Upload app.py

app.py

@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""Gradio_final_version_upwork_.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1aoMHIwjlYzuHId1tfByJINN1Gazb7bzL
+"""
+
+
+
+from joblib import dump, load
+import pickle
+import gradio as gr
+import pandas as pd
+import json
+from random import randint
+filename="Pre_Trained_Model_Setting_v15.sav"
+modlist_loaded = pickle.load(open(filename, 'rb'))
+
+clf = modlist_loaded[0]
+enc = modlist_loaded[1]
+lis = modlist_loaded[2]
+cs  = modlist_loaded[3]
+df  = modlist_loaded[4]
+df3 = modlist_loaded[5]
+df5 = modlist_loaded[6]
+vendor_small = modlist_loaded[7]
+vendor_mid = modlist_loaded[8]
+vendor_large = modlist_loaded[9]
+
+def get_probability(Industry, Company_Size):
+  var=(clf.predict_proba(enc.transform([[Industry,Company_Size]])))
+  var=var.transpose()
+  var=var.tolist()
+  name=clf.classes_.tolist()
+  dataset = pd.DataFrame({'Prob': var, 'Name':name},columns=['Prob','Name'])
+  df=dataset.sort_values('Prob',ascending=False)
+  return df
+
+def start(Industry, Company_Size):
+
+  
+    if (Industry == "" or Industry== None) and (Company_Size !="" and  Company_Size!=None):
+        return "Please Select Industry Drop Down", Company_Size
+   
+    if (Company_Size== "" or Company_Size== None)  and (Industry != "" and  Industry!=None):
+            return Industry,"Please Select Company Size Drop Down"
+   
+    if (Industry == ""  or Industry== None) and (Company_Size =="" or Industry== None): 
+            return "Please Select Industry Drop Down", "Please Select Company Size Drop Down"
+    else:
+
+
+
+        df_=get_probability(Industry, Company_Size)
+        spec_titles = ["recommendedVendor", "recommendationScore", "vendorPros", "vendorCons"]
+        cars = {}
+        title="Recommendation for " + str(Industry) + " Industry having company size " + str(Company_Size)
+        vehical_data = {title: [cars]}
+        lst_prob=[]
+        sum_first_prob=df_["Prob"].iloc[0][0]
+
+        for x in range(5,len(df_)):
+            sum_first_prob+=df_["Prob"].iloc[x][0]
+        
+        lst_prob.append(sum_first_prob)
+
+        for x in range(1,5):
+            prob=df_["Prob"].iloc[x][0]
+            lst_prob.append(prob)
+        
+        
+        for n in range(0,5):
+            name = "Recommendation Number " + str(n+1)
+            vendor_name=df_["Name"].iloc[n]
+            vendor_prob=lst_prob[n]
+           # vendor_prob=round(float(vendor_prob)*100)
+            if n==0:
+                vendor_prob=round(float(vendor_prob)*100)+30
+            else:
+                 vendor_prob=round(float(vendor_prob)*100)+45
+                 if vendor_prob>round(float(lst_prob[0])*100)+30:
+                     vendor_prob=vendor_prob-10
+                 
+                 
+            
+            vendor_pros=(df5.loc[df5['Vendor Name'] == str(vendor_name)]["Pros"]).item()
+            vendor_cons=(df5.loc[df5['Vendor Name'] == str(vendor_name)]["Cons"]).item()
+            user_satisfaction=""
+
+
+
+            
+
+            if Company_Size=="Self Employed" or Company_Size=="2-10 Employees" or Company_Size=="11-50 Employees":
+                user_satisfaction=str(vendor_small[str(vendor_name)]) +"% of " + str(vendor_name) + "'s small business users are satisfied with it"
+                
+            elif Company_Size=="51-200 Employees" or Company_Size=="201-500 Employees" or Company_Size=="501-1,000 Employees":
+                user_satisfaction=str(vendor_mid[vendor_name]) +"% of" + vendor_name + "'s medium business users are satisfied with it"
+    
+            elif Company_Size=="1,001-5,000 Employees" or Company_Size=="5,001-10,000 Employees" or Company_Size=="10,000+ Employees":
+                user_satisfaction=str(vendor_large[vendor_name]) +"% of" + vendor_name + "'s large business users are satisfied with it"
+      
+
+
+
+
+
+
+
+
+
+            spec_details = [str(vendor_name), str(vendor_prob)+"%", str(vendor_pros), str(vendor_cons),str(user_satisfaction)]
+            car_data = dict(zip(spec_titles, spec_details))
+            cars[name] = car_data
+        js=json.dumps(vehical_data)
+        return vehical_data
+
+face = gr.Interface(fn=start, inputs=[gr.Dropdown(lis), gr.Dropdown(cs),], outputs=["json"],allow_flagging="never")
+face.launch()
+