Update app.py

app.py

@@ -24,16 +24,14 @@ unique_values = joblib.load('unique_values _star.joblib')
 unique_CL =  unique_values["Color"]
 unique_SC =  unique_values["Spectral_Class"]
 
-st.title("My star type")
-st.image("https://study.com/academy/lesson/star-formation-main-sequence-dwarf-giant-stars.html")
-
 def main():
-    st.text('For comparing all models of ML It can be used for prediction')
+    st.title("My star type")
+    st.image("https://img.freepik.com/free-vector/different-types-stars-dark-space_1308-37762.jpg")
     with st.form("questionaire"):
-         K = st.slider('Temperature',min_value=1900,max_value=40000)
+         K = st.slider('Temperature',min_value=1500,max_value=40000)
          L = st.slider('Relative Luminosity',min_value=0,max_value=1000000)
          R = st.slider('Relative Radius',min_value=0,max_value=2000)
-         A_M = st.slider('Absolute Magnitude',min_value=0,max_value=25)
+         AM = st.slider('Absolute Magnitude',min_value=0,max_value=25)
          Color = st.selectbox("General Color of Spectrum",options=unique_CL)
          Spectral_Class = st.selectbox("Spectral_Class",options=unique_SC)
          clicked = st.form_submit_button("Predict type")
@@ -41,39 +39,25 @@ def main():
             result = model.predict(pd.DataFrame({"Temperature(K)": [K],
                                                  "Relative Luminosity(Watts)": [L],
                                                  "Relative Radius(m)": [R],
-                                                 "Absolute Magnitude": [A_M],
-                                                 "Color": [Color],
-                                                 "Spectral_Class": [Spectral_Class]})
-            )
-
-            prob = model.predict_prob(pd.DataFrame({"Temperature(K)": [K],
-                                                 "Relative Luminosity(Watts)": [L],
-                                                 "Relative Radius(m)": [R],
-                                                 "Absolute Magnitude": [A_M],
-                                                 "Color": [Color],
-                                                 "Spectral_Class": [Spectral_Class]})
+                                                 "Absolute Magnitude": [AM],
+                                                 "Color": [CL_DICT[Color]],
+                                                 "Spectral_Class": [SC_DICT[Spectral_Class]]})
             )
             
-            prob = pd.DataFrame(prob, columns = ["Red Dwarf",'Brown Dwarf','White Dwarf','Main Sequence','Super Giants','Hyper Giants']).reset_index(drop=True)
-            prob['Type'] = 'Probability'
-            prob = prob.set_index('Type')
-                           
-            st.write(prob)           
-            
-            if result[0] == '0':
-                st.text('Red Dwarf')
-            elif result[0] == '1':
-                st.text('Brown Dwarf')
-            elif result[0] == '2':
-                st.text('White Dwarf')
-            elif result[0] == '3':
-                st.text('Main Sequence')
-            elif result[0] == '4':
-                st.text('Super Giants')
+            if result[0] == 0:
+                result = 'Red Dwarf'
+            elif result[0] == 1:
+                result ='Brown Dwarf'
+            elif result[0] == 2:
+                result ='White Dwarf'
+            elif result[0] == 3:
+                result ='Main Sequence'
+            elif result[0] == 4:
+                result ='Super Giants'
             else:
-                st.text('Hyper Giants')
+                result ='Hyper Giants'
                 
-            st.write("My star type is...", result[0])
+            st.write("My star type is...", result)
         
 if __name__=="__main__":
     main()