Add files via upload

streamlitapp.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import sklearn
+from sklearn.preprocessing import MinMaxScaler
+
+st.set_page_config(page_title="Crop Recommendation App", layout="wide")
+
+# Load the scaler from the pickle file
+with open('scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+
+# Define the transformation function
+
+
+def transform_input(data):
+    d = data[0]
+    for i in range(len(d)):
+        if idx_to_skew[i] == 0:
+            d[i] = np.square(d[i])
+        else:
+            d[i] = np.sqrt(d[i])
+    x = np.array([d])
+    x = scaler.transform(x)
+    return x
+
+
+# Add a title and description
+st.title("🌾 Crop Recommendation App")
+st.write("""
+This app predicts the **crop type** based on soil and weather conditions! 🌱
+Upload a CSV file with your data or use the sliders and text inputs below to enter your data manually.
+""")
+
+# Sidebar for user input
+st.sidebar.header('📝 User Input Features')
+st.sidebar.markdown("""
+Upload a CSV file with the features or use the sliders and text inputs below for manual input.
+
+[Example CSV input file](https://example.com/example.csv)
+""")
+
+# Define the skew transformation
+idx_to_skew = [1, 0, 1, 0, 0, 0, 1]
+
+# File uploader for CSV input
+uploaded_file = st.sidebar.file_uploader(
+    "Upload your input CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    input_df = pd.read_csv(uploaded_file)
+    st.sidebar.write("Uploaded CSV File:")
+    st.sidebar.write(input_df)
+else:
+    st.sidebar.subheader('🔄 Manual Input (or use sliders)')
+
+    st.sidebar.write("**Use the sliders to adjust values:**")
+
+    nitrogen = st.sidebar.slider('Nitrogen', 0, 100, 50)
+    phosphorus = st.sidebar.slider('Phosphorus', 0, 100, 50)
+    potassium = st.sidebar.slider('Potassium', 0, 100, 50)
+    temperature = st.sidebar.slider('Temperature (°C)', 0.0, 50.0, 25.0)
+    humidity = st.sidebar.slider('Humidity (%)', 0.0, 100.0, 50.0)
+    ph = st.sidebar.slider('pH', 0.0, 14.0, 7.0)
+    rainfall = st.sidebar.slider('Rainfall (mm)', 0.0, 500.0, 100.0)
+
+    # Define text inputs for manual input
+    nitrogen_text = st.sidebar.number_input(
+        'Nitrogen (0-100)', min_value=0, max_value=100, value=nitrogen)
+    phosphorus_text = st.sidebar.number_input(
+        'Phosphorus (0-100)', min_value=0, max_value=100, value=phosphorus)
+    potassium_text = st.sidebar.number_input(
+        'Potassium (0-100)', min_value=0, max_value=100, value=potassium)
+    temperature_text = st.sidebar.number_input(
+        'Temperature (°C) (0.0-50.0)', min_value=0.0, max_value=50.0, step=0.1, value=temperature)
+    humidity_text = st.sidebar.number_input(
+        'Humidity (%) (0.0-100.0)', min_value=0.0, max_value=100.0, step=0.1, value=humidity)
+    ph_text = st.sidebar.number_input(
+        'pH (0.0-14.0)', min_value=0.0, max_value=14.0, step=0.1, value=ph)
+    rainfall_text = st.sidebar.number_input(
+        'Rainfall (mm) (0.0-500.0)', min_value=0.0, max_value=500.0, step=0.1, value=rainfall)
+
+    data_slider = {
+        'NITROGEN': nitrogen,
+        'PHOSPHORUS': phosphorus,
+        'POTASSIUM': potassium,
+        'TEMPERATURE': temperature,
+        'HUMIDITY': humidity,
+        'PH': ph,
+        'RAINFALL': rainfall
+    }
+    input_df_slider = pd.DataFrame(data_slider, index=[0])
+
+    data_text = {
+        'NITROGEN': nitrogen_text,
+        'PHOSPHORUS': phosphorus_text,
+        'POTASSIUM': potassium_text,
+        'TEMPERATURE': temperature_text,
+        'HUMIDITY': humidity_text,
+        'PH': ph_text,
+        'RAINFALL': rainfall_text
+    }
+    input_df_text = pd.DataFrame(data_text, index=[0])
+
+    use_sliders = st.sidebar.radio(
+        "Choose input method:", ("Use Sliders", "Use Text Inputs"))
+
+    if use_sliders == "Use Sliders":
+        st.sidebar.write("**Slider Values:**")
+        st.sidebar.write(input_df_slider)
+        input_df = input_df_slider
+    else:
+        st.sidebar.write("**Text Input Values:**")
+        st.sidebar.write(input_df_text)
+        input_df = input_df_text
+
+# Display user input features
+st.subheader('🌟 User Input Features')
+st.write(input_df)
+
+# Transform the input data
+input_data = transform_input(input_df.values)
+
+# Load the trained model
+load_clf = pickle.load(open('best_RF.pkl', 'rb'))
+
+# Make predictions
+prediction = load_clf.predict(input_data)
+prediction_proba = load_clf.predict_proba(input_data)
+
+# Display the prediction
+st.subheader('🔍 Prediction')
+crop_types = ['banana', 'blackgram', 'chickpea', 'coconut', 'coffee', 'cotton', 'jute',
+              'kidneybeans', 'lentil', 'maize', 'mango', 'mothbeans', 'mungbean', 'muskmelon',
+              'orange', 'papaya', 'pigeonpeas', 'pomegranate', 'rice', 'watermelon']
+
+predicted_crop = prediction[0]
+st.write(f"**Predicted Crop Type:** :green[{predicted_crop}]")
+
+# Display prediction probability
+st.subheader('📊 Prediction Probability')
+prob_df = pd.DataFrame(prediction_proba, columns=crop_types)
+st.bar_chart(prob_df.T, height=300, use_container_width=True)
+
+# Display the class with the highest probability
+highest_prob_class_index = np.argmax(prediction_proba, axis=1)
+highest_prob_class = crop_types[highest_prob_class_index[0]]
+highest_prob_value = prediction_proba[0, highest_prob_class_index[0]]
+
+st.subheader('🏆 Class with Highest Probability')
+st.write(f"**Class:** :blue[{highest_prob_class}]")
+st.write(f"**Probability:** :blue[{highest_prob_value:.4f}]")
+
+st.markdown("---")
+st.markdown("Thank you for using the Crop Recommendation App! 🌾")