This file contains the code that has been used to develop the loan approval checker tool using gradio and orange2



webapp_modified.py

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+#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
+import statistics
+
+
+
+#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(gender1,married1,dependents1,education1,self_employed1,applicantincome1,coapplicantincome1,loanamount1,loanamountterm1,credit_history1,property_area1):
+    
+	#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.
+    gender=DiscreteVariable("Gender",values=["Male","Female"])
+    married=DiscreteVariable("Married",values=["Yes","No"])
+    dependents=DiscreteVariable("Dependents",values=["0","1","2","3+"])
+    education=DiscreteVariable("Education",values=["Graduate","Not Graduate"])
+    self_employed=DiscreteVariable("Self_Employed",values=["Yes","No"])
+    applicantincome=ContinuousVariable("ApplicantIncome")
+    coapplicantincome=ContinuousVariable("CoapplicantIncome")
+    loanamount=ContinuousVariable("LoanAmount")
+    loanamountterm=ContinuousVariable("Loan_Amount_Term")
+    credit_history=DiscreteVariable("Credit_History",values=["0","1"])
+    property_area=DiscreteVariable("Property_Area",values=["Rural","Semiurban","Urban"])
+
+	#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
+    dependents1=str(dependents1)
+    credit_history1=str(credit_history1)
+    applicantincome1=float((applicantincome1-5403.46)/1.13)
+    print(applicantincome1)
+    coapplicantincome1=float((coapplicantincome1-1621.25)/1.80347)
+    print(coapplicantincome1)
+    loanamount1=float((loanamount1-146.41)/0.58)
+    print(loanamount1)
+    loanamountterm1=float((loanamountterm1-342)/0.19)
+    print(loanamountterm1)
+	#A domain is essentially an Orange file definition. Just like the one you set with the "file node" in the tool.
+    domain=Domain([gender,married,dependents,education,self_employed,applicantincome,coapplicantincome,loanamount,loanamountterm,credit_history,property_area])
+	
+	#Our data is the data being passed by the website inputs. This gets mapped to our domain, which we defined above.
+    data=Table(domain,[[gender1,married1,dependents1,education1,self_employed1,applicantincome1,coapplicantincome1,loanamount1,loanamountterm1,credit_history1,property_area1]])
+	
+	#Next, we can work on our predictions!
+
+	#This tiny piece of code loads our model (pickle load).
+    with open("model.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)
+		#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.
+TheGender=gr.Dropdown(["Male","Female"],label="Whats your gender?")
+IsMarried=gr.Dropdown(["Yes","No"],label="Are you married?")
+HasDependents=gr.Dropdown(["0","1","2","3+"], label="How many dependents do you have?")
+#HasDependents=gr.Slider(minimum=0,maximum=3,step=1,label="How many dependents do you have?")
+IsEducated=gr.Dropdown(["Graduate","Not Graduate"],label="Whats your education status?")
+IsSelfEmployed=gr.Dropdown(["Yes","No"],label="Are you self-employed?")
+ApplicantIncom=gr.Number(label="Whats the applicant income?")
+CoApplicantIncome=gr.Number(label="Whats the co-applicant income? If any!")
+LoanAmount=gr.Number(label="Whats the Loan Amount?")
+LoanAmountTerm=gr.Number(label="Whats the Loan Amount Term?")
+HasCreditHistory=gr.Dropdown(["0","1"],label="Do you have credit history?")
+PropertyArea=gr.Dropdown(['Rural','Semiurban','Urban'],label='What is the area where your property is located?')
+
+# 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="Loan Approval Status: ")
+
+#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=[TheGender, IsMarried, HasDependents,IsEducated,IsSelfEmployed,ApplicantIncom,CoApplicantIncome,LoanAmount,LoanAmountTerm,HasCreditHistory,PropertyArea], outputs=output)
+app.launch()