pricefix

app.py

@@ -47,6 +47,8 @@ featureToName = {
      'streetName' : 'Name of street',
 }
 
+topAgencies = ['Fastighetsbyrån','Notar','Svensk Fastighetsförmedling','HusmanHagberg','Länsförsäkringar Fastighetsförmedling','Erik Olsson','SkandiaMäklarna','Svenska Mäklarhuset','Bjurfors','Mäklarhuset','BOSTHLM','Innerstadsspecialisten','MOHV','Mäklarringen','Historiska Hem','Södermäklarna','Karlsson & Uddare','UNIK Fastighetsförmedling','Edward & Partners','Widerlöv']
+
 def downloadAutogluonModel():
     # Download saved Autogluon model from Hopsworks
     project = hopsworks.login(
@@ -246,19 +248,61 @@ def xgboostPred(df):
 
     return results
 
+def addExtraAgencyFun(df):
+    # Make 20 copies of the first row with the 20 different top agencies in Sweden
+    # Make a copy of the first row
+    firstRow = df.iloc[0]
+    # Make a list of the copies
+    rows = [firstRow] * len(topAgencies)
+    # Make a dataframe from the list
+    df2 = pd.DataFrame(rows)
+
+    # Add the top agencies to the dataframe
+    for i, agency in enumerate(topAgencies):
+        df2['agency'].iloc[i] = agency
+    
+    # Concatenate the two dataframes
+    df = pd.concat([df, df2], ignore_index=True)
+
+    return df
+
 def autoPred(df):
-    print(df.head())
-    print(df.columns)
+    df = addExtraAgencyFun(df)
     res = autoModel.predict(df)
 
     # Convert to a list
     res = res.tolist()
 
-    print('________________________________')
-    print(res, type(res))
-    print('________________________________')
+    # Get the last 20 values
+    agencyResults = res[-20:]
+    res = res[:-20]
+
+    # Get the mean of the agencies
+    meanPrice = sum(agencyResults) / len(agencyResults)
+
+    agencyToResult = {agency:result for agency, result in zip(topAgencies, agencyResults)}
 
-    return res
+    # Get the top and bottom 3 agencies with the highest results
+    sortedAgencies = sorted(agencyToResult.items(), key=lambda x: x[1])
+    top3 = sortedAgencies[-3:]
+    bottom3 = sortedAgencies[:3]
+
+    agencyString = parseAgencyResult(top3, bottom3, meanPrice)
+
+    return res, agencyString
+
+def parseAgencyResult(top3, bottom3, meanPrice):
+    toReturn = 'To get the most money for your apartment, you should sell it with the help of one of these agencies:\n'
+    toReturn += 'Top 3:\n'
+    for agency, result in top3:
+        diff = result - meanPrice
+        toReturn += f'{agency}: {parsePrice(result)} ({parsePrice(diff)} above mean)\n'
+    toReturn += '\n'
+    for agency, result in bottom3:
+        diff = result - meanPrice
+        toReturn += f'{agency}: {parsePrice(result)} ({parsePrice(diff)} below mean)\n'
+
+    return toReturn
 
 def isValidInput(streetName, number, sqm, rooms, monthlyFee, monthlyCost, floor, yearBuilt):
     # Street name is a string, all other values are numbers
@@ -327,8 +371,7 @@ def sthlm(streetName, number, sqm, rooms, monthlyFee, monthlyCost, floor, yearBu
 
     pricePred = None
     if auto:
-        pricePred = autoPred(df)
-        '', 'Autogluon is not working right now, please try again later'
+        pricePred, agencyInfo = autoPred(df)
     else:
         df = xgbFix(df)
         pricePred = xgboostPred(df)
@@ -347,6 +390,10 @@ def sthlm(streetName, number, sqm, rooms, monthlyFee, monthlyCost, floor, yearBu
                 result.append(f'If sold {explanation} it would have been worth more: {parsePrice(pred)} (+{parsePrice(diff)})')
             else:
                 result.append(f'If sold {explanation} it would have been worth less: {parsePrice(pred)} ({parsePrice(diff)})')
+    if auto:
+        result.append(agencyInfo)
+
+            
 
     return '\n'.join(result), ''