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"""Gradio_final_version_upwork_.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1aoMHIwjlYzuHId1tfByJINN1Gazb7bzL |
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""" |
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from joblib import dump, load |
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import pickle |
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import gradio as gr |
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import pandas as pd |
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import json |
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from random import randint |
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filename="Pre_Trained_Model_Setting_v15.sav" |
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modlist_loaded = pickle.load(open(filename, 'rb')) |
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clf = modlist_loaded[0] |
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enc = modlist_loaded[1] |
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lis = modlist_loaded[2] |
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cs = modlist_loaded[3] |
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df = modlist_loaded[4] |
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df3 = modlist_loaded[5] |
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df5 = modlist_loaded[6] |
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vendor_small = modlist_loaded[7] |
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vendor_mid = modlist_loaded[8] |
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vendor_large = modlist_loaded[9] |
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def get_probability(Industry, Company_Size): |
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var=(clf.predict_proba(enc.transform([[Industry,Company_Size]]))) |
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var=var.transpose() |
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var=var.tolist() |
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name=clf.classes_.tolist() |
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dataset = pd.DataFrame({'Prob': var, 'Name':name},columns=['Prob','Name']) |
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df=dataset.sort_values('Prob',ascending=False) |
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return df |
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def start(Industry, Company_Size): |
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if (Industry == "" or Industry== None) and (Company_Size !="" and Company_Size!=None): |
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return "Please Select Industry Drop Down", Company_Size |
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if (Company_Size== "" or Company_Size== None) and (Industry != "" and Industry!=None): |
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return Industry,"Please Select Company Size Drop Down" |
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if (Industry == "" or Industry== None) and (Company_Size =="" or Industry== None): |
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return "Please Select Industry Drop Down", "Please Select Company Size Drop Down" |
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else: |
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ac="ActiveCampaign" |
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df_=get_probability(Industry, Company_Size) |
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spec_titles = ["recommendedVendor", "recommendationScore", "vendorPros", "vendorCons","userSatisfaction"] |
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cars = {} |
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title="Recommendation for " + str(Industry) + " Industry having company size " + str(Company_Size) |
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vehical_data = {title: [cars]} |
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lst_prob=[] |
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sum_first_prob=df_["Prob"].iloc[0][0] |
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for x in range(5,len(df_)): |
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sum_first_prob+=df_["Prob"].iloc[x][0] |
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lst_prob.append(sum_first_prob) |
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for x in range(1,5): |
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prob=df_["Prob"].iloc[x][0] |
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lst_prob.append(prob) |
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if df_["Name"].iloc[2]==ac: |
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df_["Name"].iloc[2],df_["Name"].iloc[0]=df_["Name"].iloc[0],df_["Name"].iloc[2] |
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dig=float(lst_prob[0]) |
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dig=dig+0.01 |
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lst_prob[0]=float(dig) |
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elif df_["Name"].iloc[3]==ac: |
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df_["Name"].iloc[3],df_["Name"].iloc[0]=df_["Name"].iloc[0],df_["Name"].iloc[3] |
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dig=float(lst_prob[0]) |
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dig=dig+0.01 |
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lst_prob[0]=float(dig) |
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elif df_["Name"].iloc[4]==ac: |
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df_["Name"].iloc[4],df_["Name"].iloc[0]=df_["Name"].iloc[0],df_["Name"].iloc[4] |
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dig=float(lst_prob[0]) |
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dig=dig+0.01 |
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lst_prob[0]=float(dig) |
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myflag=True |
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for x in range(0,5): |
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if df_["Name"].iloc[x] == ac: |
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myflag=False |
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if myflag==True: |
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df_["Name"].iloc[2]=ac |
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if int(lst_prob[1])==int(lst_prob[2]): |
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dig=float(lst_prob[1]) |
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dig=dig+0.01 |
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lst_prob[1]=float(dig) |
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df_["Name"].iloc[1],df_["Name"].iloc[2]=df_["Name"].iloc[2],df_["Name"].iloc[1] |
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else: |
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dig=float(lst_prob[2]) |
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dig=dig+0.01 |
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lst_prob[2]=float(dig) |
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for n in range(0,5): |
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name = "Recommendation Number " + str(n+1) |
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vendor_name=df_["Name"].iloc[n] |
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vendor_prob=lst_prob[n] |
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if n==0: |
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vendor_prob=round(float(vendor_prob)*100)+30 |
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else: |
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vendor_prob=round(float(vendor_prob)*100)+45 |
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if vendor_prob>round(float(lst_prob[0])*100)+30: |
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vendor_prob=vendor_prob-10 |
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vendor_pros=(df5.loc[df5['Vendor Name'] == str(vendor_name)]["Pros"]).item() |
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vendor_cons=(df5.loc[df5['Vendor Name'] == str(vendor_name)]["Cons"]).item() |
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user_satisfaction="" |
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if Company_Size=="Self Employed" or Company_Size=="2-10 Employees" or Company_Size=="11-50 Employees": |
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user_satisfaction=str(vendor_small[str(vendor_name)]) +"% of " + str(vendor_name) + "'s small business users are satisfied with it" |
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elif Company_Size=="51-200 Employees" or Company_Size=="201-500 Employees" or Company_Size=="501-1,000 Employees": |
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user_satisfaction=str(vendor_mid[vendor_name]) +"% of " + vendor_name + "'s medium business users are satisfied with it" |
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elif Company_Size=="1,001-5,000 Employees" or Company_Size=="5,001-10,000 Employees" or Company_Size=="10,000+ Employees": |
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user_satisfaction=str(vendor_large[vendor_name]) +"% of " + vendor_name + "'s large business users are satisfied with it" |
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spec_details = [str(vendor_name), str(vendor_prob)+"%", str(vendor_pros), str(vendor_cons),str(user_satisfaction)] |
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car_data = dict(zip(spec_titles, spec_details)) |
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cars[name] = car_data |
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js=json.dumps(vehical_data) |
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return vehical_data |
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face = gr.Interface(fn=start, inputs=[gr.Dropdown(lis), gr.Dropdown(cs),], outputs=["json"],allow_flagging="never") |
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face.launch() |
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