user specific data

app.py

@@ -7,9 +7,7 @@ import plotly.express as px
 
 import pandas as pd
 
-# Global variable to store results
-results_storage = pd.DataFrame(columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
-
+# Remove the global variable and instead use gr.State
 # define a Dataframe styler to highlight the Feasible? column to be green and red
 def highlight_success(val):
     color = 'lightgreen' if val == 'Success' else 'lightcoral'
@@ -149,8 +147,14 @@ def plot_results(exp_data_df):
     
     return fig
 
-def predict(text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, flow_rate2, voltage2, solvent2):
-    global results_storage
+# Update predict function to use state
+def predict(state, text1, conc1, flow_rate1, voltage1, solvent1, text2, 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?'])
+    else:
+        results_storage = state.copy()
+        
     solvent_value1 = 0 if solvent1 == 'DMAc' else 1
     solvent_value2 = 0 if solvent2 == 'DMAc' else 1
     
@@ -181,12 +185,12 @@ def predict(text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, flow_rat
     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 (gr_exp_record_df, gr.DataFrame(value=results_display, label="Your Results"), plot_results(results_storage))
+    return (results_storage, gr_exp_record_df, gr.DataFrame(value=results_display, label="Your Results"), plot_results(results_storage))
 
-def reset_results():
-    global results_storage
+# Update reset_results to use state
+def reset_results(state):
     results_storage = pd.DataFrame(columns=['Concentration (%w/v)', 'Flow Rate (mL/h)', 'Voltage (kV)', 'Solvent', 'Size (um)', 'Feasible?'])
-    return (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))
+    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"),
@@ -210,6 +214,10 @@ with gr.Blocks() as demo:
     
     gr.Markdown("## Human vs CCBO Campaign - Simulated Electrospray")
     gr.Markdown(description)
+    
+    # Add state component to store user-specific results
+    results_state = gr.State()
+    
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Experiment 1")
@@ -241,17 +249,18 @@ with gr.Blocks() as demo:
     submit_btn.click(
         fn=predict,
         inputs=[
+            results_state,
             text1, conc1, flow_rate1, voltage1, solvent1,
             text2, conc2, flow_rate2, voltage2, solvent2
         ],
-        outputs=[prior_experiments, results_df, perf_plot]
+        outputs=[results_state, prior_experiments, results_df, perf_plot]
     )
     
     # Connect the reset button to the reset_results function
     reset_btn.click(
         fn=reset_results,
-        inputs=[],
-        outputs=[prior_experiments, results_df, perf_plot]
+        inputs=[results_state],
+        outputs=[results_state, prior_experiments, results_df, perf_plot]
     )
 
 demo.launch()