Create app.py

app.py

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+#1. Importing lib
+  
+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import accuracy_score,r2_score
+
+#2.Data Preprocesing
+
+df=pd.read_csv("car data.csv")
+
+df.head()
+df.tail()
+df.info()
+df.describe()
+df.isnull().sum()
+
+df["Fuel_Type"].unique()
+
+df["Seller_Type"].unique()
+
+df["Transmission"].unique()
+
+df.replace({"Fuel_Type":{"Diesel":0,"Petrol":1,"CNG":2}},inplace=True)
+
+df.replace({"Seller_Type":{"Dealer":0,"Individual":1}},inplace=True)
+
+df.replace({"Transmission":{"Manual":0,"Automatic":1}},inplace=True)
+
+# Spliting Data into x and y(independent/dependent)
+x= df.drop(["Car_Name","Selling_Price"],axis=1)
+y = df["Selling_Price"]
+
+#3. Modeling Part
+
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)
+model=RandomForestRegressor()
+
+model.fit(x_train,y_train)
+model.fit(x_test,y_test)
+
+x_predict=model.predict(x_train)
+x_accuracy=r2_score(x_predict,y_train)
+
+y_predict=model.predict(x_test)
+y_accuracy=r2_score(y_predict,y_test)
+
+#4. UI  For Model(Help of Gradio)
+
+# Function to make predictions
+def predict_car_price(year,Present_Price, km_driven, fuel_type, seller_type, transmission,owner):
+    input_data = np.array([[year,Present_Price, km_driven, fuel_type, seller_type, transmission,owner]])
+    prediction = model.predict(input_data)
+    return f"Predicted Selling Price: ₹{prediction[0]:,.2f}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_car_price,  # Function that makes predictions
+    inputs=[
+        gr.Slider(minimum=2003, maximum=2018, step=1, label="Car Year (Year of Manufacture)"),
+        gr.Slider(minimum=0, maximum=93, step=1, label="Present Pcice "),
+        gr.Slider(minimum=0, maximum=500000, step=1000, label="Kilometers Driven (km)"),
+        gr.Dropdown([0, 1, 2], label="Fuel Type (0 = Diesel, 1 = Petrol, 2 = CNG)"),
+        gr.Dropdown([0, 1], label="Seller Type (0 = Dealer, 1 = Individual)"),
+        gr.Dropdown([0, 1], label="Transmission (0 = Manual, 1 = Automatic)"),
+        gr.Dropdown([0, 1, 2, 3], label="Number of Owners (0 = First, 1 = Second, 2 = Third, 3 = Fourth)")
+    ],  # Input fields for the model's features
+    outputs="text"  # Output the predicted selling price as text
+)
+
+# Launch the Gradio UI
+iface.launch()