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Crop_Insurance_Risk.py

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+import streamlit as st
+import pandas as pd
+import pydeck as pdk
+import pickle
+import time
+import sum_insurance as insu
+import gross_premimum as gross
+import numpy as np
+
+def sum_insurance_prediction(input_data,crop_insurance_sum_Raghu):
+    input_data_asarray = np.asarray(input_data)
+    input_data_reshaped = input_data_asarray.reshape(1,-1)
+    prediction = crop_insurance_sum_Raghu.predict(input_data_reshaped)
+    return prediction
+
+def crop_grosspremimum_pred(input_data,crop_insurance_sum_Raghu):
+    input_data_asarray = np.asarray(input_data)
+    input_data_reshaped = input_data_asarray.reshape(1,-1)
+    prediction = crop_insurance_sum_Raghu.predict(input_data_reshaped)
+    return prediction
+
+def insurance_app():
+    tab1, tab2,tab3 = st.tabs(["Maximum amount an insurance pay for a covered loss.","Total Amount insurance Paid by company in given period.","Feedback"]) 
+    with tab1:    
+        st.title('Predict Insurance Payout on loss')
+        background_image = 'https://img.freepik.com/premium-photo/photo-coins-plant-black-background-with-empty-space-text-design-elements_176841-5042.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>
+        """
+        st.markdown(html_code, unsafe_allow_html=True)
+# season,scheme,state_name,district_name,area_insured,sum_insured,farmer_share,goi_share,state_share,iu_count
+# kharif,PMFBY,Andhra Pradesh,Anantapur,17.44,9493.41,453.87,285.46,285.46,85
+        @st.cache_resource
+        def load():
+            return  pickle.load(open('crop_insurance_sum_Raghu.pkl','rb'))
+        crop_insurance_sum_Raghu = load()
+        # st.subheader('Enter Input Values')
+        col1,col2 = st.columns([1,1])
+        with col1:
+            season1 = st.selectbox('Season', ('kharif', 'rabi'),key=1)
+        with col1:
+            scheme1 = st.selectbox('Scheme', ('PMFBY', 'WBCIS'),key=2)
+        with col1:
+            state_name1 = st.selectbox('State Name',('Assam' ,'Chhattisgarh', 'Goa' ,'Haryana',
+                                                        'Himachal Pradesh', 'Karnataka', 'Kerala' ,'Madhya Pradesh' ,'Maharashtra',
+                                                        'Meghalaya', 'Odisha', 'Puducherry', 'Rajasthan', 'Sikkim', 'Tamil Nadu',
+                                                        'Tripura', 'Uttar Pradesh', 'Uttarakhand'),key=3)
+        with col1:
+            district_names = ('Anantapur', 'Chittoor', 'East Godavari', 'Guntur', 'Krishna',
+                'Kurnool', 'Prakasam','Srikakulam', 'Vizianagaram',
+                'West Godavari', 'Baksa', 'Barpeta', 'Bongaigaon', 'Cachar',
+                'Chirang', 'Darrang', 'Dhemaji', 'Dhubri', 'Dibrugarh', 'Goalpara',
+                'Golaghat', 'Hailakandi', 'Jorhat', 'Kamrup', 'Karbi Anglong',
+                'Karimganj', 'Lakhimpur', 'Nagaon', 'Nalbari', 'Sivasagar',
+                'Sonitpur', 'Tinsukia', 'Udalguri', 'Balod', 'Baloda Bazar',
+                'Balrampur', 'Bastar', 'Bemetara', 'Bijapur', 'Bilaspur',
+                'Dhamtari', 'Durg', 'Gariyaband', 'Janjgir-champa', 'Jashpur',
+                'Kondagaon', 'Korba', 'Mahasamund', 'Mungeli', 'Narayanpur',
+                'Raigarh', 'Raipur', 'Rajnandgaon', 'Sukma', 'Surajpur', 'Surguja',
+                'North Goa', 'South Goa', 'Banas Kantha', 'Patan', 'Ambala',
+                'Bhiwani', 'Faridabad', 'Fatehabad', 'Hisar', 'Jhajjar', 'Jind',
+                'Kaithal', 'Karnal', 'Kurukshetra', 'Mahendragarh', 'Palwal',
+                'Panchkula', 'Panipat', 'Rewari', 'Rohtak', 'Sirsa', 'Sonipat',
+                'Yamunanagar', 'Chamba', 'Hamirpur', 'Kangra', 'Kullu', 'Mandi',
+                'Shimla', 'Sirmaur', 'Solan', 'Una', 'Doda', 'Jammu', 'Kathua',
+                'Kishtwar', 'Rajouri', 'Ramban', 'Reasi', 'Samba', 'Udhampur',
+                'Bokaro', 'Chatra', 'Deoghar', 'Dhanbad', 'Dumka', 'Garhwa',
+                'Giridih', 'Godda', 'Gumla', 'Hazaribagh', 'Jamtara', 'Khunti',
+                'Latehar', 'Lohardaga', 'Pakur', 'Palamu', 'Ramgarh', 'Ranchi',
+                'Simdega', 'Ballari', 'Belagavi', 'Bengaluru Rural',
+                'Bengaluru Urban', 'Bidar', 'Chikkamagaluru', 'Chitradurga',
+                'Dakshina Kannada', 'Davangere', 'Dharwad', 'Gadag', 'Haveri',
+                'Kodagu', 'Kolar', 'Koppal', 'Mandya', 'Mysuru', 'Raichur',
+                'Ramanagara', 'Shivamogga', 'Tumakuru', 'Udupi', 'Vijayapura',
+                'Alappuzha', 'Ernakulam', 'Idukki', 'Kannur', 'Kasaragod',
+                'Kollam', 'Kottayam', 'Kozhikode', 'Malappuram', 'Palakkad',
+                'Pathanamthitta', 'Thiruvananthapuram', 'Thrissur', 'Wayanad',
+                'Agar Malwa', 'Alirajpur', 'Anuppur', 'Ashoknagar', 'Balaghat',
+                'Barwani', 'Betul', 'Bhind', 'Bhopal', 'Burhanpur', 'Chhatarpur',
+                'Chhindwara', 'Damoh', 'Datia', 'Dewas', 'Dhar', 'Dindori', 'Guna',
+                'Gwalior', 'Harda', 'Indore', 'Jabalpur', 'Jhabua', 'Katni',
+                'Mandla', 'Mandsaur', 'Morena', 'Neemuch', 'Panna', 'Raisen',
+                'Rajgarh', 'Ratlam', 'Rewa', 'Sagar', 'Satna', 'Sehore', 'Seoni',
+                'Shahdol', 'Shajapur', 'Sheopur', 'Shivpuri', 'Sidhi', 'Singrauli',
+                'Tikamgarh', 'Ujjain', 'Umaria', 'Vidisha', 'Akola', 'Amravati',
+                'Aurangabad', 'Bhandara', 'Chandrapur', 'Dhule', 'Gadchiroli',
+                'Hingoli', 'Jalgaon', 'Jalna', 'Kolhapur', 'Latur', 'Nagpur',
+                'Nanded', 'Nandurbar', 'Nashik', 'Osmanabad', 'Palghar',
+                'Parbhani', 'Pune', 'Ratnagiri', 'Sangli', 'Satara', 'Sindhudurg',
+                'Solapur', 'Thane', 'Wardha', 'Washim', 'Yavatmal', 'Anugul',
+                'Balangir', 'Baleshwar', 'Bargarh', 'Bhadrak', 'Cuttack',
+                'Dhenkanal', 'Gajapati', 'Ganjam', 'Jagatsinghapur', 'Jajapur',
+                'Jharsuguda', 'Kalahandi', 'Kandhamal', 'Kendrapara', 'Kendujhar',
+                'Khordha', 'Koraput', 'Malkangiri', 'Mayurbhanj', 'Nayagarh',
+                'Nuapada', 'Puri', 'Rayagada', 'Sambalpur', 'Sundargarh', 'Ajmer',
+                'Alwar', 'Banswara', 'Baran', 'Barmer', 'Bharatpur', 'Bhilwara',
+                'Bikaner', 'Bundi', 'Churu', 'Dausa', 'Dungarpur', 'Hanumangarh',
+                'Jaipur', 'Jaisalmer', 'Jhalawar', 'Jhunjhunu', 'Jodhpur',
+                'Karauli', 'Kota', 'Nagaur', 'Pali', 'Pratapgarh', 'Rajsamand',
+                'Sawai Madhopur', 'Sikar', 'Sirohi', 'Tonk', 'Udaipur', 'Ariyalur',
+                'Coimbatore', 'Cuddalore', 'Dharmapuri', 'Dindigul', 'Erode',
+                'Kanniyakumari', 'Karur', 'Krishnagiri', 'Madurai', 'Nagapattinam',
+                'Namakkal', 'Perambalur', 'Pudukkottai', 'Salem', 'Sivaganga',
+                'Thanjavur', 'The Nilgiris', 'Theni', 'Thiruvallur', 'Thiruvarur',
+                'Tiruchirappalli', 'Tirunelveli', 'Tiruppur', 'Tiruvannamalai',
+                'Vellore', 'Virudhunagar', 'Adilabad', 'Kamareddy', 'Karimnagar',
+                'Khammam', 'Mahabubabad', 'Mancherial', 'Medak', 'Nagarkurnool',
+                'Nalgonda', 'Nirmal', 'Nizamabad', 'Peddapalli', 'Ranga Reddy',
+                'Sangareddy', 'Siddipet', 'Suryapet', 'Vikarabad', 'Wanaparthy',
+                'Agra', 'Aligarh', 'Ambedkar Nagar', 'Amethi', 'Amroha', 'Auraiya',
+                'Azamgarh', 'Baghpat', 'Bahraich', 'Ballia', 'Banda', 'Barabanki',
+                'Bareilly', 'Basti', 'Bhadohi', 'Bijnor', 'Budaun', 'Bulandshahr',
+                'Chandauli', 'Chitrakoot', 'Deoria', 'Etah', 'Etawah',
+                'Farrukhabad', 'Fatehpur', 'Firozabad', 'Gautam Buddha Nagar',
+                'Ghaziabad', 'Ghazipur', 'Gonda', 'Gorakhpur', 'Hapur', 'Hardoi',
+                'Hathras', 'Jalaun', 'Jaunpur', 'Jhansi', 'Kannauj',
+                'Kanpur Dehat', 'Kanpur Nagar', 'Kasganj', 'Kaushambi', 'Kheri',
+                'Kushi Nagar', 'Lalitpur', 'Lucknow', 'Mahoba', 'Mainpuri',
+                'Mathura', 'Mau', 'Meerut', 'Mirzapur', 'Moradabad',
+                'Muzaffarnagar', 'Pilibhit', 'Rae Bareli', 'Rampur', 'Saharanpur',
+                'Sambhal', 'Shahjahanpur', 'Shamli', 'Siddharth Nagar', 'Sitapur',
+                'Sonbhadra', 'Sultanpur', 'Unnao', 'Varanasi', 'Almora',
+                'Bageshwar', 'Chamoli', 'Champawat', 'Dehradun', 'Nainital',
+                'Pithoragarh', 'Rudra Prayag', 'Tehri Garhwal', 'Uttar Kashi',
+                'Alipurduar', 'Bankura', 'Birbhum', 'Jalpaiguri', 'Jhargram',
+                'Kalimpong', 'Maldah', 'Murshidabad', 'Nadia', 'Paschim Bardhaman',
+                'Purba Bardhaman', 'Kokrajhar', 'Arvalli', 'Bharuch', 'Bhavnagar',
+                'Gandhinagar', 'Kheda', 'Mahesana', 'Panch Mahals', 'Porbandar',
+                'Surendranagar', 'Kinnaur', 'Karaikal', 'Ramanathapuram', 'Dhalai',
+                'Gomati', 'Khowai', 'North Tripura', 'Sepahijala', 'Unakoti',
+                'West Tripura', 'Dima Hasao', 'Ahmadabad', 'Niwari', 'Bishnupur',
+                'Chandel', 'Churachandpur', 'Imphal East', 'Imphal West',
+                'Senapati', 'Thoubal', 'East Khasi Hills', 'North Garo Hills',
+                'Ri Bhoi', 'South West Garo Hills', 'West Garo Hills',
+                'West Khasi Hills', 'Mulugu', 'Narayanpet', 'South Tripura',
+                'West Jaintia Hills', 'Chengalpattu', 'Kallakurichi', 'Ranipet',
+                'Tenkasi', 'Tirupathur', 'Anantnag', 'Biswanath', 'Hojai',
+                'Kamrup Metro', 'Majuli', 'Marigaon', 'South Salmara Mancachar',
+                'West Karbi Anglong', 'Ukhrul', 'Mayiladuthurai',
+                'Alluri Sitharama Raju', 'Anakapalli', 'Annamayya', 'Bapatla',
+                'Eluru', 'Kakinada', 'Nandyal', 'Ntr', 'Palnadu',
+                'Parvathipuram Manyam', 'Spsr Nellore', 'Sri Sathya Sai',
+                'Tirupati', 'Visakhapatanam', 'Bajali', 'Charaideo', 'Dantewada',
+                'Gaurella Pendra Marwahi', 'Kabirdham', 'Kanker', 'Korea',
+                'Gurugram', 'Nuh', 'Lahul And Spiti', 'East Nimar', 'Khargone',
+                'Narsinghpur', 'Ahmednagar', 'Beed', 'Buldhana', 'Gondia',
+                'Raigad', 'Tamenglong', 'South Garo Hills', 'Boudh', 'Deogarh',
+                'Nabarangpur', 'Sonepur', 'Pondicherry', 'Chittorgarh', 'Dholpur',
+                'Jalore', 'Gangtok', 'Gyalshing', 'Namchi', 'Ayodhya',
+                'Maharajganj', 'Prayagraj', 'Shravasti', 'Haridwar',
+                'Pauri Garhwal', 'Kanchipuram', 'Tuticorin', 'Villupuram')
+            district_name1 = st.selectbox('District Name',district_names,key=4)
+        with col2:
+            area_insured1 = st.number_input('Total Area Covered for Insurence', value=17.44,min_value=1.0,max_value=3777.0,step=1.0,key=5)
+        with col2:
+            farmer_share1 = st.number_input('Premium Paid by Individual', value=453.87,min_value=1.0,max_value=8600.32,step=10.0,key=6)
+        with col2:
+            goi_share1 = st.number_input('Premium Paid by GOI', value=285.46,min_value=1.0,max_value=33292.16,step=10.0,key=7)
+        with col2:
+            state_share1 = st.number_input('Premium Paid by Govt', value=285.46,min_value=1.0,max_value=40723.02,step=10.0,key=8)
+        with col1:
+            iu_count1 = st.number_input('Count of Insurence Units', value=85.0,min_value=1.0,max_value=2492.00,step=5.0,key=9)
+
+        
+        prediction1 = ''
+        input_data = [season1,scheme1,state_name1,district_name1,area_insured1,farmer_share1,goi_share1,state_share1,iu_count1]
+        if st.button('Predict'):
+            encode = insu.encoding(input_data)
+            try:
+                prediction1 = sum_insurance_prediction(encode,crop_insurance_sum_Raghu)
+                progress = st.progress(0)
+                for i in range(100):
+                    time.sleep(0.005)
+                    progress.progress(i+1)
+                st.subheader(f"Sum Insured: {round(prediction1[0],3)} Rupees")
+            except:
+                st.error("Invalid Inputs")
+
+
+
+
+    with tab2:
+        st.title('Gross Premium')
+        # background_image = 'https://img.freepik.com/premium-photo/photo-coins-plant-black-background-with-empty-space-text-design-elements_176841-5042.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>
+        # """
+        # st.markdown(html_code, unsafe_allow_html=True)
+
+# season,scheme,state_name,district_name,area_insured,sum_insured,farmer_share,goi_share,state_share,iu_count,gross_premium
+# kharif,PMFBY,Andhra Pradesh,Anantapur,17.44,9493.41,453.87,285.46,285.46,85,1024.79
+        def loada():
+            return pickle.load(open('crop_grosspremimum_Jp.pkl','rb'))
+        crop_grosspremimum = loada()
+        # st.subheader('Enter Input Values')
+        col1,col2 = st.columns([1,1])
+        with col1:
+            season = st.selectbox('Season', ('kharif', 'rabi'))
+        with col1:
+            scheme = st.selectbox('Scheme', ('PMFBY', 'WBCIS'))
+        with col1:
+            state_name = st.selectbox('State Name',('Assam' ,'Chhattisgarh', 'Goa' ,'Haryana',
+                                                        'Himachal Pradesh', 'Karnataka', 'Kerala' ,'Madhya Pradesh' ,'Maharashtra',
+                                                        'Meghalaya', 'Odisha', 'Puducherry', 'Rajasthan', 'Sikkim', 'Tamil Nadu',
+                                                        'Tripura', 'Uttar Pradesh', 'Uttarakhand'))
+        with col1:
+            district_name = st.selectbox('District Name', district_names,key = 34)
+        with col2:
+            area_insured = st.number_input('Total Area Covered for Insurence', value=17.44,min_value=1.0,max_value=3777.0,step=1.0)
+        with col2:
+            farmer_share = st.number_input('Premium Paid by Individual', value=453.87,min_value=1.0,max_value=8600.32,step=10.0)
+        with col2:
+            goi_share = st.number_input('Premium Paid by GOI', value=285.46,min_value=1.0,max_value=33292.15,step=10.0)
+        with col2:
+            state_share = st.number_input('Premium Paid by Govt', value=285.46,min_value=1.0,max_value=40723.02,step=10.0)
+        with col1:
+            iu_count = st.number_input('Count of Insurence Units', value=85.0,min_value=1.0,max_value=2492.00,step=5.0)
+        with col2:
+            sum_insured = st.number_input('YOur Sum Insured For The Crop', value=9493.41,min_value=1.0,max_value=535572.47,step=50.0)        
+        
+        prediction = ''
+        input_data = [season,scheme,state_name,district_name,area_insured,sum_insured,farmer_share,goi_share,state_share,iu_count]
+        if st.button('Predict',key=10):
+            encode = gross.encoding(input_data)
+            try:
+                prediction = crop_grosspremimum_pred(encode,crop_grosspremimum)
+                progress = st.progress(0)
+                for i in range(100):
+                    time.sleep(0.005)
+                    progress.progress(i+1)
+                st.subheader(f"gross premium: {round(prediction[0],3)} Rupees ")
+            except:
+                st.error("Invalid Inputs")
+
+        with tab3:
+            df = pd.read_csv('insurance.csv')
+            st.write('Current Dataset',df)
+            col1,col2 = st.columns(2)
+            with col1:
+                season = st.selectbox('Season', ('kharif', 'rabi'),key = 101)
+            with col1:
+                scheme = st.selectbox('Scheme', ('PMFBY', 'WBCIS'),key = 102)
+            with col1:
+                state_name = st.selectbox('State Name',('Assam' ,'Chhattisgarh', 'Goa' ,'Haryana',
+                                                            'Himachal Pradesh', 'Karnataka', 'Kerala' ,'Madhya Pradesh' ,'Maharashtra',
+                                                            'Meghalaya', 'Odisha', 'Puducherry', 'Rajasthan', 'Sikkim', 'Tamil Nadu',
+                                                            'Tripura', 'Uttar Pradesh', 'Uttarakhand'),key = 103)
+            with col1:
+                district_name = st.selectbox('District Name', district_names,key =104)
+            with col2:
+                area_insured = st.number_input('Total Area Covered for Insurence', value=17.44,min_value=1.0,max_value=3777.0,step=1.0,key = 105)
+            with col2:
+                farmer_share = st.number_input('Premium Paid by Individual', value=453.87,min_value=1.0,max_value=8600.32,step=10.0,key = 106)
+            with col2:
+                goi_share = st.number_input('Premium Paid by GOI', value=285.46,min_value=1.0,max_value=33292.15,step=10.0,key = 1062)
+            with col2:
+                state_share = st.number_input('Premium Paid by Govt', value=285.46,min_value=1.0,max_value=40723.02,step=10.0,key = 107)
+            with col1:
+                iu_count = st.number_input('Count of Insurence Units', value=85.0,min_value=1.0,max_value=2492.00,step=5.0,key = 108)
+            with col2:
+                sum_insured = st.number_input('YOur Sum Insured For The Crop', value=9493.41,min_value=1.0,max_value=535572.47,step=50.0,key = 109)
+            with col1:
+                gross_premium = st.number_input('Enter the amount after policy mature', value=2255.6,min_value=0.0,max_value=80103.4,step=5.0,key = 110)
+            
+            if st.button('submit',key = 10111):
+                new_row = {'season':season, 'scheme':scheme, 'state_name':state_name, 'district_name':district_name, 'area_insured':area_insured,
+                                'sum_insured':sum_insured, 'farmer_share':farmer_share, 'goi_share':goi_share, 'state_share':state_share, 'iu_count':iu_count,
+                                'gross_premium':gross_premium}
+                df = df.append(new_row,ignore_index= True)
+                df.to_csv('insurance.csv')
+                st.success("Thanks for the feedback")
+                st.write("Updated Dataset",df) 
+
+if __name__=='__main__':
+    insurance_app()

Crop_disease_prediction.py

@@ -0,0 +1,142 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+import tensorflow as tf 
+from tensorflow.keras.models import load_model
+
+# Load the saved model from Google Drive
+@st.cache_resource
+def loadu():
+    reso = load_model("model.h5")
+    return reso
+loaded_model = loadu()
+class_names = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
+               'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew',
+               'Cherry_(including_sour)___healthy', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
+               'Corn_(maize)___Common_rust_', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy',
+               'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+               'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot',
+               'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight',
+               'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy',
+               'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy',
+               'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold',
+               'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot',
+               'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus', 'Tomato___healthy']
+
+crop_medicines = {
+    'Apple___Apple_scab': ['Fungicides (e.g., sulfur or copper-based products)', 'Sanitation practices'],
+    'Apple___Black_rot': ['Fungicides', 'Proper sanitation practices'],
+    'Apple___Cedar_apple_rust': ['Fungicides', 'Removal of infected juniper plants'],
+    'Apple___healthy': ['No specific treatment'],
+
+    'Blueberry___healthy': ['Well-drained soil', 'Proper irrigation'],
+
+    'Cherry_(including_sour)___Powdery_mildew': ['Fungicides', 'Pruning for air circulation', 'Proper sanitation'],
+    'Cherry_(including_sour)___healthy': ['No specific treatment'],
+
+    'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': ['Fungicides', 'Crop rotation'],
+    'Corn_(maize)___Common_rust_': ['Fungicides', 'Resistant varieties'],
+    'Corn_(maize)___Northern_Leaf_Blight': ['Fungicides', 'Crop rotation'],
+    'Corn_(maize)___healthy': ['No specific treatment'],
+
+    'Grape___Black_rot': ['Fungicides', 'Proper pruning'],
+    'Grape___Esca_(Black_Measles)': ['Pruning', 'Cultural practices'],
+    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': ['Fungicides', 'Proper canopy management'],
+    'Grape___healthy': ['No specific treatment'],
+
+    'Orange___Haunglongbing_(Citrus_greening)': ['Vector control', 'Removing infected trees'],
+
+    'Peach___Bacterial_spot': ['Copper-based fungicides', 'Proper sanitation'],
+    'Peach___healthy': ['No specific treatment'],
+
+    'Pepper,_bell___Bacterial_spot': ['Copper-based fungicides', 'Resistant varieties'],
+    'Pepper,_bell___healthy': ['No specific treatment'],
+
+    'Potato___Early_blight': ['Fungicides', 'Crop rotation', 'Proper field hygiene'],
+    'Potato___Late_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'],
+    'Potato___healthy': ['No specific treatment'],
+
+    'Raspberry___healthy': ['Well-drained soil', 'Proper pruning'],
+
+    'Soybean___healthy': ['Proper crop rotation'],
+
+    'Squash___Powdery_mildew': ['Fungicides', 'Proper spacing for air circulation'],
+
+    'Strawberry___Leaf_scorch': ['Fungicides', 'Proper irrigation'],
+    'Strawberry___healthy': ['No specific treatment'],
+
+    'Tomato___Bacterial_spot': ['Copper-based fungicides', 'Resistant varieties'],
+    'Tomato___Early_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'],
+    'Tomato___Late_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'],
+    'Tomato___Leaf_Mold': ['Fungicides', 'Proper ventilation'],
+    'Tomato___Septoria_leaf_spot': ['Fungicides', 'Proper plant spacing'],
+    'Tomato___Spider_mites Two-spotted_spider_mite': ['Miticides', 'Biological control'],
+    'Tomato___Target_Spot': ['Fungicides', 'Proper plant hygiene'],
+    'Tomato___Tomato_Yellow_Leaf_Curl_Virus': ['Vector control', 'Resistant varieties'],
+    'Tomato___Tomato_mosaic_virus': ['Resistant varieties', 'Vector control'],
+    'Tomato___healthy': ['No specific treatment']
+}
+
+
+IMAGE_SIZE = (224, 224)
+
+def load_and_preprocess_image(image_path):
+    try:
+        img = tf.io.read_file(image_path)
+        img = tf.image.decode_image(img)
+        img = tf.image.resize(img, size=IMAGE_SIZE)
+        return img
+    except Exception as e:
+        st.error(f"Error loading or preprocessing image: {e}")
+        return None
+
+# def disease_predict(image_path):
+#     image = load_and_preprocess_image(image_path)
+
+#     if image is not None:
+#         try:
+#             pred = loaded_model.predict(tf.expand_dims(image, axis=0))
+#             predicted_value = class_names[pred.argmax()]
+#             display_prediction(predicted_value, image)
+#         except Exception as e:
+#             st.error(f"Error predicting disease: {e}")
+
+def disease_predict(image_path):
+    image = load_and_preprocess_image(image_path)
+
+    if image is not None:
+        try:
+            pred = loaded_model.predict(tf.expand_dims(image, axis=0))
+            if max(pred[0]) >= 0.90:
+               predicted_value = class_names[pred.argmax()]
+               display_prediction(predicted_value, image)
+            else:
+                st.error("Invalid Image")
+        except Exception as e:
+            st.error(f"Error predicting disease: {e}")
+
+def display_prediction(predicted_value, image):
+    st.image(image.numpy() / 255., caption=f"Predicted Disease: {predicted_value}", use_column_width=True)
+    st.success(f"Disease: **{predicted_value }**")
+    treatment(predicted_value)
+    # st.write(f"Image Shape: {image.shape}")
+
+def treatment(predicted_value): 
+    medicines = crop_medicines.get(predicted_value, [])
+    st.subheader("Solution for this Disease:")
+    st.write("Medicines:")
+    for medicine in medicines:
+        st.write(f"- {medicine}")
+
+def disease_app():
+    st.title('Agricultural Disease Detector 🦠')
+    uploaded_file = st.file_uploader("Upload an Image for Disease Analysis", type="jpg")
+    if uploaded_file is None:
+        uploaded_file =  st.camera_input("Capture a photo")
+    if uploaded_file is not None:
+        img_path = f"uploaded_image.jpg"
+        with open(img_path, "wb") as f:
+            f.write(uploaded_file.read())
+        disease_predict(img_path)
+
+# if __name__ == "__main__":
+    

crop_grosspremimum_Jp.pkl

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+size 778

crop_insurance_sum_Raghu.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:668518f12d1300fd92188941af6ec087b1c5b3db51ed3e851436fb98c9770236
+size 123777233