Create app.py

app.py

@@ -0,0 +1,245 @@
+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)
+@st.cache_resource
+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
+)