Update app.py

app.py

@@ -7,37 +7,7 @@ import os
 working_dir = os.path.dirname(os.path.abspath(__file__))
 model = pickle.load(open(f'{working_dir}/RF_Crop.sav', 'rb'))
 
-# Overview section content
-overview_text = """
-### Welcome to the Crop Recommendation App!
 
-This application assists farmers in selecting the optimal crop to cultivate, considering soil composition
-and environmental conditions. By providing information such as nitrogen, phosphorus, and
-potassium levels, as well as temperature, humidity, pH, and rainfall, users receive tailored
-recommendations for the most suitable crop out of a selection of 22 options.
-
-### How to Use the App
-1. Navigate to the "Crop Recommendation" section.
-2. Enter the values for the soil and environmental factors in the input fields.
-3. Click the "Predict" button to get the crop recommendation.
-
-### About the Model
-The recommendation is made using a Random Forest model trained on agricultural data.
-This model considers various factors to predict the best crop for your field.
-The model has been developed by analyzing many models like SVM, Random Forest,
-Decision Tree, Logistic Regression, Gaussian Naive Bayes. Random Forest has been selected based on
-the Cross Validation Accuracy & Test Accuracy.
-
-### Benefits of Using Crop Recommendation
-- **Increased Yield**: By planting the most suitable crop, you can maximize your harvest.
-- **Cost Efficiency**: Avoid wasting resources on crops that are not suited to your soil and climate.
-- **Sustainable Farming**: Promote better land use and reduce environmental impact.
-
-### Contact Us
-If you have any questions or feedback about the project, feel free to reach out:
-- **Email**: kanchanrai2307@gmail.com
-- **Github**: [kanchanrai7](https://github.com/kanchanrai7)
-"""
 
 # Define the prediction function
 def predict_crop(N, P, K, temperature, humidity, pH, rainfall):