changed to gr_blocks dev

app.py

@@ -183,6 +183,11 @@ def predict(text1, conc1, flow_rate1, voltage1, solvent1, text2, conc2, flow_rat
    
     return (gr_exp_record_df, gr.DataFrame(value=results_display, label="Your Results"), plot_results(results_storage))
 
+def reset_results():
+    global results_storage
+    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))
+
 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),
@@ -200,12 +205,53 @@ outputs = [gr_exp_record_df, gr.DataFrame(label="Your Results"), gr.Plot(label="
 
 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>"
 
-# Update interface
-demo = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    title="Human vs CCBO Campaign - Simulated Electrospray",
-    description=description
-)
+# Create a Blocks interface instead of using Interface
+with gr.Blocks() as demo:
+    
+    gr.Markdown("## Human vs CCBO Campaign - Simulated Electrospray")
+    gr.Markdown(description)
+    with gr.Row():
+        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)
+            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)
+            solvent2 = gr.Dropdown(['DMAc', 'CHCl3'], value='CHCl3', label='Solvent')
+            
+        with gr.Column():
+            prior_experiments = gr.DataFrame(value=exp_record_df.style.map(highlight_success, subset=['Feasible?']).format(precision=3), label="Prior Experiments")
+            results_df = gr.DataFrame(label="Your Results")
+            perf_plot = gr.Plot(label="Performance Comparison")
+    
+    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=[
+            text1, conc1, flow_rate1, voltage1, solvent1,
+            text2, conc2, flow_rate2, voltage2, solvent2
+        ],
+        outputs=[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]
+    )
+
 demo.launch()