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Author: alisamad1

Crop_Prediction_Project (1).ipynb

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+import streamlit as st
+import pandas as pd
+import pickle
+# Load and cache your dataset
+@st.cache_data
+def load_dataset():
+    # Replace 'your_dataset.csv' with your actual CSV file name
+    df = pd.read_csv('Crop.csv')  # Change this to your CSV file name
+    return df
+# Load your trained model (adjust path as needed)
+@st.cache_data
+def load_model():
+    # Replace with your actual model loading code
+    return pickle.load(open('crop_model.pkl', 'rb'))
+# Load the dataset
+df = load_dataset()
+model = load_model()
+# Streamlit app
+st.title("🌾 Crop Prediction System")
+st.write("Enter the farming conditions to predict the best crop")
+# Optional: Show dataset info
+with st.sidebar:
+    st.subheader("Dataset Information")
+    st.write(f"Total samples: {len(df)}")
+    st.write(f"Features: {list(df.columns)}")
+    if st.checkbox("Show sample data"):
+        st.dataframe(df.head())
+# Create input fields for features
+st.subheader("Enter Soil and Climate Conditions")
+col1, col2 = st.columns(2)
+with col1:
+    nitrogen = st.number_input("Nitrogen (N)", min_value=0, max_value=300, value=50)
+    phosphorus = st.number_input("Phosphorus (P)", min_value=0, max_value=300, value=50)
+    potassium = st.number_input("Potassium (K)", min_value=0, max_value=300, value=50)
+with col2:
+    temperature = st.number_input("Temperature (°C)", min_value=0.0, max_value=50.0, value=25.0)
+    humidity = st.slider("Humidity (%)", 0, 100, 50)
+    ph = st.slider("pH Level", 0.0, 14.0, 7.0)
+rainfall = st.number_input("Rainfall (mm)", min_value=0.0, max_value=500.0, value=100.0)
+# Prediction
+if st.button("🔮 Predict Best Crop", type="primary"):
+    # Create input array for prediction
+    features = [[nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall]]
+    # Convert to DataFrame with correct column names (adjust based on your dataset)
+    feature_names = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']
+    input_df = pd.DataFrame(features, columns=feature_names)
+    # Make prediction
+    try:
+        prediction = model.predict(input_df)
+        # Display result
+        st.success(f"🌱 Recommended Crop: **{prediction[0]}**")
+        # Optional: Show prediction confidence if available
+        if hasattr(model, 'predict_proba'):
+            proba = model.predict_proba(input_df)
+            confidence = max(proba[0]) * 100
+            st.info(f"Prediction Confidence: {confidence:.1f}%")
+    except Exception as e:
+        st.error(f"Error making prediction: {str(e)}")
+        st.info("Please check that your model file and input features are compatible")
+# Additional information
+st.markdown("---")
+st.subheader("About this System")
+st.write("This system uses machine learning to recommend the best crop based on soil nutrients and climate conditions.")

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+# Load and cache your dataset
+@st.cache_data
+def load_dataset():
+    # Replace 'your_dataset.csv' with your actual CSV file name
+    df = pd.read_csv('Crop.csv')  # Change this to your CSV file name
+    return df
+# Load your trained model (adjust path as needed)
+@st.cache_data
+def load_model():
+    # Replace with your actual model loading code
+    return pickle.load(open('crop_model.pkl', 'rb'))
+# Load the dataset
+df = load_dataset()
+model = load_model()
+# Streamlit app
+st.title("🌾 Crop Prediction System")
+st.write("Enter the farming conditions to predict the best crop")
+# Optional: Show dataset info
+with st.sidebar:
+    st.subheader("Dataset Information")
+    st.write(f"Total samples: {len(df)}")
+    st.write(f"Features: {list(df.columns)}")
+    if st.checkbox("Show sample data"):
+        st.dataframe(df.head())
+# Create input fields for features

crop_model.pkl

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+import pandas as pd
+import pickle
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+
+# Load data
+df = pd.read_csv('Crop.csv')
+
+# Prepare features and target
+X = df.drop('label', axis=1)  # Replace 'label' with your target column name
+y = df['label']  # Replace 'label' with your target column name
+
+# Encode target if it's text
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Save model
+pickle.dump(model, open('crop_model.pkl', 'wb'))
+
+# Save label encoder for predictions
+pickle.dump(le, open('label_encoder.pkl', 'wb'))
+
+print("Model created and saved successfully!")