From 54c94edb7a222c7c5585ee18648ce809e5d4ad0e Mon Sep 17 00:00:00 2001
From: Biswakalyan Bhuyan <biswa@surgot.in>
Date: Wed, 27 Nov 2024 23:09:38 +0530
Subject: Added the price preediction output to the predict.py

---
 main.py    |  2 +-
 predict.py | 41 ++++++++++++++++++++++++++++-------------
 2 files changed, 29 insertions(+), 14 deletions(-)

diff --git a/main.py b/main.py
index a8bdec8..b0d6673 100644
--- a/main.py
+++ b/main.py
@@ -90,7 +90,7 @@ def train_model(df, target, model_name):
     # Save the model
     model_file = f'{model_name}.pkl'
     joblib.dump(best_model, model_file)
-    print("Model saved as '{model_file}'")
+    print(f"Model saved as '{model_file}'")
 
 # Main Function
 def main():
diff --git a/predict.py b/predict.py
index 5d470b6..9ab313c 100644
--- a/predict.py
+++ b/predict.py
@@ -25,23 +25,38 @@ def predict(input_data):
     
     return mileage, price, int(year)
 
-# Sample data for prediction
-data = {
-    'Year': 2022,
-    'Kilometers_Driven': 30000,
-    'Fuel_Type': 'Petrol',
-    'Transmission': 'Manual',
-    'Owner_Type': 'First',
-    'Location': 'Mumbai',
-    'Engine CC': 1200,
-    'Power': 85,
-    'Seats': 5
-}
+def collect_input():
+    print("Enter the car details for prediction:")
+    year = int(input("Year of Manufacture (e.g., 2022): "))
+    kilometers_driven = int(input("Kilometers Driven (e.g., 30000): "))
+    fuel_type = input("Fuel Type (Petrol/Diesel/CNG/Electric): ")
+    transmission = input("Transmission (Manual/Automatic): ")
+    owner_type = input("Owner Type (First/Second/Third/Fourth & Above): ")
+    location = input("Location (e.g., Mumbai): ")
+    engine_cc = int(input("Engine Capacity in CC (e.g., 1200): "))
+    power = float(input("Power in BHP (e.g., 85): "))
+    seats = int(input("Number of Seats (e.g., 5): "))
+
+    return {
+        'Year': year,
+        'Kilometers_Driven': kilometers_driven,
+        'Fuel_Type': fuel_type,
+        'Transmission': transmission,
+        'Owner_Type': owner_type,
+        'Location': location,
+        'Engine CC': engine_cc,
+        'Power': power,
+        'Seats': seats
+    }
+
+
+# Collect input data from the user
+data = collect_input()
 
 # Make prediction 
-
 predicted_mileage, predicted_price, predicted_year = predict(data)
 
+print("\nPrediction Results:")
 print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
 print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")
 print(f"Predicted Year: {predicted_year}")
-- 
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