Delete src

src/app.py

@@ -1,238 +0,0 @@
-import streamlit as st
-import joblib
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-st.sidebar.title("Crop & Fertilizer Recommendation System for Sustainable Agriculture")
-app_mode = st.sidebar.radio("Select Page",["HOME","CROP RECOMMENDATION","FERTILIZER RECOMMENDATION"])
-
-from PIL import Image
-img = Image.open("Diseases.png")
-
-st.image(img)
-
-if(app_mode=="HOME"):
-    st.markdown("<h1 style='text-align: center;'>Crop & Fertilizer Recommendation System for Sustainable Agriculture", unsafe_allow_html=True)
-
-elif(app_mode=="CROP RECOMMENDATION"):
-    st.header("Crop Recommendation System")
-    dtc = joblib.load("crop_prediction_model.pkl")
-    scaler = joblib.load("crop_scaler.pkl")
-    crop_dict = {1: 'rice', 2: 'maize', 3: 'chickpea', 4: 'kidneybeans', 5: 'pigeonpeas',
-                 6: 'mothbeans', 7: 'mungbean', 8: 'blackgram', 9: 'lentil', 10: 'pomegranate',
-                 11: 'banana', 12: 'mango', 13: 'grapes', 14: 'watermelon', 15: 'muskmelon',
-                 16: 'apple', 17: 'orange', 18: 'papaya', 19: 'coconut', 20: 'cotton',
-                 21: 'jute', 22: 'coffee'}
-    N = st.number_input("Nitrogen (N)", min_value=0.0,value=0.0)
-    P = st.number_input("Phosphorus (P)",min_value=0.0, value=0.0)
-    K = st.number_input("Potassium (K)", min_value=0.0, value=0.0)
-    temp = st.number_input("Temperature (°C)", value=0.0)
-    hum = st.number_input("Humidity (%)",min_value=0.0, max_value=100.0,value=0.0)
-    ph = st.number_input("pH Level", min_value=0.0, max_value=14.0, value=0.0)
-    rain = st.number_input("Rainfall (mm)",min_value=0.0, max_value=1200.00, value=0.0)
-    def crop_rec(N, P, K, temp, hum, ph, rain):
-        features = np.array([[N, P, K, temp, hum, ph, rain]])
-        transformed_features = scaler.transform(features)
-        prediction = dtc.predict(transformed_features)
-        crop = crop_dict.get(prediction[0], "Unknown Crop")
-        return f"{crop} is the recommended crop for the given conditions."
-    if st.button("Predict"):
-        col1, col2 = st.columns(2)
-        with col1:
-            labels = ['Nitrogen (N)', 'Phosphorus (P)', 'Potassium (K)']
-            sizes = [N, P, K]
-            colors = ['#ff9999', '#66b3ff', '#99ff99']
-
-            def custom_autopct(pct, allsizes):
-                total = sum(allsizes)
-                absolute = round(pct / 100. * total, 1)
-                closest_value = min(allsizes, key=lambda x: abs(x - absolute))
-                return f' {closest_value} units ({pct:.1f}%)'
-
-            fig, ax = plt.subplots(figsize=(6, 6), dpi=100)
-
-            ax.pie(sizes, labels=labels, colors=colors,
-                   autopct=lambda pct: custom_autopct(pct, sizes),
-                   startangle=90, wedgeprops=dict(width=0.3))
-            plt.title('NPK Composition', size=20, color='blue', fontweight='bold')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-            rfall = (rain / 1200) * 100
-            sizes = [rfall, 100 - rfall]  # Example values
-            colors = ['#66b3ff', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Rain Fall : {rain} mm')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-        with col2:
-            sizes = [hum, 100 - hum]  # Example values
-            colors = ['#4169E1', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Humidity : {hum} %')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-
-            sizes = [temp, 100-temp]  # Example values
-            colors = ['#FFA500', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Temperature : {temp}°C')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-        ph_values = np.linspace(0, 14, 100)
-
-        # Define a colormap for pH scale
-        colors = [
-            (1, 0, 0),  # Red (Strong Acid, pH 0)
-            (1, 0.5, 0),  # Orange
-            (1, 1, 0),  # Yellow
-            (0, 1, 0),  # Green (Neutral, pH 7)
-            (0, 0, 1),  # Blue (Weak Base)
-            (0.5, 0, 1)  # Purple (Strong Base, pH 14)
-        ]
-        cmap = mcolors.LinearSegmentedColormap.from_list("pH Scale", colors, N=100)
-
-        # Create a figure
-        fig, ax = plt.subplots(figsize=(10, 1))
-
-        # Create a gradient color bar
-        gradient = np.linspace(0, 1, 100).reshape(1, -1)
-        ax.imshow(gradient, aspect="auto", cmap=cmap, extent=[0, 14, 0, 1])
-
-        # Set x-axis labels for pH values
-        ax.set_xticks(np.arange(0, 15, 1))
-        ax.set_xticklabels(np.arange(0, 15, 1))
-        ax.set_yticks([])  # Hide y-axis
-
-        # Mark a specific pH point
-        ph_point = ph  # Change this to mark another pH value
-        ax.scatter(ph_point, 0.5, color="black", s=100, label=f'pH {ph_point}')
-        ax.vlines(ph_point, 0, 1, color="black", linestyle="dashed", linewidth=1)
-
-        # Add title and legend
-        ax.set_title("pH Scale Visualization", fontsize=12)
-        ax.legend(loc="upper right")
-
-        st.pyplot(fig)
-
-
-        result = crop_rec(N, P, K, temp, hum, ph, rain)
-        st.success(result)
-
-elif(app_mode=="FERTILIZER RECOMMENDATION"):
-    st.header("Fertilizer Recommendation System")
-    # Load the model and scaler
-    model = joblib.load('fertilizer_prediction_model.pkl')
-    sc = joblib.load('fertilizer_scaler.pkl')
-
-    # Fertilizer dictionary
-    fert_dict = {1: 'Urea', 2: 'DAP', 3: '14-35-14', 4: '28-28', 5: '17-17-17', 6: '20-20', 7: '10-26-26'}
-
-    # Soil type dictionary
-    soil_type_dict = {0: 'Black', 1: 'Clayey', 2: 'Loamy', 3: 'Red', 4:'Sandy'}
-
-    # Crop type dictionary
-    crop_type_dict = {
-    0: "Barley",
-    1: "Cotton",
-    2: "Ground Nuts",
-    3: "Maize",
-    4: "Millets",
-    5: "Oil seeds",
-    6: "Paddy",
-    7: "Pulses",
-    8: "Sugarcane",
-    9: "Tobacco",
-    10: "Wheat"
-}
-
-
-
-    # Function to recommend fertilizer
-    def recommend_fertilizer(Temparature, Humidity, Moisture, Soil_Type, Crop_Type, Nitrogen, Potassium, Phosphorous):
-        features = np.array([[Temparature, Humidity, Moisture, Soil_Type, Crop_Type, Nitrogen, Potassium, Phosphorous]])
-        transformed_features = sc.transform(features)
-        prediction = model.predict(transformed_features).reshape(1, -1)
-        fertilizer = [fert_dict[i] for i in prediction[0]]
-        return f"{fertilizer[0]} is the best fertilizer for the given conditions"
-
-
-    # Streamlit app
-    Temparature = st.number_input('Temperature', min_value=0.0, max_value=100.0, value=0.0)
-    Humidity = st.number_input('Humidity', min_value=0.0, max_value=100.0, value=0.0)
-    Moisture = st.number_input('Moisture', min_value=0.0, max_value=100.0, value=0.0)
-    Soil_Type = st.selectbox('Soil Type', options=list(soil_type_dict.values()))
-    Crop_Type = st.selectbox('Crop Type', options=list(crop_type_dict.values()))
-    Nitrogen = st.number_input('Nitrogen', min_value=0.0, max_value=100.0, value=0.0)
-    Potassium = st.number_input('Potassium', min_value=0.0, max_value=100.0, value=0.0)
-    Phosphorous = st.number_input('Phosphorous', min_value=0.0, max_value=100.0, value=0.0)
-
-    if st.button('Recommend Fertilizer'):
-        col1, col2 = st.columns(2)
-
-        with col1:
-            labels = ['Nitrogen (N)', 'Phosphorus (P)', 'Potassium (K)']
-            sizes = [Nitrogen, Potassium, Phosphorous]
-            colors = ['#ff9999', '#66b3ff', '#99ff99']
-
-            def custom_autopct(pct, allsizes):
-                total = sum(allsizes)
-                absolute = round(pct / 100. * total, 1)
-                closest_value = min(allsizes, key=lambda x: abs(x - absolute))
-                return f' {closest_value} units ({pct:.1f}%)'
-
-            fig, ax = plt.subplots(figsize=(6, 6), dpi=100)
-
-            ax.pie(sizes, labels=labels, colors=colors,
-                   autopct=lambda pct: custom_autopct(pct, sizes),
-                   startangle=90, wedgeprops=dict(width=0.3))
-            plt.title('NPK Composition', size=20, color='blue', fontweight='bold')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-            sizes = [Humidity, 100 - Humidity]  # Example values
-            colors = ['#4169E1', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Humidity : {Humidity} %')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-        with col2:
-            sizes = [Moisture, 100 - Moisture]  # Example values
-            colors = ['#4169E1', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Moisture : {Moisture} %')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-            sizes = [Temparature, 100 - Temparature]  # Example values
-            colors = ['#FFA500', '#D3D3D3']
-            fig, ax = plt.subplots()
-            ax.pie(sizes, colors=colors,
-                   startangle=90)  # Edge color for visibility
-            plt.title(f'Temperature : {Temparature}°C')
-            plt.axis('equal')
-            st.pyplot(fig)
-
-
-        # Convert soil and crop types to their numerical values
-        Soil_Type_num = list(soil_type_dict.keys())[list(soil_type_dict.values()).index(Soil_Type)]
-        Crop_Type_num = list(crop_type_dict.keys())[list(crop_type_dict.values()).index(Crop_Type)]
-
-        result = recommend_fertilizer(Temparature, Humidity, Moisture, Soil_Type_num, Crop_Type_num, Nitrogen,
-                                      Potassium, Phosphorous)
-        st.success(result)