Delete crop_selector2.py

crop_selector2.py

@@ -1,43 +0,0 @@
-import pandas as pd
-import streamlit as st
-from sklearn.naive_bayes import GaussianNB
-
-dataset = pd.read_csv(r"C:\Users\Kush\Desktop\hackathons\IITB\Crop_selection.csv")
-features = dataset[['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']]
-target = dataset['label']
-
-prices = pd.read_csv(r"C:\Users\Kush\Desktop\hackathons\IITB\crop_prices.csv")
-
-
-model = GaussianNB()
-model.fit(features, target)
-
-st.title("Crop Selection App")
-
-st.header("Input Soil Data:")
-nitrogen = st.number_input("Nitrogen", min_value=0, max_value=100, value=50)
-phosphorus = st.number_input("Phosphorus", min_value=0, max_value=100, value=50)
-potassium = st.number_input("Potassium", min_value=0, max_value=100, value=50)
-temperature = st.number_input("Temperature", min_value=0.0, max_value=100.0, value=25.0,step=1.,format="%.4f")
-humidity = st.number_input("Humidity", min_value=0.0, max_value=100.0, value=50.0,step=1.,format="%.4f")
-ph = st.number_input("pH", min_value=0.0, max_value=14.0, value=7.0,step=1.,format="%.4f")
-rainfall = st.number_input("Rainfall", min_value=0.0, max_value=1000.0, value=500.0,step=1.,format="%.4f")
-
-user_input = [[nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall]]
-
-if st.button("Predict"):
-    predicted_crop = model.predict_proba(user_input)
-
-    crop_probabilities = list(zip(model.classes_, predicted_crop[0]))
-
-    # Sort crops based on probability estimates
-    sorted_crops = sorted(crop_probabilities, key=lambda x: x[1], reverse=True)
-
-    # Display the sorted crops
-    st.header("Top 3 Crops to grow:")
-    for i, (crop, probability) in enumerate(sorted_crops[:3]):
-        prob_percent = probability*100
-        #st.write(f"{i+1}. {crop}: {prob_percent:.2f}%")
-        average_price = prices.loc[prices['CROP'] == crop, 'AVG PRICES'].values[0]
-        
-        st.write(f"{i+1}. {crop}: {prob_percent:.2f}% || Average Price: Rs.{average_price} / Quintal")