Update app.py

app.py

@@ -23,7 +23,7 @@ def get_POMFinder():
     return y, y_onehotenc_cat, y_onehotenc_values, POMFinder
 
 
-def PDF_Preparation(Your_PDF_Name, Qmin, Qmax, Qdamp, rmax, nyquist, plot=True):
+def PDF_Preparation(Your_PDF_Name, Qmin, Qmax, Qdamp, rmax, nyquist):
     for i in range(1000):
         with open(Your_PDF_Name, "r") as file:
             data = file.read().splitlines(True)
@@ -58,30 +58,31 @@ def PDF_Preparation(Your_PDF_Name, Qmin, Qmax, Qdamp, rmax, nyquist, plot=True):
     # Add experimental parameters to the Gr
     Gr = np.expand_dims(np.concatenate((np.expand_dims(Qmin, axis=0), np.expand_dims(Qmax, axis=0), np.expand_dims(Qdamp, axis=0), Gr), axis=0), axis=0)
 
-    if plot:
-        # Plot the transformation to make sure everything is alright
-        plt.plot(PDF[:,0], PDF[:,1], label="Original Data")
-        plt.plot(r, Gr[0,3:], label="Gr ready for ML")
-        plt.legend()
-        plt.title("Original Data vs. normalised Data")
-        plt.xlabel("r (AA)")
-        plt.ylabel("Gr")
-        plt.show()
+    # Create a new figure object
+    fig, ax = plt.subplots()
     
+    # Plot the transformation to make sure everything is alright
+    ax.plot(PDF[:,0], PDF[:,1], label="Original Data")
+    ax.plot(r, Gr[0,3:], label="Gr ready for ML")
+    ax.legend()
+    ax.set_title("Original Data vs. normalised Data")
+    ax.set_xlabel("r (AA)")
+    ax.set_ylabel("Gr")
+    
+    st.pyplot(fig)
+        
     return r, Gr
 
 def POMPredicter(POMFinder, Gr, y_onehotenc_cat):
     y_pred_proba = POMFinder.predict_proba(Gr);
     y_pred_proba = y_pred_proba[:,1];
-    #print (np.shape(y_pred_proba))
-    #y_pred_proba = y_pred_proba[0];
     res = sorted(range(len(y_pred_proba)), key = lambda sub: y_pred_proba[sub]);
     res.reverse();
-    print ("The 1st guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[0]])[2:-2]+"cale.xyz")
-    print ("The 2nd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[1]])[2:-2]+"cale.xyz")
-    print ("The 3rd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[2]])[2:-2]+"cale.xyz")
-    print ("The 4th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[3]])[2:-2]+"cale.xyz")
-    print ("The 5th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[4]])[2:-2]+"cale.xyz")
+    st.write("The 1st guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[0]])[2:-2]+"cale.xyz")
+    st.write("The 2nd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[1]])[2:-2]+"cale.xyz")
+    st.write("The 3rd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[2]])[2:-2]+"cale.xyz")
+    st.write("The 4th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[3]])[2:-2]+"cale.xyz")
+    st.write("The 5th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[4]])[2:-2]+"cale.xyz")
     
     return res, y_pred_proba
 
@@ -120,8 +121,11 @@ else:
 
     #Predict with POMFinder
     y, y_onehotenc_cat, y_onehotenc_values, POMFinder = get_POMFinder()
+    st.write("POMfinder loaded")
     r, Gr = PDF_Preparation(args.data, args.Qmin, args.Qmax, args.Qdamp, rmax=10, nyquist=args.nyquist)
+    st.write("Data loaded")
     res, y_pred_proba = POMPredicter(POMFinder, Gr, y_onehotenc_cat);
+    st.write("Predictions is: ", res)
 
 
 st.subheader('Cite')