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| import streamlit as st | |
| import numpy as np | |
| import pickle | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder | |
| from sklearn.compose import ColumnTransformer | |
| import os | |
| # Set page config first | |
| st.set_page_config( | |
| page_title="Crop Prediction App", | |
| page_icon="🌾", | |
| layout="centered", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .title { | |
| color: #2c3e50; | |
| text-align: center; | |
| margin-bottom: 30px; | |
| } | |
| .stButton>button { | |
| background-color: #27ae60; | |
| color: white; | |
| border-radius: 8px; | |
| padding: 10px 20px; | |
| width: 100%; | |
| transition: all 0.3s; | |
| } | |
| .stButton>button:hover { | |
| background-color: #2ecc71; | |
| transform: scale(1.02); | |
| } | |
| .input-section { | |
| background-color: #f8f9fa; | |
| padding: 20px; | |
| border-radius: 10px; | |
| margin-bottom: 20px; | |
| } | |
| .prediction-section { | |
| background-color: #e8f5e9; | |
| padding: 20px; | |
| border-radius: 10px; | |
| margin-top: 20px; | |
| } | |
| .step-card { | |
| background-color: #ffffff; | |
| border-radius: 10px; | |
| padding: 15px; | |
| margin-bottom: 10px; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Load model (with error handling) | |
| def load_model(): | |
| try: | |
| with open("lor_f.pkl", "rb") as f: | |
| model = pickle.load(f) | |
| return model | |
| except FileNotFoundError: | |
| st.error("Model file not found. Please ensure 'lor_f.pkl' exists.") | |
| return None | |
| except Exception as e: | |
| st.error(f"Error loading model: {str(e)}") | |
| return None | |
| model = load_model() | |
| # App title | |
| st.markdown("<h1 class='title'>🌾 Smart Crop Prediction</h1>", unsafe_allow_html=True) | |
| # Main app sections | |
| tab1, tab2 = st.tabs(["Prediction", "Project Overview"]) | |
| with tab1: | |
| st.subheader("Enter Soil and Weather Conditions") | |
| with st.container(): | |
| st.markdown("<div class='input-section'>", unsafe_allow_html=True) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| nitrogen = st.slider("Nitrogen (N) level", 1, 140, 50, | |
| help="Nitrogen content in soil (1-140 kg/ha)") | |
| phosphorus = st.slider("Phosphorus (P) level", 5, 145, 50, | |
| help="Phosphorus content in soil (5-145 kg/ha)") | |
| potassium = st.slider("Potassium (K) level", 5, 205, 50, | |
| help="Potassium content in soil (5-205 kg/ha)") | |
| ph_value = st.slider("Soil pH Value", 3.0, 9.9, 6.5, 0.1, | |
| help="Soil acidity/alkalinity (3.0-9.9 pH)") | |
| with col2: | |
| temperature = st.slider("Temperature (°C)", 8.0, 43.0, 25.0, 0.1, | |
| help="Average temperature (8-43°C)") | |
| humidity = st.slider("Humidity (%)", 14, 99, 60, | |
| help="Relative humidity (14-99%)") | |
| rainfall = st.slider("Rainfall (mm)", 20, 298, 100, | |
| help="Annual rainfall (20-298 mm)") | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| if st.button("Predict Optimal Crop", key="predict_btn"): | |
| if model is None: | |
| st.error("Model not available. Please check the model file.") | |
| else: | |
| try: | |
| user_data = np.array([[nitrogen, phosphorus, potassium, temperature, | |
| humidity, ph_value, rainfall]]) | |
| prediction = model.predict(user_data) | |
| with st.container(): | |
| st.markdown("<div class='prediction-section'>", unsafe_allow_html=True) | |
| st.success(f"### Recommended Crop: **{prediction[0]}**") | |
| # Add some visual feedback | |
| st.write("Based on your inputs, the optimal crop for these conditions is:") | |
| st.markdown(f"<h3 style='text-align: center; color: #27ae60;'>{prediction[0]}</h3>", | |
| unsafe_allow_html=True) | |
| # Add some additional information | |
| st.markdown(""" | |
| **Tips for better yield:** | |
| - Maintain proper irrigation | |
| - Monitor soil nutrients regularly | |
| - Follow recommended crop rotation practices | |
| """) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| except Exception as e: | |
| st.error(f"Prediction error: {str(e)}") | |
| with tab2: | |
| st.header("Machine Learning Project Steps") | |
| st.write(""" | |
| This crop prediction system was developed following these key machine learning steps: | |
| """) | |
| steps = { | |
| "1. Problem Definition 🧠": { | |
| "description": "Identify the agricultural challenge and define objectives for crop prediction.", | |
| "actions": [ | |
| "Determine key factors affecting crop growth", | |
| "Define success metrics for the model" | |
| ] | |
| }, | |
| "2. Data Collection 📊": { | |
| "description": "Gather comprehensive agricultural datasets.", | |
| "actions": [ | |
| "Collect soil nutrient data (N, P, K, pH)", | |
| "Gather weather and climate data", | |
| "Obtain historical crop yield information" | |
| ] | |
| }, | |
| "3. Data Cleaning 🧹": { | |
| "description": "Prepare raw data for analysis by addressing quality issues.", | |
| "actions": [ | |
| "Handle missing values and outliers", | |
| "Correct measurement inconsistencies", | |
| "Remove duplicate entries" | |
| ] | |
| }, | |
| "4. Exploratory Analysis 🔍": { | |
| "description": "Understand data patterns and relationships.", | |
| "actions": [ | |
| "Analyze feature distributions", | |
| "Identify correlations between variables", | |
| "Visualize data patterns" | |
| ] | |
| }, | |
| "5. Feature Engineering ⚙️": { | |
| "description": "Select and transform relevant features.", | |
| "actions": [ | |
| "Normalize numerical features", | |
| "Create derived features if needed", | |
| "Select most predictive features" | |
| ] | |
| }, | |
| "6. Model Selection 🤖": { | |
| "description": "Choose appropriate machine learning algorithms.", | |
| "actions": [ | |
| "Compare classification algorithms", | |
| "Evaluate based on accuracy and performance", | |
| "Select final model (Logistic Regression)" | |
| ] | |
| }, | |
| "7. Model Training 🏋️♂️": { | |
| "description": "Train the selected model with prepared data.", | |
| "actions": [ | |
| "Split data into training and validation sets", | |
| "Train model with optimal parameters", | |
| "Validate model performance" | |
| ] | |
| }, | |
| "8. Model Evaluation 📈": { | |
| "description": "Assess model performance rigorously.", | |
| "actions": [ | |
| "Calculate precision, recall, and F1-score", | |
| "Analyze confusion matrix", | |
| "Test on unseen data" | |
| ] | |
| }, | |
| "9. Deployment 🚀": { | |
| "description": "Implement the model in a production environment.", | |
| "actions": [ | |
| "Develop user-friendly interface", | |
| "Create API endpoints if needed", | |
| "Ensure scalability" | |
| ] | |
| }, | |
| "10. Monitoring 🔄": { | |
| "description": "Continuously track and improve the system.", | |
| "actions": [ | |
| "Monitor prediction accuracy", | |
| "Update model with new data", | |
| "Address concept drift" | |
| ] | |
| } | |
| } | |
| for step, content in steps.items(): | |
| with st.expander(step): | |
| st.write(content["description"]) | |
| st.markdown("**Key Actions:**") | |
| for action in content["actions"]: | |
| st.markdown(f"- {action}") | |
| st.markdown("---") | |
| st.write("This application helps farmers make data-driven decisions for optimal crop selection.") | |
| # Add footer | |
| st.markdown("---") | |
| st.markdown( | |
| """ | |
| <div style="text-align: center; color: #777; font-size: 0.9em;"> | |
| Agricultural Decision Support System • Powered by Machine Learning | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) |