Upload 2 files

app.py

@@ -35,16 +35,25 @@ with col1:
     long = st.number_input("Conference Duration, Days", min_value=0, value=3)
     
     # Dropdown menus
-    event_type = st.selectbox("Event Type", ["Conference", "Workshop", "Other"])
-    cntry = st.selectbox("Conference Location Country", ["USA", "China", "India", "Other"])
-    loc_state = st.selectbox("Conference Location State", ["NJ", "CA", "Other"])
+    event_type = st.selectbox("Event Type", ["Conference", "Workshop", "Forum", "Other"])
+    cntry = st.selectbox("Conference Location Country", ["USA", "China", "India", "Romania", "Other"])
+    if cntry =="USA":
+        loc_state = st.selectbox("Conference Location State", [ "CA", "NJ", "NY", "FL", "Other"], index=4)
+    else:
+        loc_state = "Other"
+
     # Checkboxes
-    st.text("Keywords")
+    st.markdown("<h4>Keywords</h4>", unsafe_allow_html=True)
     kw_comp = st.checkbox("Computing")
-    kw_edu = st.checkbox("Education")
+    kw_sys = st.checkbox("Systems")
     kw_app = st.checkbox("Applications")
     kw_computational = st.checkbox("Computational")
-    kw_sys = st.checkbox("Systems")
+    kw_wless = st.checkbox("Wireless")
+    kw_mdl = st.checkbox("Modeling")
+    kw_ntwk = st.checkbox("Networking")
+    kw_des = st.checkbox("Design")
+    kw_adv = st.checkbox("Advanced")
+    kw_dist = st.checkbox("Distributed")
     
 
 submit = st.button("Submit")
@@ -52,66 +61,85 @@ submit = st.button("Submit")
 # If the submit button is clicked, show output in the second column (col2)
 with col2:
     if submit:
-        try:
-            p = True
-            regressor = joblib.load("budget_prediction_model.joblib")
-        except:
-            p = False
-          
-        if not p:
-            st.write('Check the model path')
+        if cntry != "USA" and loc_state != "Other":
+            st.error("Correct Event Location: Country and State!")
         else:
-            atnd_num = atnd_num / 1.1
-            ppr_num = ppr_num / 1.08
-            
-            
-            data = pd.DataFrame([{'act_atnd_tot_atnd_num': atnd_num,
-                                    'long_atnd_ratio':long/atnd_num, 
-                                    'act_paprs_num': ppr_num, 
-                                    'longevity': long,
-                                    'papr_atnd_ratio':ppr_num/atnd_num, 
-                                    'exh_num': exh_num,
-                                    'exh_atnd_ratio': exh_num/atnd_num,
-                                    'conf_loc_cntry_nm_USA': int(cntry == "USA"),
-                                    'computing': int(kw_comp),
-                                    'conf_evnt_typ_nm_Workshop': int(event_type == "Workshop"),
-                                    'conf_loc_state_nm_CA': int(loc_state == "CA"),
-                                    'education': int(kw_edu), 
-                                    'applications': int(kw_app),
-                                    'conf_loc_state_nm_NJ': int(loc_state == "NJ"),
-                                    'computational': int(kw_computational),
-                                    'systems': int(kw_sys),
-                                    'conf_loc_cntry_nm_China': int(cntry == "China"),
-                                    'conf_loc_cntry_nm_India': int(cntry == "India")
-            }])
-            
-            lambdas = pd.read_csv('lambdas_yeojohnson.csv', header=None, index_col=0)
-            lambdas = lambdas.to_dict()[1]
-            
-            for n in ['act_atnd_tot_atnd_num', 'exh_num', 'act_paprs_num']:
-                data[n] = yeojohnson(data[n], lambdas[n])
-            data.longevity -= 1
-    
-            # Predict income
-            income = regressor.predict(data)[0]
-            income = inv_transform.inv_yeojohnson(income, lambdas['fin_inc_tot_amt'])
-            
-            reg_fees_inc = income * 0.74
-            exh_inc = min(exh_num * 3000, income - reg_fees_inc)
-            
-            expenses = income * 0.833
-            local_arr_exp = expenses * 0.331
-            socl_funcs_exp = expenses * 0.383
-            admintn_exp = expenses * 0.091
             
-            # Display results
-            st.markdown("### Predicted Budget")
-            st.markdown("<h4>Income Structure</h4>", unsafe_allow_html=True)
-            st.write(f"**Total Income: ${round(round(income, -3))}**")
-            st.write(f"Registration Fees Income: ${round(round(reg_fees_inc, -3))}")
-            st.write(f"Exhibit Income: ${round(round(exh_inc, -3))}")
-            st.markdown("<h4>Expenses Structure</h4>", unsafe_allow_html=True)
-            st.write(f"**Total Expenses: ${round(round(expenses, -3))}**")
-            st.write(f"Local Arrangement Amount: ${round(round(local_arr_exp, -3))}")
-            st.write(f"Social Functions Amount: ${round(round(socl_funcs_exp, -3))}")
-            st.write(f"Administration Amount: ${round(round(admintn_exp, -3))}")
+            try:
+                p = True
+                regressor = joblib.load("budget_prediction_model.joblib")
+            except:
+                p = False
+              
+            if not p:
+                st.write('Check the model path')
+                st.error("Model doesn't Exist!")
+            else:
+                atnd_num = atnd_num / 1.1
+                ppr_num = ppr_num / 1.08
+                
+                data = pd.DataFrame([{'act_atnd_tot_atnd_num': atnd_num,
+                                        'long_atnd_ratio':long/atnd_num, 
+                                        'act_paprs_num': ppr_num, 
+                                        'longevity': long,
+                                        'papr_atnd_ratio':ppr_num/atnd_num, 
+                                        'exh_num': exh_num,
+                                        'exh_atnd_ratio': exh_num/atnd_num,
+                                        'computing': int(kw_comp),
+                                        'conf_loc_cntry_nm_USA': int(cntry == "USA"),
+                                        'conf_loc_cntry_nm_India': int(cntry == "India"),
+                                        'conf_evnt_typ_nm_Workshop': int(event_type == "Workshop"),
+                                        'conf_loc_state_nm_CA': int(loc_state == "CA"),
+                                        'conf_loc_state_nm_NJ': int(loc_state == "NJ"),
+                                        'systems': int(kw_sys),
+                                        'applications': int(kw_app),
+                                        'computational': int(kw_computational),
+                                        'conf_loc_cntry_nm_China': int(cntry == "China"),
+                                        'wireless': int(kw_wless),
+                                        'conf_loc_cntry_nm_Romania': int(cntry == "Romania"),
+                                        'conf_loc_state_nm_FL': int(loc_state == "FL"),
+                                        'modeling': int(kw_mdl), 
+                                        'networking': int(kw_ntwk), 
+                                        'design': int(kw_des),
+                                        'conf_loc_state_nm_NY': int(loc_state == "NY"),
+                                        'conf_evnt_typ_nm_Forum': int(event_type == "Forum"),
+                                        'advanced': int(kw_adv),
+                                        'distributed': int(kw_dist)
+                                    
+                }])
+                
+                
+                lambdas = pd.read_csv('lambdas_yeojohnson.csv', header=None, index_col=0)
+                lambdas = lambdas.to_dict()[1]
+                
+                for n in ['act_atnd_tot_atnd_num', 'exh_num', 'act_paprs_num']:
+                    data[n] = yeojohnson(data[n], lambdas[n])
+                data.longevity -= 1
+        
+                # Predict income
+                income = regressor.predict(data)[0]
+                income = inv_transform.inv_yeojohnson(income, lambdas['fin_inc_tot_amt'])
+                
+                reg_fees_inc = income * 0.74
+                exh_inc = min(exh_num * 3000, income - reg_fees_inc)
+                
+                expenses = income * 0.833
+                local_arr_exp = expenses * 0.331
+                socl_funcs_exp = expenses * 0.383
+                admintn_exp = expenses * 0.091
+                audit_exp = max(expenses*0.007, 6000)
+                
+                # Display results
+                st.markdown("### Predicted Budget*")
+                st.markdown("<h4>Income Structure</h4>", unsafe_allow_html=True)
+                st.write(f"**Total Income: ${round(round(income, -3))}**")
+                st.write(f"Registration Fees Income: ${round(round(reg_fees_inc, -3))}")
+                st.write(f"Exhibit Income: ${round(round(exh_inc, -3))}")
+                st.markdown("<h4>Expenses Structure</h4>", unsafe_allow_html=True)
+                st.write(f"**Total Expenses: ${round(round(expenses, -3))}**")
+                st.write(f"Local Arrangement Amount: ${round(round(local_arr_exp, -3))}")
+                st.write(f"Social Functions Amount: ${round(round(socl_funcs_exp, -3))}")
+                st.write(f"Administration Amount: ${round(round(admintn_exp, -3))}")
+                st.write(f"Audit Fees Amount: ${round(round(audit_exp, -3))}")
+                st.write("")
+                st.write("***The numbers are approximate and should be adjusted according to event needs**")

budget_prediction_model.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f21f28e7ba6a51923a8fa8afd72f6e7182213b6acc399d71d07d4ffc4e668075
-size 30783753
+oid sha256:1638cad00656550f71eedb730ffbb33b9fc0b42f23dc6397d975586487c842bb
+size 29676185