The code file for the estimator

This file let's the user input a bunch of parameters and then spins out the estimated house price based upon that value!

app.py

@@ -0,0 +1,77 @@
+#First we have to import libraries
+#Think of libraries as "pre-written programs" that help us accelerate what we do in Python
+
+#Gradio is a web interface library for deploying machine learning models
+import gradio as gr
+
+#Pickle is a library that lets us work with machine learning models, which in Python are typically in a "pickle" file format
+import pickle
+
+#Orange is the Python library used by... well, Orange!
+from Orange.data import * 
+
+#This is called a function. This function can be "called" by our website (when we click submit). Every time it's called, the function runs.
+#Within our function, there are inputs (bedrooms1, bathrooms1, etc.). These are passed from our website front end, which we will create further below.
+def make_prediction(bedrooms1, bathrooms1, stories1, mainroad1,guestroom1,basement1,hotwaterheating1,airconditioning1,parking1,prefarea1,furnishingstatus1):
+    
+	#Because we already trained a model on these variables, any inputs we feed to our model has to match the inputs it was trained on.
+	#Even if you're not familiar with programming, you can probably decipher the below code.
+    bedrooms=DiscreteVariable("bedrooms",values=["1","2","3","4","5","6"])
+    bathrooms=DiscreteVariable("bathrooms",values=["1","2","3"])
+    stories=DiscreteVariable("stories",values=["1","2","3","4"])
+    mainroad=DiscreteVariable("mainroad",values=["yes","no"])
+    guestroom=DiscreteVariable("guestroom",values=["yes","no"])
+    basement=DiscreteVariable("basement",values=["yes","no"])
+    hotwaterheating=DiscreteVariable("hotwaterheating",values=["yes","no"])
+    airconditioning=DiscreteVariable("airconditioning",values=["yes","no"])
+    parking=DiscreteVariable("parking",values=["0","1","2","3"])
+    prefarea=DiscreteVariable("prefarea",values=["yes","no"])
+    furnishingstatus=DiscreteVariable("furnishingstatus",values=['furnished','semi-furnished','unfurnished'])
+    
+	#This code is a bit of housekeeping. 
+	#Since our model is expecting discrete inputs (just like in Orange), we need to convert our numeric values to strings
+    bedrooms1=str(bedrooms1)
+    bathrooms1=str(bathrooms1)
+    stories1=str(stories1)
+    parking1=str(parking1)
+    prefarea1=str(prefarea1)
+	
+	#A domain is essentially an Orange file definition. Just like the one you set with the "file node" in the tool.
+    domain=Domain([bedrooms,bathrooms,stories,mainroad,guestroom,basement,hotwaterheating,airconditioning,parking,prefarea,furnishingstatus])
+	
+	#Our data is the data being passed by the website inputs. This gets mapped to our domain, which we defined above.
+    data=Table(domain,[[bedrooms1, bathrooms1, stories1, mainroad1,guestroom1,basement1,hotwaterheating1,airconditioning1,parking1,prefarea1,furnishingstatus1]])
+	
+	#Next, we can work on our predictions!
+
+	#This tiny piece of code loads our model (pickle load).
+    with open("model_custom.pkcls", "rb") as f:
+		#Then feeds our data into the model, then sets the "preds" variable to the prediction output for our class variable, which is price.	
+        clf  = pickle.load(f)
+        ar=clf(data)
+        preds=clf.domain.class_var.str_val(ar)
+        preds="$"+preds
+		#Finally, we send the prediction to the website.
+        return preds
+
+#Now that we have defined our prediction function, we need to create our web interface.
+#This code creates the input components for our website. Gradio has this well documented and it's pretty easy to modify.
+NumberOfBedrooms=gr.Slider(minimum=1,maximum=6,step=1,label="How many bedrooms?")
+NumberOfBathrooms=gr.Slider(minimum=1,maximum=3,step=1,label="How many bathrooms?")
+NumberOfStories=gr.Slider(minimum=1,maximum=4,step=1,label="How many stories?")
+OnMainRoad=gr.Dropdown(["yes","no"],label="Is the house on a main road?")
+HasGuestRoom=gr.Dropdown(["yes","no"],label="Does the house have a guest room?")
+HasBasement=gr.Dropdown(["yes","no"],label="Does the house have a basement?")
+HasHotWaterHeating=gr.Dropdown(["yes","no"],label="Does the house have hot water heating?")
+HasAC=gr.Dropdown(["yes","no"],label="Does the house have air conditioning?")
+parkingstatus=gr.Slider(minimum=0,maximum=3,step=1,label="How many parking spots?")
+HasPrefArea=gr.Dropdown(["yes","no"],label="Is the house in a preferred area?")
+Furnished=gr.Dropdown(['furnished','semi-furnished','unfurnished'],label='Is the house furnished?')
+
+# Next, we have to tell Gradio what our model is going to output. In this case, it's going to be a text result (house prices).
+output = gr.Textbox(label="House price estimate:")
+
+#Then, we just feed all of this into Gradio and launch the web server. 
+#Our fn (function) is our make_prediction function above, which returns our prediction based on the inputs.
+app = gr.Interface(fn = make_prediction, inputs=[NumberOfBedrooms, NumberOfBathrooms, NumberOfStories,OnMainRoad,HasGuestRoom,HasBasement,HasHotWaterHeating,HasAC,parkingstatus,HasPrefArea,Furnished], outputs=output)
+app.launch()