
| opt2_pie = gr.Plot(label="ME") | |
| with gr.Row(): | |
| gr.Markdown("Select a strategy from the dropdown") | |
| with gr.Row(): | |
| file_dropdown = gr.Dropdown(choices=list_files(),label=" ") | |
| download_button = gr.Button("Generate Strategies") | |
| with gr.Row(): | |
| file_output = gr.File(label="Download File") | |
| visualize_markdown = gr.Markdown(visible=False) | |
| download_button.click( | |
| fn=provide_file_path, | |
| inputs=[file_dropdown], | |
| outputs=[file_output, visualize_markdown] | |
| ) | |
| btn.click( | |
| fn=lambda model, increment: ( | |
| *process_file(model, increment), # Unpack all outputs from process_file | |
| gr.update(value=None), # Reset dropdown to first item | |
| None, # Clear file output | |
| gr.update(visible=False) # Hide visualize markdown | |
| ), | |
| inputs=[model_dropdown, increment_slider], | |
| outputs=[output_text, plot_output, opt1_pie, opt2_pie, file_dropdown, file_output, visualize_markdown] | |
| ) | |
| # Launch the app | |
| demo.launch() |