fixed calc issue

app.py

@@ -1,19 +1,24 @@
 import numpy as np
 import gradio as gr
 import pickle
-
 import plotly.graph_objects as go
 import plotly.express as px
-
 import pandas as pd
 
-# Remove the global variable and instead use gr.State
-# define a Dataframe styler to highlight the Feasible? column to be green and red
+# Define styling for success/failure highlighting
 def highlight_success(val):
     color = 'lightgreen' if val == 'Success' else 'lightcoral'
     return f'color:white;background-color: {color}'
 
+# Simulation function for electrospraying
 def sim_espray_constrained(x, noise_se=None):
+    # Ensure x is a numpy array with float data type
+    x = np.array(x, dtype=float)
+    
+    # Ensure x is a 2D array
+    if x.ndim == 1:
+        x = x.reshape(1, -1)
+        
     # Define the equations
     conc = x[:, 0]
     flow_rate = x[:, 1]
@@ -25,6 +30,7 @@ def sim_espray_constrained(x, noise_se=None):
     exp_con = (np.log(flow_rate) * (solvent - 0.5) + 1.40 >= 0).astype(float)
     return np.column_stack((diameter, exp_con))
 
+# Initialize experiment data
 X_init = np.array([[0.5, 15, 10, 0],
                    [0.5, 0.1, 10, 1],
                    [3, 20, 15, 0],
@@ -34,12 +40,11 @@ X_init = np.array([[0.5, 15, 10, 0],
 Y_init = sim_espray_constrained(X_init)
 exp_record_df = pd.DataFrame(X_init, columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent'])
 exp_record_df['Size (um)'] = Y_init[:, 0]
-# map 1 to CHCl3 and 0 to DMAc
 exp_record_df['Solvent'] = ['DMAc' if x == 0 else 'CHCl3' for x in exp_record_df['Solvent']]
 exp_record_df['Feasible?'] = ['Success' if x == 1 else 'Failed' for x in Y_init[:, 1]]
+prior_experiments_display = exp_record_df.style.map(highlight_success, subset=['Feasible?']).format(precision=3)
 
-gr_exp_record_df = gr.DataFrame(value=exp_record_df.style.map(highlight_success, subset=['Feasible?']).format(precision=3), label="Prior Experiments")
-
+# Functions for data processing and visualization
 def import_results():
     strategies = ['qEI', 'qEI_vi_mixed_con', 'qEICF_vi_mixed_con', 'rnd']
     
@@ -58,13 +63,21 @@ def import_results():
     return best_distances_df_long
 
 def calc_human_performance(df):
+    # Make a copy of the dataframe to avoid modifying the original
+    df_copy = df.copy()
+    
     # convert back solvent to 0 and 1
-    df['Solvent'] = [0 if x == 'DMAc' else 1 for x in df['Solvent']]
+    df_copy['Solvent'] = [0 if x == 'DMAc' else 1 for x in df_copy['Solvent']]
     
     TARGET_SIZE = 3.0  # Example target size
-    ROUNDS = len(df) // 2
+    ROUNDS = len(df_copy) // 2
 
-    X_human = df[['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent']].values
+    # Ensure all values are numeric
+    numeric_cols = ['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent']
+    for col in numeric_cols:
+        df_copy[col] = pd.to_numeric(df_copy[col])
+    
+    X_human = df_copy[numeric_cols].values
 
     X_human_init = X_init.copy()
     Y_human_init = Y_init.copy()
@@ -74,9 +87,15 @@ def calc_human_performance(df):
     for iter in range(ROUNDS + 1):
         Y_distance = -np.abs(Y_human_init[:, 0] - TARGET_SIZE)
         best_human_distance.append(np.ma.masked_array(Y_distance, mask=~Y_human_init[:, 1].astype(bool)).max())
-        new_x = X_human[2 * iter:2 * (iter + 1)]
-        X_human_init = np.vstack([X_human_init, new_x])
-        Y_human_init = np.vstack([Y_human_init, sim_espray_constrained(new_x)])
+        
+        # Check if we have more data for this iteration
+        if 2 * iter < len(X_human):
+            # Get the slice of new experiments
+            new_x = X_human[2 * iter:min(2 * (iter + 1), len(X_human))]
+            
+            # Add the new experiments to our dataset
+            X_human_init = np.vstack([X_human_init, new_x])
+            Y_human_init = np.vstack([Y_human_init, sim_espray_constrained(new_x)])
 
     return -np.array(best_human_distance)
 
@@ -147,8 +166,8 @@ def plot_results(exp_data_df):
     
     return fig
 
-# Update predict function to use state
-def predict(state, text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, flow_rate2, voltage2, solvent2):
+# Prediction function - simplified signature by removing unnecessary text params
+def predict(state, conc1, flow_rate1, voltage1, solvent1, conc2, flow_rate2, voltage2, solvent2):
     # Get current results storage from state or initialize if None
     if state is None:
         results_storage = pd.DataFrame(columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
@@ -158,26 +177,17 @@ def predict(state, text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, f
     solvent_value1 = 0 if solvent1 == 'DMAc' else 1
     solvent_value2 = 0 if solvent2 == 'DMAc' else 1
     
-    # Convert inputs to numpy array
+    # Process inputs and get predictions
     inputs1 = np.array([[conc1, flow_rate1, voltage1, solvent_value1]])
     inputs2 = np.array([[conc2, flow_rate2, voltage2, solvent_value2]])
-
-    # Get predictions
     results1 = sim_espray_constrained(inputs1)
     results2 = sim_espray_constrained(inputs2)
     
-    # Format output
-    diameter1 = results1[0, 0]
-    exp_con1 = results1[0, 1]
-    
-    diameter2 = results2[0, 0]
-    exp_con2 = results2[0, 1]
-
-    # create a dataframe to store the results
-    results_df = pd.DataFrame(np.array([
-        [conc1, flow_rate1, voltage1, solvent_value1, diameter1, exp_con1],
-        [conc2, flow_rate2, voltage2, solvent_value2, diameter2, exp_con2]
-    ]), columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
+    # Format and store results
+    results_df = pd.DataFrame([
+        [conc1, flow_rate1, voltage1, solvent_value1, results1[0, 0], results1[0, 1]],
+        [conc2, flow_rate2, voltage2, solvent_value2, results2[0, 0], results2[0, 1]]
+    ], columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
 
     results_df['Solvent'] = ['DMAc' if x == 0 else 'CHCl3' for x in results_df['Solvent']]
     results_df['Feasible?'] = ['Success' if x == 1 else 'Failed' for x in results_df['Feasible?']]
@@ -185,73 +195,68 @@ def predict(state, text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, f
     results_storage = pd.concat([results_storage, results_df], ignore_index=True)
     results_display = results_storage.style.map(highlight_success, subset=['Feasible?']).format(precision=3)
    
-    return (results_storage, gr_exp_record_df, gr.DataFrame(value=results_display, label="Your Results"), plot_results(results_storage))
+    # Return updated state and UI updates
+    return (
+        results_storage, 
+        gr.DataFrame(value=prior_experiments_display, label="Prior Experiments"),
+        gr.DataFrame(value=results_display, label="Your Results"), 
+        plot_results(results_storage)
+    )
 
-# Update reset_results to use state
+# Reset results function
 def reset_results(state):
     results_storage = pd.DataFrame(columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
-    return (results_storage, gr_exp_record_df, gr.DataFrame(value=results_storage.style.map(highlight_success, subset=['Feasible?']).format(precision=3), label="Your Results"), plot_results(results_storage))
-
-inputs = [
-    gr.Markdown("### Experiment 1"),
-    gr.Number(value=1.2, label="Concentration (%w/v, range: 0.05-5.00)", minimum=0.05, maximum=5.0, precision=3),
-    gr.Number(value=20.0, label="Flow Rate (mL/h, range: 0.01-60.00)", minimum=0.01, maximum=60.0, precision=3),
-    gr.Number(value=15.0, label="Voltage (kV, range: 10.00-18.00)", minimum=10.0, maximum=18.0, precision=3),
-    gr.Dropdown(['DMAc', 'CHCl3'], value='DMAc', label='Solvent'),
-    gr.Markdown("### Experiment 2"),
-    gr.Number(value=2.8, label="Concentration (%w/v, range: 0.05-5.00)", minimum=0.05, maximum=5.0, precision=3),
-    gr.Number(value=20.0, label="Flow Rate (mL/h, range: 0.01-60.00)", minimum=0.01, maximum=60.0, precision=3),
-    gr.Number(value=15.0, label="Voltage (kV, 10.00-18.00)", minimum=10.0, maximum=18.0, precision=3),
-    gr.Dropdown(['DMAc', 'CHCl3'], value='CHCl3', label='Solvent')
-]
-
-outputs = [gr_exp_record_df, gr.DataFrame(label="Your Results"), gr.Plot(label="Performance Comparison")]
+    return (
+        results_storage, 
+        gr.DataFrame(value=prior_experiments_display, label="Prior Experiments"),
+        gr.DataFrame(value=results_storage.style.map(highlight_success, subset=['Feasible?']).format(precision=3), label="Your Results"), 
+        plot_results(results_storage)
+    )
 
-description = "<h3>Welcome, challenger!</h3><p> If you think you may perform better than <strong>CCBO</strong>, try this interactive game to optimize electrospray! Rules are simple: <ul><li>Examine carefully the initial experiments you have on the right (or below if you're using your phone), remember, your target size is <u><i><strong>3.000 um</strong></i></u> ----></li><li>Select your parameters, you have <strong>2</strong> experiments (chances) in each round, use them wisely! </li><li>Click <strong>Submit</strong> to see the results, reflect and improve your selection!</li><li>Repeat the process for <strong>5</strong> rounds to see if you can beat CCBO!</li></ul></p>"
+# Application description
+description = "<h3>Welcome, challenger!</h3><p> If you think you may perform better than <strong>CCBO</strong>, try this interactive game to optimize electrospray! Rules are simple: <ul><li>Examine carefully the initial experiments you have on the right (or below if you're using your phone), remember, your target size is <u><i><strong>3.000 um</strong></i></u> ----></li><li>Select your parameters, you have <strong>2</strong> experiments (chances) in each round, use them wisely! </li><li>Click <strong>Submit</strong> to see the results, reflect and improve your selection!</li><li>Repeat the process for <strong>5</strong> rounds to see if you can beat CCBO!</li></ul></p><p>Your data will not be stored, so feel free to play again, good luck!</p>"
 
-# Create a Blocks interface instead of using Interface
+# Create Gradio interface
 with gr.Blocks() as demo:
-    
-    gr.Markdown("## Human vs CCBO Campaign - Simulated Electrospray")
+    gr.Markdown("## Human vs CCBO Campaign - Optimize Electrospray")
     gr.Markdown(description)
     
     # Add state component to store user-specific results
     results_state = gr.State()
     
     with gr.Row():
+        # Input parameters column
         with gr.Column():
             gr.Markdown("### Experiment 1")
-            conc1 = gr.Number(value=1.2, label="Concentration (%w/v, range: 0.05-5.00)", minimum=0.05, maximum=5.0, precision=3)
-            flow_rate1 = gr.Number(value=20.0, label="Flow Rate (mL/h, range: 0.01-60.00)", minimum=0.01, maximum=60.0, precision=3)
-            voltage1 = gr.Number(value=15.0, label="Voltage (kV, range: 10.00-18.00)", minimum=10.0, maximum=18.0, precision=3)
+            conc1 = gr.Slider(minimum=0.05, maximum=5.0, value=1.2, step=0.001, label="Concentration (%w/v)")
+            flow_rate1 = gr.Slider(minimum=0.01, maximum=60.0, value=20.0, step=0.001, label="Flow Rate (mL/h)")
+            voltage1 = gr.Slider(minimum=10.0, maximum=18.0, value=15.0, step=0.001, label="Voltage (kV)")
             solvent1 = gr.Dropdown(['DMAc', 'CHCl3'], value='DMAc', label='Solvent')
             
             gr.Markdown("### Experiment 2")
-            conc2 = gr.Number(value=2.8, label="Concentration (%w/v, range: 0.05-5.00)", minimum=0.05, maximum=5.0, precision=3)
-            flow_rate2 = gr.Number(value=20.0, label="Flow Rate (mL/h, range: 0.01-60.00)", minimum=0.01, maximum=60.0, precision=3)
-            voltage2 = gr.Number(value=15.0, label="Voltage (kV, 10.00-18.00)", minimum=10.0, maximum=18.0, precision=3)
+            conc2 = gr.Slider(minimum=0.05, maximum=5.0, value=2.8, step=0.001, label="Concentration (%w/v)")
+            flow_rate2 = gr.Slider(minimum=0.01, maximum=60.0, value=20.0, step=0.001, label="Flow Rate (mL/h)")
+            voltage2 = gr.Slider(minimum=10.0, maximum=18.0, value=15.0, step=0.001, label="Voltage (kV)")
             solvent2 = gr.Dropdown(['DMAc', 'CHCl3'], value='CHCl3', label='Solvent')
-            
+        
+        # Results display column
         with gr.Column():
-            prior_experiments = gr.DataFrame(value=exp_record_df.style.map(highlight_success, subset=['Feasible?']).format(precision=3), label="Prior Experiments")
+            prior_experiments = gr.DataFrame(value=prior_experiments_display, label="Prior Experiments")
             results_df = gr.DataFrame(label="Your Results")
             perf_plot = gr.Plot(label="Performance Comparison")
     
+    # Action buttons
     with gr.Row():
         submit_btn = gr.Button("Submit")
         reset_btn = gr.Button("Reset Results")
     
-    # Add invisible text input components to match the predict function signature
-    text1 = gr.Textbox(visible=False)
-    text2 = gr.Textbox(visible=False)
-    
     # Connect the submit button to the predict function
     submit_btn.click(
         fn=predict,
         inputs=[
-            results_state,
-            text1, conc1, flow_rate1, voltage1, solvent1,
-            text2, conc2, flow_rate2, voltage2, solvent2
+            results_state, 
+            conc1, flow_rate1, voltage1, solvent1,
+            conc2, flow_rate2, voltage2, solvent2
         ],
         outputs=[results_state, prior_experiments, results_df, perf_plot]
     )