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Crop_Recommendation.py

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+
+
+
+import requests
+import pandas as pd
+import numpy as np
+import pickle as pk
+import streamlit as st
+import time
+import Weather_app as wa
+
+
+
+
+import warnings
+warnings.filterwarnings("ignore")
+data = pd.read_csv("Crop_recommendation.csv")
+data_new = data.copy(deep = True)
+
+from sklearn.preprocessing import LabelEncoder
+
+le = LabelEncoder()
+data["Crop"] = le.fit_transform(data["label"])
+
+data.drop(columns = ["label"], inplace = True)
+
+@st.cache_resource
+def recmod():
+    return pk.load(open('crop_recommendation.pickle','rb'))
+recommendation_model = recmod()
+
+def crop_encoding(Predicted_value):
+    Predicted_value = (data_new[data.Crop == Predicted_value]["label"]).to_list()[0]
+    return Predicted_value
+
+def Crop_recommendation_function(crop_data_input):
+    crop_data_asarray = np.asarray(crop_data_input)
+    crop_data_reshaped = crop_data_asarray.reshape(1, -1)
+    crop_recommended = recommendation_model.predict(crop_data_reshaped)[0]  # Extract the result
+    crop = crop_encoding(crop_recommended)
+    return crop
+def Crop_recommendation_function2(input_data_speed):
+    # crop_data_asarray = np.array(input_data_speed).reshape(1, -1)
+
+# Make predictions using the loaded model
+    # predictions = loaded_data.predict(crop_data_asarray)[0]
+    
+
+    # modaa = pk.load(open('Soli_to_recommandation_model_Raghuu.pkl', 'rb'))
+    with open('Soli_to_recommandation_model_Raghuu.pkl', 'rb') as file:
+        loaded_model = pk.load(file)
+    # input_data = np.array(input_data_speed).reshape(1, -1)
+    mapp = {'Pomegranate': 10,
+            'Banana': 2,
+            'Mango': 6,
+            'Grapes': 4,
+            'Peach': 9,
+            'Black Berry': 3,
+            'Apple': 0,
+            'Orange': 7,
+            'Papaya': 8,
+            'Guava': 5,
+            'Apricot': 1}
+
+    criop =loaded_model.predict(input_data_speed)[0]
+    predicted_label = [key for key, value in mapp.items() if value == criop][0]
+
+    return predicted_label
+
+
+# def get_weather_details(city_name):
+#     base_url = "https://api.openweathermap.org/data/2.5/weather"
+#     params = {
+#         'q': city_name,
+#         'appid': "d73ec4f18aca81c32b1836a8ac2506e0"
+#     }
+
+#     try:
+#         response = requests.get(base_url, params=params)
+#         data = response.json()
+
+#         # Check if the request was successful
+#         if response.status_code == 200:
+#             # Extract weather details
+#             weather_details = {
+#                 'temperature': data['main']['temp'],
+#                 'humidity': data['main']['humidity']
+#             }
+#             return weather_details
+#         else:
+#             st.write("Error {}: {}".format(response.status_code, data['message']))
+#             return None
+#     except Exception as e:
+#         st.write("An error occurred:", e)
+#         return None
+
+def run_crop_recommendation():
+    st.title('Crop Recommendation')
+    background_image = 'https://c1.wallpaperflare.com/preview/436/828/940/clouds-summer-storm-clouds-form.jpg'
+    html_code = f"""
+        <style>
+            body {{
+                background-image: url('{background_image}');
+                background-size: cover;
+                background-position: center;
+                background-repeat: no-repeat;
+                height: 100vh;  /* Set the height of the background to fill the viewport */
+                margin: 0;  /* Remove default body margin */
+                display: flex;
+                flex-direction: column;
+                justify-content: center;
+                align-items: center;
+            }}
+            .stApp {{
+                background: none;  /* Remove Streamlit app background */
+            }}
+        </style>
+    """
+    tab1, tab2, tab3= st.tabs(['Based On Land And Water', 'Based On Fertilizers','Feedback'])
+    # st.title("Crop Recommendation System")
+    with tab1:
+        
+        try:
+            weather_details = wa.get_weather_details(wa.city_name)
+            # Load the trained model
+            @st.cache_resource
+            def soli():
+                return pk.load(open('Soli_to_recommandation_model_Simha.pkl', 'rb'))
+            
+                
+            loaded_model = soli()
+
+            # Streamlit UI
+            # st.title("Crop Recommendation System")
+
+            # Input features for prediction
+            col1, col2 = st.columns(2)
+            with col1:
+                Soil_EC = st.selectbox(("Soil_EC Siemens per meter (S/m)"),(1,2,3,4),3)
+            with col2:
+                Water_TDS = st.selectbox(("Water_TDS"),(1,2,3,4,5,6),5)
+            if weather_details:
+                Temprature = weather_details['temperature']
+                Humidity = weather_details['humidity']
+            col3,col4 = st.columns(2)   
+            with col3:
+
+                Ph = st.number_input("acidity or alkalinity",value=8.0, min_value= 0.0, max_value= 14.0, step=0.5)
+            with col4:
+                Rain_Fall = st.number_input("Rain_Fall in (mm) ", min_value=50.0,value=100.97,max_value=500.0)
+
+            # Reshape input data for prediction
+            input_data = np.array([Soil_EC, Water_TDS, Temprature, Humidity, Ph, Rain_Fall]).reshape(1, -1)
+
+            # Make prediction
+            mapp = {'Pomegranate': 10,
+                'Banana': 2,
+                'Mango': 6,
+                'Grapes': 4,
+                'Peach': 9,
+                'Black Berry': 3,
+                'Apple': 0,
+                'Orange': 7,
+                'Papaya': 8,
+                'Guava': 5,
+                'Apricot': 1}
+            crop_image_urls = {'Wheat': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRIp7ucodsB63giF1CvVjBtbHf14Px83ck2hcZRUJlMxA&s',
+                    'Rice': 'https://media.istockphoto.com/id/153737841/photo/rice.webp?b=1&s=170667a&w=0&k=20&c=SF6Ks-8AYpbPTnZlGwNCbCFUh-0m3R5sM2hl-C5r_Xc=',
+                    'Maize (Corn)': 'https://plus.unsplash.com/premium_photo-1667047165840-803e47970128?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8MXx8bWFpemV8ZW58MHx8MHx8fDA%3D',
+                    'Bajra (Pearl millet)': 'https://media.istockphoto.com/id/1400438871/photo/pear-millet-background.jpg?s=612x612&w=0&k=20&c=0GlBeceuX9Q_AZ0-CH57_A5s7_tD769N2f_jrbNcbrw=',
+                    'Jowar (Sorghum)': 'https://media.istockphoto.com/id/1262684430/photo/closeup-view-of-a-white-millet-jowar.jpg?s=612x612&w=0&k=20&c=HLyBy06EjbABKybUy1nIQTfxMLV1-s4xofGigOdd6dU=',
+                    'Barley': 'https://www.poshtik.in/cdn/shop/products/com1807851487263barley_Poshtik_c1712f8e-6b63-4231-9596-a49ce84f26ba.png?v=1626004318',
+                    'Gram (Chickpea)': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHtf9ivxD23Bp_-VOY4H2tCRMC0_znhzyAEt2jfzvUlskEZcv0',
+                    'Tur (Pigeonpea)': 'https://rukminim2.flixcart.com/image/850/1000/xif0q/plant-seed/f/l/n/25-pigeon-pea-for-planting-home-garden-farming-vegetable-kitchen-original-imaghphgmepkjqfz.jpeg?q=90',
+                    'Moong (Green Gram)': 'https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTyIa1Wq11MaHZ_cIdArPjZSR8cnr85STU83QsjKvkI9xNdVDjJ',
+                    'Urad (Black gram)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcRl-eFmBSLAHxB7U_b_SQNptQoQpi585JWgpqU0LH0jmvmrp9mESzQrL3ieox6ICl_-v7rzl38Pi7faf-4',
+                    'Masoor (Red lentil)': 'https://www.vegrecipesofindia.com/wp-content/uploads/2022/11/masoor-dal-red-lentils.jpg',
+                    'Groundnut (Peanut)': 'https://www.netmeds.com/images/cms/wysiwyg/blog/2019/10/Groundnut_big_2.jpg',
+                    'Sesamum (Sesame)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcThAjpal-k0urS19A2NEoVW35yqF9ljlvx1d-amDokoIiHZ9-RGyUsDaiVcr7SdfwsFjP-I6U1_VYeiEc0',
+                    'Castor seed': 'https://5.imimg.com/data5/QV/VN/MY-3966004/caster-seeds.jpg',
+                    'Sunflower': 'https://t0.gstatic.com/licensed-image?q=tbn:ANd9GcRuCcoGrqSVqOzxFU9rHPsWKxaHpm7i_srXQPMHaVfrrDmz4eXc5PGWpQFfpAr8qaH2',
+                    'Safflower': 'https://upload.wikimedia.org/wikipedia/commons/7/7f/Safflower.jpg',
+                    'Sugarcane': 'https://www.saveur.com/uploads/2022/03/05/sugarcane-linda-xiao.jpg?auto=webp',
+                    'Cotton (lint)': 'https://img2.tradewheel.com/uploads/images/products/6/0/0048590001615360690-cotton-lint.jpeg.webp',
+                    'Jute': 'https://rukminim2.flixcart.com/image/850/1000/kuk4u4w0/rope/d/k/f/2-jute-cord-for-craft-project-natural-jute-rope-jute-thread-original-imag7nrjbkrmgbpm.jpeg?q=20',
+                    'Potato': 'https://cdn.mos.cms.futurecdn.net/iC7HBvohbJqExqvbKcV3pP.jpg',
+                    'Onion': 'https://familyneeds.co.in/cdn/shop/products/2_445fc9bd-1bab-4bfb-8d5d-70b692745567_600x600.jpg?v=1600812246',
+                    'Tomato': 'https://upload.wikimedia.org/wikipedia/commons/thumb/8/89/Tomato_je.jpg/1200px-Tomato_je.jpg',
+                    'Banana': 'https://fruitboxco.com/cdn/shop/products/asset_2_grande.jpg?v=1571839043',
+                    'Coconut': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_rZgOJry6Twt8urk4C1FTo6d6tEDyiIw39w&usqp=CAU',
+                    'Mango': "https://i.pinimg.com/474x/70/bd/5f/70bd5f8fd50d30bfcab3ac0f27ff4202.jpg",
+                    'Orange': "https://images.unsplash.com/photo-1611080626919-7cf5a9dbab5b?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8b3Jhbmdlc3xlbnwwfHwwfHx8MA%3D%3D",
+                    "Pomegranate": "https://thumbs.dreamstime.com/b/juicy-pomegranate-its-half-leaves-16537522.jpg",
+                    "Banana": "https://media.istockphoto.com/id/173242750/photo/banana-bunch.jpg?s=612x612&w=0&k=20&c=MAc8AXVz5KxwWeEmh75WwH6j_HouRczBFAhulLAtRUU=",
+                    "Grapes": "https://cf.ltkcdn.net/wine/images/std/165373-800x532r1-grapes.jpg",
+                    "Peach": "https://www.shutterstock.com/image-photo/peaches-isolated-ripe-peach-half-260nw-2189388721.jpg",
+                    "Black Berry": "https://example.com/blackberry.jpg",
+                    "Apple": "https://images.unsplash.com/photo-1560806887-1e4cd0b6cbd6?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxleHBsb3JlLWZlZWR8Nnx8fGVufDB8fHx8fA%3D%3D",
+                    "Papaya": "https://media.istockphoto.com/id/864053288/photo/whole-and-half-of-ripe-papaya-fruit-with-seeds-isolated-on-white-background.jpg?s=612x612&w=0&k=20&c=hJ5DpNTt0oKjZMIHYV6gUHTntB2zIs_78dPKiuDUXgE=",
+                    "Guava": "https://media.istockphoto.com/id/1224636159/photo/closeup-of-a-red-guava-cut-in-half-in-the-background-several-guavas-and-green-leaf.jpg?s=612x612&w=0&k=20&c=KJ9YilkRRuFh0bnw64Ol0IZDfoQF7UIxyC6dRVIjaoA=",
+                    "Apricot": "https://www.shutterstock.com/image-photo/apricot-isolated-apricots-on-white-600nw-1963600408.jpg",
+                    "Kidneybeans": "https://www.healthifyme.com/blog/wp-content/uploads/2022/01/807716893sst1641271427-scaled.jpg",
+                    "Chickpea": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHtf9ivxD23Bp_-VOY4H2tCRMC0_znhzyAEt2jfzvUlskEZcv0"}
+                    
+            def get_crop_image_url(crop_name):
+                    return crop_image_urls.get(crop_name, None)
+
+            if st.button("Submit", key=32):
+                prediction = loaded_model.predict(input_data)
+                predicted_label = [key for key, value in mapp.items() if value == prediction][0]
+                st.success(f"The predicted fruit is: {predicted_label}")
+                
+                crop_image_url = get_crop_image_url(predicted_label.capitalize())
+                
+                if crop_image_url is None:
+                    st.warning("No image found for the predicted fruit.")
+                else:
+                    try:
+                        st.markdown(f'<img src="{crop_image_url}" alt="Image for {predicted_label}" style="width:300px; height:300px;">', unsafe_allow_html=True)
+                    except Exception as e:
+                        st.warning(f"Error displaying image: {e}")
+
+                        
+                
+        except AttributeError:
+            st.warning("Please Select the city")
+
+        # col1, col2 = st.columns(2)
+        # with col1:
+        #     Soil_EC = st.selectbox(('Soil conductivity'),(1,2,3,4),2,key = 3)
+        # with col2:
+        #     Water_TDS = st.selectbox(('Water solvents'),(1,2,3,4,5,6),3,key = 4)
+        # col3,col4 = st.columns([3,1])
+        # with col3:
+        #     Ph = st.slider("Enter ph",1,14,(1,7))
+        # with col4:
+        #     Rain_Fall = st.number_input("Enter Annual Rainfall in mm", min_value=10.0, max_value=2000.0)
+        # weather_details = wa.get_weather_details(wa.city_name)
+        
+        
+
+        # if weather_details:
+        #     Temperature = (weather_details['temperature'])
+        #     Humidity =(weather_details['humidity'])
+        # st.write(Temperature)
+        # st.write(Humidity)
+        # input_data = [Soil_EC,Water_TDS,Temperature,Humidity,Ph,Rain_Fall]
+        # if st.button('Submit',key = 1):
+        #         input_data = np.asarray(input_data).reshape(1, -1)
+
+        #         crop_pred = Crop_recommendation_function2(input_data)
+                
+        #         progress = st.progress(0)
+        #         for i in range(100):
+        #             time.sleep(0.005)
+        #             progress.progress(i+1)
+        #         st.subheader(f"Crop Recommendation: {crop_pred.capitalize()}")
+
+                # crop_image_url = get_crop_image_url(crop_pred)
+                # try:
+                #     st.image(crop_image_url, caption=f"Image for {crop_prediction.capitalize()}", use_column_width=True)
+                # except:
+                #     pass
+
+    
+
+    with tab2:
+
+        st.markdown(html_code, unsafe_allow_html=True)
+
+        col1, col2 = st.columns(2)
+        nitrogen = col1.selectbox('Enter Nitrogen (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140),key = 0)
+        phosphorus = col2.selectbox('Enter Phosphorus (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 120, 125, 130, 135, 140, 145),key = 13)
+        potassium = col1.selectbox('Enter Potassium (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 185, 190, 195, 200, 205),key = 2)
+        
+        # Get weather details
+        # city_name = st.text_input("Enter City Name for Weather Details")
+        weather_details = wa.get_weather_details(wa.city_name)
+        ph = col2.slider('Enter pH value',value=6.502985,min_value=0.0,max_value=14.0,step=0.5)
+        rainfall = col1.number_input('Enter Rainfall (e.g., in mm)',value=202.935536,min_value=25.0,max_value=1000.0,step=5.0)
+
+        
+        if weather_details:
+            temperature = weather_details['temperature']
+            humidity = weather_details['humidity']
+            
+            
+            crop_input = ''
+
+            def get_crop_image_url(crop_name):
+        # You need to replace the following with the actual URLs or paths of your crop images
+                crop_image_urls = {'Wheat': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRIp7ucodsB63giF1CvVjBtbHf14Px83ck2hcZRUJlMxA&s',
+                            'Rice': 'https://media.istockphoto.com/id/153737841/photo/rice.webp?b=1&s=170667a&w=0&k=20&c=SF6Ks-8AYpbPTnZlGwNCbCFUh-0m3R5sM2hl-C5r_Xc=',
+                            'Maize (Corn)': 'https://plus.unsplash.com/premium_photo-1667047165840-803e47970128?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8MXx8bWFpemV8ZW58MHx8MHx8fDA%3D',
+                            'Bajra (Pearl millet)': 'https://media.istockphoto.com/id/1400438871/photo/pear-millet-background.jpg?s=612x612&w=0&k=20&c=0GlBeceuX9Q_AZ0-CH57_A5s7_tD769N2f_jrbNcbrw=',
+                            'Jowar (Sorghum)': 'https://media.istockphoto.com/id/1262684430/photo/closeup-view-of-a-white-millet-jowar.jpg?s=612x612&w=0&k=20&c=HLyBy06EjbABKybUy1nIQTfxMLV1-s4xofGigOdd6dU=',
+                            'Barley': 'https://www.poshtik.in/cdn/shop/products/com1807851487263barley_Poshtik_c1712f8e-6b63-4231-9596-a49ce84f26ba.png?v=1626004318',
+                            'Gram (Chickpea)': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHtf9ivxD23Bp_-VOY4H2tCRMC0_znhzyAEt2jfzvUlskEZcv0',
+                            'Tur (Pigeonpea)': 'https://rukminim2.flixcart.com/image/850/1000/xif0q/plant-seed/f/l/n/25-pigeon-pea-for-planting-home-garden-farming-vegetable-kitchen-original-imaghphgmepkjqfz.jpeg?q=90',
+                            'Moong (Green Gram)': 'https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTyIa1Wq11MaHZ_cIdArPjZSR8cnr85STU83QsjKvkI9xNdVDjJ',
+                            'Urad (Black gram)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcRl-eFmBSLAHxB7U_b_SQNptQoQpi585JWgpqU0LH0jmvmrp9mESzQrL3ieox6ICl_-v7rzl38Pi7faf-4',
+                            'Masoor (Red lentil)': 'https://www.vegrecipesofindia.com/wp-content/uploads/2022/11/masoor-dal-red-lentils.jpg',
+                            'Groundnut (Peanut)': 'https://www.netmeds.com/images/cms/wysiwyg/blog/2019/10/Groundnut_big_2.jpg',
+                            'Sesamum (Sesame)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcThAjpal-k0urS19A2NEoVW35yqF9ljlvx1d-amDokoIiHZ9-RGyUsDaiVcr7SdfwsFjP-I6U1_VYeiEc0',
+                            'Castor seed': 'https://5.imimg.com/data5/QV/VN/MY-3966004/caster-seeds.jpg',
+                            'Sunflower': 'https://t0.gstatic.com/licensed-image?q=tbn:ANd9GcRuCcoGrqSVqOzxFU9rHPsWKxaHpm7i_srXQPMHaVfrrDmz4eXc5PGWpQFfpAr8qaH2',
+                            'Safflower': 'https://upload.wikimedia.org/wikipedia/commons/7/7f/Safflower.jpg',
+                            'Sugarcane': 'https://www.saveur.com/uploads/2022/03/05/sugarcane-linda-xiao.jpg?auto=webp',
+                            'Cotton (lint)': 'https://img2.tradewheel.com/uploads/images/products/6/0/0048590001615360690-cotton-lint.jpeg.webp',
+                            'Jute': 'https://rukminim2.flixcart.com/image/850/1000/kuk4u4w0/rope/d/k/f/2-jute-cord-for-craft-project-natural-jute-rope-jute-thread-original-imag7nrjbkrmgbpm.jpeg?q=20',
+                            'Potato': 'https://cdn.mos.cms.futurecdn.net/iC7HBvohbJqExqvbKcV3pP.jpg',
+                            'Onion': 'https://familyneeds.co.in/cdn/shop/products/2_445fc9bd-1bab-4bfb-8d5d-70b692745567_600x600.jpg?v=1600812246',
+                            'Tomato': 'https://upload.wikimedia.org/wikipedia/commons/thumb/8/89/Tomato_je.jpg/1200px-Tomato_je.jpg',
+                            'Banana': 'https://fruitboxco.com/cdn/shop/products/asset_2_grande.jpg?v=1571839043',
+                            'Coconut': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_rZgOJry6Twt8urk4C1FTo6d6tEDyiIw39w&usqp=CAU',
+                            'Mango': "https://i.pinimg.com/474x/70/bd/5f/70bd5f8fd50d30bfcab3ac0f27ff4202.jpg",
+                            'Orange': "https://images.unsplash.com/photo-1611080626919-7cf5a9dbab5b?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8b3Jhbmdlc3xlbnwwfHwwfHx8MA%3D%3D",
+                            "Kidneybeans": "https://www.healthifyme.com/blog/wp-content/uploads/2022/01/807716893sst1641271427-scaled.jpg",
+                            "Chickpea": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHtf9ivxD23Bp_-VOY4H2tCRMC0_znhzyAEt2jfzvUlskEZcv0",
+                            "Grapes": "https://rukminim2.flixcart.com/image/850/1000/kt0enww0/plant-seed/h/h/n/25-dg-214-paudha-original-imag6fgvre6bmd5y.jpeg?q=90&crop=false",
+                            "Coffee": "https://www.agrifarming.in/wp-content/uploads/2017/06/Coffee-Growing.-1.jpg"}
+                if crop_name not in crop_image_urls.keys():
+                    return None
+                else:
+                    return crop_image_urls[crop_name]
+
+            if st.button('Submit'):
+                crop_input = [nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall]
+                crop_prediction = Crop_recommendation_function(crop_input)
+                
+                progress = st.progress(0)
+                for i in range(100):
+                    time.sleep(0.005)
+                    progress.progress(i+1)
+                st.subheader(f"Crop Recommendation: {crop_prediction.capitalize()}")
+
+                crop_image_url = get_crop_image_url(crop_prediction.capitalize())
+                try:
+                    st.image(crop_image_url, caption=f"Image for {crop_prediction.capitalize()}", use_column_width=True)
+                except:
+                    pass
+
+    with tab3:
+        df = pd.read_csv('Crop_recommendation.csv')
+        st.write('Current Dataset',df)
+        col1, col2 = st.columns(2)
+        nitrogen = col1.selectbox('Enter Nitrogen (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140),key = 20)
+        phosphorus = col2.selectbox('Enter Phosphorus (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 120, 125, 130, 135, 140, 145),key = 143)
+        potassium = col1.selectbox('Enter Potassium (e.g., in kg/ha)',(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 185, 190, 195, 200, 205),key = 21)
+        temperature = col2.number_input('Enter temprature',max_value=45.0,min_value=8.0,value=32.0,step = 2.0,key = 232)
+        humidity = col1.number_input('Enter Humidity',value=80.47,max_value=99.98,min_value=14.25,step = 2.0,key = 103)
+        ph = col2.slider('Enter pH value',value=6.502985,min_value=0.0,max_value=14.0,step=0.5,key = 104)
+        rainfall = col1.number_input('Enter Rainfall (e.g., in mm)',value=202.935536,min_value=25.0,max_value=1000.0,step=5.0,key = 105)
+        label = col1.selectbox('Enter the crop',('rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',
+                                            'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',
+                                            'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',
+                                            'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'),key =106)
+        
+        if st.button('submit'):
+            new_row = {'N':nitrogen, 'P':phosphorus, 'K':potassium, 'temperature':temperature, 'humidity':humidity, 'ph':ph, 'rainfall':rainfall, 'label':label}
+            df = df.append(new_row,ignore_index= True)
+            df.to_csv('Crop_recommendation.csv')
+            st.success("Thanks for the feedback")
+            st.write("Updated Dataset",df)
+
+
+
+
+
+        
+
+if __name__ == "__main__":
+    run_crop_recommendation()

Crop_yield.py

@@ -0,0 +1,270 @@
+
+import pandas as pd
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle as pk
+import time
+import warnings
+import requests
+import requests
+from PIL import Image, ImageDraw, ImageFont
+from geopy.geocoders import Nominatim
+import geocoder
+warnings.filterwarnings('ignore')
+
+data = pd.read_csv('crop_yield.csv')
+
+## only for encoding purpose
+data_new = data.copy(deep = True)
+
+# Apply transformation to string values in the 'Crop', 'Season', and 'State' columns
+columns_to_transform = ['Crop', 'Season', 'State']
+
+for column in columns_to_transform:
+    data_new[column] = data_new[column].apply(
+        lambda x: x.lower().replace(" ", "").replace("/", "").replace("(", "").replace(")", "") if isinstance(x, str) else x)
+
+columns = ['Crop', 'Season', 'State']
+from sklearn.preprocessing import LabelEncoder
+encoder = LabelEncoder()
+for col in columns:
+    data[col] = encoder.fit_transform(data[col])
+
+data.drop(columns = ["Crop_Year"], inplace = True)
+# @st.cache_data
+def get_user_ip():
+    try:
+        response = requests.get('https://api64.ipify.org?format=json')
+        data = response.json()
+        return data.get('ip')
+    except Exception as e:
+        print(f"Error getting user IP: {e}")
+        return None
+
+def apiip_net_request():
+    user_ip = get_user_ip()
+    if user_ip:
+        access_key = '630523ff-348e-490e-b851-ab295b5ff3fd'
+        url = f'https://apiip.net/api/check?ip={user_ip}&accessKey={access_key}'
+        
+        try:
+            response = requests.get(url)
+            result = response.json()
+            return result.get('regionName')
+        except Exception as e:
+            print(f"Error making API request: {e}")
+    else:
+        print("Unable to retrieve user IP.")
+
+
+IP = get_user_ip()
+state_name = apiip_net_request()
+
+
+# Automatic location detection using st.location
+def get_weather(city):
+    # Using the OpenWeatherMap API to get weather information based on city name
+    openweathermap_api_key = "d73ec4f18aca81c32b1836a8ac2506e0"
+    openweathermap_url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={openweathermap_api_key}"
+    
+    response = requests.get(openweathermap_url)
+    data = response.json()
+    
+    return data.get("weather")[0].get("main")
+
+
+from datetime import datetime
+
+def get_season(month):
+    # Mapping of months to seasons
+    month_to_season = {
+        1: 'Winter', 2: 'Winter', 3: 'Spring',
+        4: 'Spring', 5: 'Spring', 6: 'Summer',
+        7: 'Summer', 8: 'Summer', 9: 'Autumn',
+        10: 'Autumn', 11: 'Autumn', 12: 'Winter'
+    }
+
+    # Get the season based on the month
+    season = month_to_season.get(month, 'Invalid Month')
+    
+    return season
+
+# Example: Get the season for a specific month
+current_month = datetime.now().month
+current_season = get_season(current_month)
+
+# Example: Get the season for a specific month
+current_month = datetime.now().month
+current_season = get_season(current_month)
+
+
+
+
+def encoding(input_data):
+    try:
+        input_data[0] = (data[data_new.Crop == input_data[0].lower().replace(" ", "").replace(" ", "").replace(" ", "").replace("/", "").replace("(", "").replace(")", "")]["Crop"]).to_list()[0]
+        input_data[1] = (data[data_new.Season== input_data[1].lower().replace(" ", "").replace(" ", "").replace(" ", "").replace("/", "").replace("/", "").replace("(", "").replace(")", "")]["Season"]).to_list()[0]
+        input_data[2] = (data[data_new.State== input_data[2].lower().replace(" ", "").replace(" ", "").replace(" ", "").replace("/", "").replace("(", "").replace(")", "")]["State"]).to_list()[0]
+        return input_data
+    except:
+        return None
+
+
+crop_yield_model = pk.load(open('crop_yield_model.pkl','rb'))
+
+def crop_yield_prediction(input_data):
+    input_data_asarray = np.asarray(input_data)
+    input_data_reshaped = input_data_asarray.reshape(1,-1)
+    prediction = crop_yield_model.predict(input_data_reshaped)
+    return prediction
+
+def Crop_yield():
+    tab1, tab2,tab3 = st.tabs(["Crop Labels", "Crop Yield","Feedback"])
+    with tab1:
+        def display_images_in_columns(dictionary, num_columns=2):
+            num_images = len(dictionary)
+            num_rows = -(-num_images // num_columns)  # Ceiling division to calculate rows
+
+            for i in range(num_rows):
+                cols = st.columns(num_columns)
+                for j in range(num_columns):
+                    index = i * num_columns + j
+                    if index < num_images:
+                        label, url = list(dictionary.items())[index]
+                        cols[j].image(url, caption=label, use_column_width=True)
+
+        # Example dictionary (replace this with your actual dictionary)
+        image_dictionary = {'Wheat': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRIp7ucodsB63giF1CvVjBtbHf14Px83ck2hcZRUJlMxA&s',
+                            'Rice': 'https://media.istockphoto.com/id/153737841/photo/rice.webp?b=1&s=170667a&w=0&k=20&c=SF6Ks-8AYpbPTnZlGwNCbCFUh-0m3R5sM2hl-C5r_Xc=',
+                            'Maize (Corn)': 'https://plus.unsplash.com/premium_photo-1667047165840-803e47970128?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8MXx8bWFpemV8ZW58MHx8MHx8fDA%3D',
+                            'Bajra (Pearl millet)': 'https://media.istockphoto.com/id/1400438871/photo/pear-millet-background.jpg?s=612x612&w=0&k=20&c=0GlBeceuX9Q_AZ0-CH57_A5s7_tD769N2f_jrbNcbrw=',
+                            'Jowar (Sorghum)': 'https://media.istockphoto.com/id/1262684430/photo/closeup-view-of-a-white-millet-jowar.jpg?s=612x612&w=0&k=20&c=HLyBy06EjbABKybUy1nIQTfxMLV1-s4xofGigOdd6dU=',
+                            'Barley': 'https://www.poshtik.in/cdn/shop/products/com1807851487263barley_Poshtik_c1712f8e-6b63-4231-9596-a49ce84f26ba.png?v=1626004318',
+                            'Gram (Chickpea)': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHtf9ivxD23Bp_-VOY4H2tCRMC0_znhzyAEt2jfzvUlskEZcv0',
+                            'Tur (Pigeonpea)': 'https://rukminim2.flixcart.com/image/850/1000/xif0q/plant-seed/f/l/n/25-pigeon-pea-for-planting-home-garden-farming-vegetable-kitchen-original-imaghphgmepkjqfz.jpeg?q=90',
+                            'Moong (Green Gram)': 'https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTyIa1Wq11MaHZ_cIdArPjZSR8cnr85STU83QsjKvkI9xNdVDjJ',
+                            'Urad (Black gram)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcRl-eFmBSLAHxB7U_b_SQNptQoQpi585JWgpqU0LH0jmvmrp9mESzQrL3ieox6ICl_-v7rzl38Pi7faf-4',
+                            'Masoor (Red lentil)': 'https://www.vegrecipesofindia.com/wp-content/uploads/2022/11/masoor-dal-red-lentils.jpg',
+                            'Groundnut (Peanut)': 'https://www.netmeds.com/images/cms/wysiwyg/blog/2019/10/Groundnut_big_2.jpg',
+                            'Sesamum (Sesame)': 'https://encrypted-tbn0.gstatic.com/licensed-image?q=tbn:ANd9GcThAjpal-k0urS19A2NEoVW35yqF9ljlvx1d-amDokoIiHZ9-RGyUsDaiVcr7SdfwsFjP-I6U1_VYeiEc0',
+                            'Castor seed': 'https://5.imimg.com/data5/QV/VN/MY-3966004/caster-seeds.jpg',
+                            'Sunflower': 'https://t0.gstatic.com/licensed-image?q=tbn:ANd9GcRuCcoGrqSVqOzxFU9rHPsWKxaHpm7i_srXQPMHaVfrrDmz4eXc5PGWpQFfpAr8qaH2',
+                            'Safflower': 'https://upload.wikimedia.org/wikipedia/commons/7/7f/Safflower.jpg',
+                            'Sugarcane': 'https://www.saveur.com/uploads/2022/03/05/sugarcane-linda-xiao.jpg?auto=webp',
+                            'Cotton (lint)': 'https://img2.tradewheel.com/uploads/images/products/6/0/0048590001615360690-cotton-lint.jpeg.webp',
+                            'Jute': 'https://rukminim2.flixcart.com/image/850/1000/kuk4u4w0/rope/d/k/f/2-jute-cord-for-craft-project-natural-jute-rope-jute-thread-original-imag7nrjbkrmgbpm.jpeg?q=20',
+                            'Potato': 'https://cdn.mos.cms.futurecdn.net/iC7HBvohbJqExqvbKcV3pP.jpg',
+                            'Onion': 'https://familyneeds.co.in/cdn/shop/products/2_445fc9bd-1bab-4bfb-8d5d-70b692745567_600x600.jpg?v=1600812246',
+                            'Tomato': 'https://upload.wikimedia.org/wikipedia/commons/thumb/8/89/Tomato_je.jpg/1200px-Tomato_je.jpg',
+                            'Banana': 'https://fruitboxco.com/cdn/shop/products/asset_2_grande.jpg?v=1571839043',
+                            'Coconut': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_rZgOJry6Twt8urk4C1FTo6d6tEDyiIw39w&usqp=CAU',
+                            'Mango': "https://i.pinimg.com/474x/70/bd/5f/70bd5f8fd50d30bfcab3ac0f27ff4202.jpg",
+                            'Orange': "https://images.unsplash.com/photo-1611080626919-7cf5a9dbab5b?q=80&w=1000&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8b3Jhbmdlc3xlbnwwfHwwfHx8MA%3D%3D"}
+
+
+        display_images_in_columns(image_dictionary)
+    with tab2:
+        st.title('Crop Yield Prediction')
+        background_image = ' https://us.123rf.com/450wm/vittuperkele/vittuperkele1804/vittuperkele180400186/100517230-growing-green-crop-fields-at-late-evening-blue-sky-with-clouds-in-countryside-fresh-air-clean.jpg?ver=6'
+        html_code = f"""
+            <style>
+                body {{
+                    background-image: url('{background_image}');
+                    background-size: cover;
+                    background-position: center;
+                    background-repeat: no-repeat;
+                    height: 100vh;  /* Set the height of the background to fill the viewport */
+                    margin: 0;  /* Remove default body margin */
+                    display: flex;
+                    flex-direction: column;
+                    justify-content: center;
+                    align-items: center;
+                }}
+                .stApp {{
+                    background: none;  /* Remove Streamlit app background */
+                }}
+            </style>
+        """
+        st.markdown(html_code, unsafe_allow_html=True)
+        
+        col1, col2 = st.columns(2)
+        # c1,c2,c3 = st.columns([3,0.5,0.5])
+        crop = col1.selectbox(':black[Enter crop type]',('Arecanut', 'Arhar/Tur', 'Castor seed', 'Coconut ', 'Cotton(lint)',
+        'Dry chillies', 'Gram', 'Jute', 'Linseed', 'Maize', 'Mesta',
+        'Niger seed', 'Onion', 'Other  Rabi pulses', 'Potato',
+        'Rapeseed &Mustard', 'Rice', 'Sesamum', 'Small millets',
+        'Sugarcane', 'Sweet potato', 'Tapioca', 'Tobacco', 'Turmeric',
+        'Wheat', 'Bajra', 'Black pepper', 'Cardamom', 'Coriander',
+        'Garlic', 'Ginger', 'Groundnut', 'Horse-gram', 'Jowar', 'Ragi',
+        'Cashewnut', 'Banana', 'Soyabean', 'Barley', 'Khesari', 'Masoor',
+        'Moong(Green Gram)', 'Other Kharif pulses', 'Safflower',
+        'Sannhamp', 'Sunflower', 'Urad', 'Peas & beans (Pulses)',
+        'other oilseeds', 'Other Cereals', 'Cowpea(Lobia)',
+        'Oilseeds total', 'Guar seed', 'Other Summer Pulses', 'Moth'))
+        
+        season = current_season
+        state = 'Karnataka'
+        try:
+                area = col2.number_input("Enter area (e.g., in ha)", min_value=1.0, max_value=10000000.0, value=6637.0, step=1.0, format="%f", help="Enter the area in Hacter")
+                minallowed = area * 0.03
+                maxallowed = area * 1.5
+                
+                annual_rainfall = col2.number_input('Enter annual rainfall (e.g., in mm)',value=2051.4,min_value=200.0,max_value=2500.0,step=100.0)
+                fertilizer = col1.number_input('Enter fertilizer (e.g., in g)',value=631643.29,min_value=1.0,max_value=10000000.0,step=10.0)
+                pesticide = col2.number_input('Enter pesticide (e.g., in g)',value=2057.47,min_value=1.0,max_value=10000000.0,step=10.0)
+                # st.write(state)
+        # st.write(IP)
+        except:
+            st.warning("Max area is more than limits")
+        prediction = ''
+        production = col1.number_input('Enter production (e.g., in kg)', value=minallowed, min_value=minallowed, max_value=maxallowed, step=10.0)
+        if st.button('Submit'):
+            encode = encoding([crop, season, state, area, production, annual_rainfall, fertilizer, pesticide])
+            try:
+                prediction = crop_yield_prediction(list(encode))
+                progress = st.progress(0)
+                for i in range(100):
+                    time.sleep(0.005)
+                    progress.progress(i+1)
+                st.subheader(f"Crop Yied: {round(prediction[0],3)} kg/ha")
+            except:
+                st.error("Invalid Inputs")
+
+    with tab3:
+        df = pd.read_csv('crop_yield.csv')
+        st.write('Current Dataset',df)
+        col1,col2 = st.columns(2)
+        crop = col1.selectbox(':black[Enter crop type]',('Arecanut', 'Arhar/Tur', 'Castor seed', 'Coconut ', 'Cotton(lint)',
+                                                        'Dry chillies', 'Gram', 'Jute', 'Linseed', 'Maize', 'Mesta',
+                                                        'Niger seed', 'Onion', 'Other  Rabi pulses', 'Potato',
+                                                        'Rapeseed &Mustard', 'Rice', 'Sesamum', 'Small millets',
+                                                        'Sugarcane', 'Sweet potato', 'Tapioca', 'Tobacco', 'Turmeric',
+                                                        'Wheat', 'Bajra', 'Black pepper', 'Cardamom', 'Coriander',
+                                                        'Garlic', 'Ginger', 'Groundnut', 'Horse-gram', 'Jowar', 'Ragi',
+                                                        'Cashewnut', 'Banana', 'Soyabean', 'Barley', 'Khesari', 'Masoor',
+                                                        'Moong(Green Gram)', 'Other Kharif pulses', 'Safflower',
+                                                        'Sannhamp', 'Sunflower', 'Urad', 'Peas & beans (Pulses)',
+                                                        'other oilseeds', 'Other Cereals', 'Cowpea(Lobia)',
+                                                        'Oilseeds total', 'Guar seed', 'Other Summer Pulses', 'Moth'),key = 104)
+        area = col2.number_input("Enter area (e.g., in ha)", min_value=1.0, max_value=10000000.0, value=6637.0, step=1.0, format="%f", help="Enter the area in Hacter",key = 105)
+        minallowed = area * 0.03
+        maxallowed = area * 1.5
+        production = col1.number_input('Enter production (e.g., in kg)', value=minallowed, min_value=minallowed, max_value=maxallowed, step=10.0,key = 106)
+        annual_rainfall = col2.number_input('Enter annual rainfall (e.g., in mm)',value=2051.4,min_value=200.0,max_value=2500.0,step=100.0,key = 107)
+        fertilizer = col1.number_input('Enter fertilizer (e.g., in g)',value=631643.29,min_value=1.0,max_value=10000000.0,step=10.0,key = 108)
+        pesticide = col2.number_input('Enter pesticide (e.g., in g)',value=2057.47,min_value=1.0,max_value=10000000.0,step=10.0,key = 109)
+        Yield = col1.number_input('Enter the yield(kg per hectare)',value = 79.9,max_value=21105.0,min_value=0.0,step = 5.0,key = 101)
+        
+        if st.button('submit',key = 102):
+            new_row = {'Crop':crop,'Area':area, 'Production':production,'Annual_Rainfall':annual_rainfall, 'Fertilizer':fertilizer, 'Pesticide':pesticide, 'Yield':Yield}
+            df = df.append(new_row,ignore_index= True)
+            df.to_csv('crop_yield.csv')
+            st.success("Thanks for the feedback")
+            st.write("Updated Dataset",df)       
+
+
+
+if __name__ == '__main__':
+    Crop_yield()

Lasso Regression.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b07b1d5479f558ac9a6494f837b04309561a988ab3bb93c8b39f37e5b9bb4099
+size 129

Mail.py

@@ -0,0 +1,19 @@
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+
+def send_confirmation_email(recipient_email, username):
+    sender_email = "agritechserviceorg@gmail.com"
+    password = "yvoq mzmv qmjr ahoi"
+    subject = "Account Confirmation"
+    body = f"Hello {username},\n\nThank you for creating an account!\n\nBest regards,\nAgriTech Team"
+    message = MIMEMultipart()
+    message["From"] = sender_email
+    message["To"] = recipient_email
+    message["Subject"] = subject
+    message.attach(MIMEText(body, "plain"))
+    with smtplib.SMTP("smtp.gmail.com", 587) as server:
+        server.starttls()
+        server.login(sender_email, password)
+        server.sendmail(sender_email, recipient_email, message.as_string())
+

crop_recommendation.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03352e857a8d9b32ccbef1407583cc24d27304e2cbc9b0d487f97a3bdc4b81f5
+size 3907534

crop_yield_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8717df38ceca417a37d28cada5870349a1414d96852a410f8136973a36f48d4d
+size 133829713

feedbacko.csv

@@ -0,0 +1,2 @@
+timestamp,briefit,feedbacko
+2024-01-15 14:15:13,5,super

feedbacko.py

@@ -0,0 +1,28 @@
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+def run_feedback():
+    df = pd.read_csv('feedbacko.csv')
+    st.title('Feedback Form')
+    brief = st.slider('Rate your experience ⭐️', min_value=1, max_value=5, value=3)
+    feedback_text = st.text_area('Provide additional comments or feedback:')
+
+    if st.button('Submit Feedback'):
+        # Check if 'feedbacko' is empty and replace it with None
+        feedback_text = None if not feedback_text else feedback_text
+
+        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        new_data = {
+            'timestamp': timestamp,
+            'briefit': brief,
+            'feedbacko': feedback_text
+        }
+        new_df = pd.DataFrame([new_data])
+        combined_df = pd.concat([df, new_df], ignore_index=True, axis=0)
+        combined_df = combined_df.drop_duplicates(subset=['feedbacko'])
+        combined_df = combined_df.dropna(subset=['briefit'])
+        combined_df.to_csv('feedbacko.csv', index=False)
+        st.success('Thank You for your feedback')
+
+        

gross_premimum.py

@@ -0,0 +1,178 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[145]:
+
+
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd
+
+
+# In[146]:
+
+
+data = pd.read_csv('insurance.csv')
+data_new = data.copy(deep = True)
+
+
+# In[147]:
+
+
+data.head()
+
+
+# In[148]:
+
+
+data.isnull().sum()
+
+
+# In[149]:
+
+
+data.dropna(inplace = True)
+
+
+# In[150]:
+
+
+X = data.drop('gross_premium', axis = 1)
+y = data['gross_premium']
+
+
+# In[151]:
+
+
+import re
+
+obj_columns = list(data.select_dtypes("object").columns)
+obj_columns
+
+
+# In[152]:
+
+
+import re
+
+for col in obj_columns:
+    data[col] = data[col].astype("str")
+    data[col] = data[col].apply(lambda x: re.sub(r'[^a-zA-Z0-9]', '', x.lower())).astype("str")
+
+
+# In[153]:
+
+
+season_catogory = list(data.season.values)
+scheme_catogory = list(data.scheme.values)
+state_catogory  = list(data.state_name.values)
+district_catogory = list(data.district_name.values)
+
+
+# In[154]:
+
+
+columns = ['season','scheme','state_name','district_name']
+from sklearn.preprocessing import LabelEncoder
+encoder = LabelEncoder()
+for col in columns:
+    data[col] = encoder.fit_transform(data[col])
+
+
+# In[155]:
+
+
+season_label = list(data.season.values)
+scheme_label = list(data.scheme.values)
+state_label = list(data.state_name.values)
+district_label = list(data.district_name.values)
+
+
+# In[156]:
+
+
+season_category_label_dict = dict(zip(season_catogory, season_label))
+
+scheme_category_label_dict = dict(zip(scheme_catogory, scheme_label))
+
+state_category_label_dict = dict(zip(state_catogory, state_label))
+
+district_category_label_dict = dict(zip(district_catogory, district_label))
+
+
+# In[157]:
+
+
+
+# In[163]:
+
+
+# X = data.iloc[:,:-1]
+# y = data.iloc[:,-1]
+
+
+# In[164]:
+
+
+# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+# # In[165]:
+
+
+# from sklearn.linear_model import LinearRegression
+
+
+# In[166]:
+
+
+# model = LinearRegression()
+
+
+# # In[167]:
+
+
+# model.fit(X, y)
+
+
+# In[168]:
+
+
+# import pickle as pk
+# filename= 'crop_grosspremimum_Jp.pkl'
+# pk.dump(model,open(filename,'wb'))
+
+
+# In[169]:
+
+
+def encoding(input_data):
+      input_data[0] = season_category_label_dict[input_data[0].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[1] = scheme_category_label_dict[input_data[1].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[2] = state_category_label_dict[input_data[2].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[3] = district_category_label_dict[input_data[3].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      return input_data
+
+
+# In[170]:
+
+
+# crop_grosspremimum = pk.load(open(filename, "rb"))
+
+
+# # In[172]:
+
+
+# data = ["kharif","PMFBY","Andhra Pradesh","Chittoor",18.82,22410.65,792.39,50.93,50.93,614]
+# crop_grosspremimum.predict([encoding(data)])[0]
+
+
+# In[ ]:
+
+
+
+

model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d46327688e9e60166e607920dc5875195c9131afbe6760c5be19335f0479331c
+size 28043192