Update app.py

app.py

@@ -36,34 +36,19 @@ clf.fit(x_train, y_train)
 
 
 import gradio as gr
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 def OPS_LWAC(OPC, SCM_FA, SCM_SF, w_b, FA, CA, OPS, SP, T):
     # Turning the arguments into a numpy array
     x = np.array([OPC, SCM_FA, SCM_SF, w_b, FA, CA, OPS, SP, T])
     prediction = clf.pred_dist(x.reshape(1, -1))
     aa=prediction.params["loc"].item()
+    #value1 = aa[0]
     bb=prediction.params["scale"].item()
-    
-    # Plotting probability density curve
-    x_values = np.linspace(aa - 3*bb, aa + 3*bb, 1000)
-    y_values = norm.pdf(x_values, aa, bb)
-    plt.plot(x_values, y_values, label='Probability Density Curve')
-    plt.xlabel('Strength (MPa)')
-    plt.ylabel('Probability Density')
-    plt.title('Probability Density Curve for Compressive Strength')
-    plt.grid(True)
-    plt.legend()
-    plt.savefig('probability_density_curve.png')
-
-    result_string = "[CS: {:.2f}; σ: {:.2f}]".format(aa, bb)
-    return result_string, "probability_density_curve.png"
+    #value2 = bb[0]
+    #prediction = clf.predict(x.reshape(1, -1))
+    #-3*y_dists.params["scale"]+y_dists.params["loc"]
+    result_string = "[CS: {:.3f}; σ: {:.3f}]".format(aa, bb)
+    return result_string
+    #return [aa, bb]
 
 inputs = [
     gr.inputs.Number(label="cement content (OPC, unit: kg/m3)"),
@@ -76,10 +61,7 @@ inputs = [
     gr.inputs.Number(label="superplasticizer content (SP, unit: kg/m3)"),
     gr.inputs.Number(label="curing time (T, unit: d)")
 ]
-outputs = [
-    gr.outputs.Textbox(label="Estimated mean prediction and σ value (unit: MPa)"),
-    gr.outputs.Image(label="Probability Density Curve")
-]
+outputs = gr.outputs.Textbox(label="Estimated mean prediction and σ value (unit: MPa)")
 
 app = gr.Interface(fn=OPS_LWAC, inputs=inputs, outputs=outputs, description="Estimation of compressive strength of OPS-based lightweight concrete")
 app.launch()