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| from pycaret.regression import load_model, predict_model | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from PIL import Image | |
| #image = Image.open('omdena_logo.png') | |
| #st.set_page_config(page_title='omdena-milan', page_icon=image) | |
| import pycaret | |
| model1 = load_model('data/models/cereals_knn') | |
| model2= load_model('data/models/fruits&nuts_knn') | |
| model3 = load_model('data/models/grapes_olives_et') | |
| model4 = load_model('data/models/fresh_veg_et') | |
| model5 = load_model('data/models/industrial_crop_et') | |
| def predict1(model1, input_df1): | |
| predictions_df1 = predict_model(estimator=model1, data=input_df1) | |
| predictions1 = predictions_df1['prediction_label'][0] | |
| return predictions1 | |
| def predict2(model2, input_df2): | |
| predictions_df2= predict_model(estimator=model2, data=input_df2) | |
| predictions2 = predictions_df2['prediction_label'][0] | |
| return predictions2 | |
| def predict3(model3, input_df3): | |
| predictions_df3 = predict_model(estimator=model3, data=input_df3) | |
| predictions3 = predictions_df3['prediction_label'][0] | |
| return predictions3 | |
| def predict4(model4, input_df4): | |
| predictions_df4= predict_model(estimator=model4, data=input_df4) | |
| predictions4 = predictions_df4['prediction_label'][0] | |
| return predictions4 | |
| def predict5(model5, input_df5): | |
| predictions_df5= predict_model(estimator=model5, data=input_df5) | |
| predictions5 = predictions_df5['prediction_label'][0] | |
| return predictions5 | |
| def run(): | |
| add_selectbox = st.sidebar.selectbox( | |
| "Please choose your crop", | |
| ("Cereal & Legumes", "Fruits & Nuts", "Grapes & Olives", "Fresh Vegetables","Industrial crops", )) | |
| st.sidebar.info('Omdena-Milan Agrifood') | |
| st.sidebar.success('https://omdena.com/local-chapters/milan-italy-chapter/') | |
| st.title("Crop Prediction") | |
| if add_selectbox == 'Cereal & Legumes': | |
| temperature_max = st.number_input('Temperature max (°C)', min_value= 20, max_value= 50) | |
| temperature_min = st.number_input('Temperature min (°C)', min_value= -5, max_value= 20) | |
| relative_humidity = st.number_input('Relative humidity (%)', min_value=0, max_value=100) | |
| root_moisture = st.number_input('Root moisture', max_value=1) | |
| total_area_ha = st.number_input('Total area(ha)', min_value=0, max_value=6150) | |
| fertilizer_tonnes = st.number_input('Fertilizer (tonnes)', min_value=0, max_value=3405) | |
| fertilizer = st.selectbox('Type of fertilizer', ['calcium cyanamide', 'nitrogen-potassium', 'peaty-amend', | |
| 'organic-nitrogen', 'organic', 'ammonium sulphate', | |
| 'nitrogen-phosphorous', 'phosphorus-potassium', 'urea']) | |
| crop = st.selectbox('Type of crop', ['barley', 'bro-bean', 'chick-peas', 'dry-k-bean', 'd-wheat', | |
| 'early potatoes', 'lentil', 'oats', 'potatoes', 'grain pea', | |
| 'oats mix', 'spring barley', 'winter barley', 'c-wheat', 'maize', | |
| 'protein pea', 'rice', 'sorghum', 'sugar beet', 'other cereals', | |
| 'rye', 'titicale', 'c-spr-wheat&spelt', 'c-wint-wheat&spelt', | |
| 'sweet potatoes', 'sweet lupin', 'rye mix', 'cereal mix', | |
| 'wint-cereal-mix']) | |
| city = st.selectbox('City', ['Agrigento', 'Alessandria', 'Ancona', 'Arezzo', 'Ascoli Piceno', | |
| 'Asti', 'Avellino', 'Bari', 'Barletta-Andria-Trani', 'Belluno', | |
| 'Benevento', 'Bergamo', 'Biella', 'Bologna', 'Bolzano / Bozen', | |
| 'Brescia', 'Brindisi', 'Cagliari', 'Caltanissetta', 'Campobasso', | |
| 'Carbonia-Iglesias', 'Caserta', 'Catania', 'Catanzaro', 'Chieti', | |
| 'Como', 'Cosenza', 'Cremona', 'Crotone', 'Cuneo', 'Enna', 'Fermo', | |
| 'Ferrara', 'Firenze', 'Foggia', 'Forlì-Cesena', 'Frosinone', | |
| 'Genova', 'Gorizia', 'Grosseto', 'Imperia', 'Isernia', "L'Aquila", | |
| 'La Spezia', 'Latina', 'Lecce', 'Lecco', 'Livorno', 'Lodi', | |
| 'Lucca', 'Macerata', 'Mantova', 'Massa-Carrara', 'Matera', | |
| 'Medio Campidano', 'Messina', 'Milano', 'Modena', | |
| 'Monza e della Brianza', 'Napoli', 'Novara', 'Nuoro', 'Ogliastra', | |
| 'Olbia-Tempio', 'Oristano', 'Padova', 'Palermo', 'Parma', 'Pavia', | |
| 'Perugia', 'Pesaro e Urbino', 'Pescara', 'Piacenza', 'Pisa', | |
| 'Pistoia', 'Pordenone', 'Potenza', 'Prato', 'Ragusa', 'Ravenna', | |
| 'Reggio di Calabria', "Reggio nell'Emilia", 'Rieti', 'Rimini', | |
| 'Roma', 'Rovigo', 'Salerno', 'Sassari', 'Savona', 'Siena', | |
| 'Siracusa', 'Sondrio', 'Sud Sardegna', 'Taranto', 'Teramo', | |
| 'Terni', 'Torino', 'Trapani', 'Trentino Alto Adige / Südtirol', | |
| 'Trento', 'Treviso', 'Trieste', 'Udine',"Valle d'Aosta / Vallée d'Aoste", | |
| 'Varese', 'Venezia', 'Verbano-Cusio-Ossola', 'Vercelli', | |
| 'Verona', 'Vibo Valentia', 'Vicenza', 'Viterbo']) | |
| output1="" | |
| input_dict1 = {'T2M_MAX': temperature_max, 'T2M_MIN':temperature_min,'RH2M' : relative_humidity, 'total_area_ha': total_area_ha, | |
| 'GWETROOT' : root_moisture, 'Type_crop' : crop, 'Type_fertilizer': fertilizer, 'Fertilizers_tonnes': fertilizer_tonnes ,'City' : city} | |
| input_df1 = pd.DataFrame([input_dict1]) | |
| if st.button("Predict Cereal & Legumes"): | |
| output1 = predict1(model1=model1, input_df1=input_df1) | |
| output1 = 'Tons ' + "{:.2f}".format(output1) | |
| st.success('The output is {}'.format(output1)) | |
| if add_selectbox == 'Fruits & Nuts': | |
| temperature_max = st.number_input('Temperature max (°C)', min_value= 20, max_value= 50) | |
| temperature_min = st.number_input('Temperature min (°C)', min_value= -5, max_value= 20) | |
| relative_humidity = st.number_input('Relative humidity (%)', min_value=0, max_value=100) | |
| root_moisture = st.number_input('Root moisture', max_value=1) | |
| total_area_ha = st.number_input('Total area(ha)', min_value=0, max_value=430) | |
| fertilizer_tonnes = st.number_input('Fertilizer (tonnes)', min_value=0, max_value=3477) | |
| fertilizer = st.selectbox('Type of fertilizer', ['calcium cyanamide', 'nitrogen-potassium', 'peaty-amend', | |
| 'organic-nitrogen', 'organic', 'ammonium sulphate', | |
| 'nitrogen-phosphorous', 'phosphorus-potassium', 'urea']) | |
| crop = st.selectbox('Type of crop', ['apple', 'apricot', 'cherry in complex', 'kiwi', 'nectarine', | |
| 'plum', 'hazelnut', 'pear', 'peach', 'almond']) | |
| city = st.selectbox('City', ['Agrigento', 'Alessandria', 'Ancona', 'Arezzo', 'Ascoli Piceno', | |
| 'Asti', 'Avellino', 'Bari', 'Belluno', 'Benevento', 'Bergamo', | |
| 'Biella', 'Bologna', 'Brescia', 'Brindisi', 'Caltanissetta', | |
| 'Campobasso', 'Caserta', 'Catania', 'Catanzaro', 'Chieti', 'Como', | |
| 'Cosenza', 'Cremona', 'Crotone', 'Enna', 'Ferrara', 'Firenze', | |
| 'Foggia', 'Frosinone', 'Genova', 'Gorizia', 'Grosseto', 'Imperia', | |
| 'Isernia', 'La Spezia', 'Latina', 'Lecce', 'Lecco', 'Livorno', | |
| 'Lodi', 'Lucca', 'Macerata', 'Mantova', 'Matera', 'Messina', | |
| 'Milano', 'Modena', 'Napoli', 'Novara', 'Nuoro', 'Oristano', | |
| 'Padova', 'Palermo', 'Parma', 'Pavia', 'Perugia', | |
| 'Pesaro e Urbino', 'Pescara', 'Piacenza', 'Pisa', 'Pistoia', | |
| 'Pordenone', 'Potenza', 'Prato', 'Ragusa', 'Ravenna', | |
| 'Reggio di Calabria', "Reggio nell'Emilia", 'Rieti', 'Rimini', | |
| 'Roma', 'Rovigo', 'Salerno', 'Sassari', 'Savona', 'Siena', | |
| 'Siracusa', 'Taranto', 'Teramo', 'Terni', 'Torino', 'Trapani', | |
| 'Treviso', 'Trieste', 'Udine', 'Varese', 'Venezia', | |
| 'Verbano-Cusio-Ossola', 'Vercelli', 'Verona', 'Vibo Valentia', | |
| 'Vicenza', 'Viterbo', 'Carbonia-Iglesias', 'Medio Campidano', | |
| 'Ogliastra', 'Olbia-Tempio', 'Barletta-Andria-Trani', 'Fermo', | |
| 'Monza e della Brianza']) | |
| output2="" | |
| input_dict2 = {'T2M_MAX': temperature_max, 'T2M_MIN':temperature_min,'RH2M' : relative_humidity, 'total_area_ha': total_area_ha, | |
| 'GWETROOT' : root_moisture, 'Type_crop' : crop, 'Type_fertilizer': fertilizer, 'Fertilizers_tonnes': fertilizer_tonnes ,'City' : city} | |
| input_df2 = pd.DataFrame([input_dict2]) | |
| if st.button("Predict Fruits & Nuts"): | |
| output2 = predict2(model2=model2, input_df2=input_df2) | |
| output2 = 'Tons ' + "{:.2f}".format(output2) | |
| st.success('The output is {}'.format(output2)) | |
| if add_selectbox == 'Grapes & Olives': | |
| temperature_max = st.number_input('Temperature max (°C)', min_value= 20, max_value= 50) | |
| temperature_min = st.number_input('Temperature min (°C)', min_value= -5, max_value= 20) | |
| relative_humidity = st.number_input('Relative humidity (%)', min_value=0, max_value=100) | |
| root_moisture = st.number_input('Root moisture', max_value=1) | |
| total_area_ha = st.number_input('Total area(ha)', min_value=0, max_value=5010) | |
| fertilizer_tonnes = st.number_input('Fertilizer (tonnes)', min_value=0, max_value=2852) | |
| fertilizer = st.selectbox('Type of fertilizer', ['calcium cyanamide', 'nitrogen-potassium', 'peaty-amend', | |
| 'organic-nitrogen', 'organic', 'ammonium sulphate', | |
| 'nitrogen-phosphorous', 'phosphorus-potassium', 'urea']) | |
| crop = st.selectbox('Type of crop', ['grapes-n.e.c', 'grapes-wines(N-pdo/pgi)', 'table olives', | |
| 'grapes-table', 'oil olives', 'other olives', | |
| 'grapes-wines(Y-pdo)', 'grapes-wines(Y-pgi)', 'grapes-raisins']) | |
| city = st.selectbox('City', ['Agrigento', 'Alessandria', 'Ancona', 'Arezzo', 'Ascoli Piceno', | |
| 'Asti', 'Avellino', 'Bari', 'Belluno', 'Benevento', 'Bergamo', | |
| 'Biella', 'Bologna', 'Brescia', 'Brindisi', 'Caltanissetta', | |
| 'Campobasso', 'Caserta', 'Catania', 'Catanzaro', 'Chieti', | |
| 'Cosenza', 'Cremona', 'Crotone', 'Enna', 'Ferrara', 'Firenze', | |
| 'Foggia', 'Frosinone', 'Genova', 'Grosseto', 'Imperia', 'Isernia', | |
| 'La Spezia', 'Latina', 'Lecce', 'Livorno', 'Lodi', 'Lucca', | |
| 'Macerata', 'Mantova', 'Matera', 'Messina', 'Milano', 'Modena', | |
| 'Napoli', 'Novara', 'Nuoro', 'Oristano', 'Padova', 'Palermo', | |
| 'Parma', 'Pavia', 'Perugia', 'Pesaro e Urbino', 'Pescara', | |
| 'Piacenza', 'Pisa', 'Pistoia', 'Pordenone', 'Potenza', 'Prato', | |
| 'Ragusa', 'Ravenna', 'Reggio di Calabria', "Reggio nell'Emilia", | |
| 'Rieti', 'Rimini', 'Roma', 'Rovigo', 'Salerno', 'Sassari', | |
| 'Savona', 'Siena', 'Siracusa', 'Taranto', 'Teramo', 'Terni', | |
| 'Torino', 'Trapani', 'Treviso', 'Trieste', 'Udine', 'Varese', | |
| 'Venezia', 'Verbano-Cusio-Ossola', 'Vercelli', 'Verona', | |
| 'Vibo Valentia', 'Vicenza', 'Viterbo', 'Carbonia-Iglesias', | |
| 'Medio Campidano', 'Ogliastra', 'Olbia-Tempio', | |
| 'Barletta-Andria-Trani', 'Fermo', 'Monza e della Brianza']) | |
| output3="" | |
| input_dict3 = {'T2M_MAX': temperature_max, 'T2M_MIN':temperature_min,'RH2M' : relative_humidity, 'total_area_ha': total_area_ha, | |
| 'GWETROOT' : root_moisture, 'Type_crop' : crop, 'Type_fertilizer': fertilizer, 'Fertilizers_tonnes': fertilizer_tonnes ,'City' : city} | |
| input_df3 = pd.DataFrame([input_dict3]) | |
| if st.button("Predict Grapes & Olives"): | |
| output3 = predict3(model3=model3, input_df3=input_df3) | |
| output3 = 'Tons ' + "{:.2f}".format(output3) | |
| st.success('The output is {}'.format(output3)) | |
| if add_selectbox == 'Fresh Vegetables': | |
| temperature_max = st.number_input('Temperature max (°C)', min_value= 20, max_value= 50) | |
| temperature_min = st.number_input('Temperature min (°C)', min_value= -5, max_value= 20) | |
| relative_humidity = st.number_input('Relative humidity (%)', min_value=0, max_value=100) | |
| root_moisture = st.number_input('Root moisture', max_value=1) | |
| total_area_ha = st.number_input('Total area(ha)', min_value=0, max_value=431) | |
| fertilizer_tonnes = st.number_input('Fertilizer (tonnes)', min_value=0, max_value=3473) | |
| fertilizer = st.selectbox('Type of fertilizer', ['calcium cyanamide', 'nitrogen-potassium', 'peaty-amend', | |
| 'organic-nitrogen', 'organic', 'ammonium sulphate', | |
| 'nitrogen-phosphorous', 'phosphorus-potassium', 'urea']) | |
| crop = st.selectbox('Type of crop', ['cauliflower&broccoli-field', 'courgette-field', 'egg-plant-field', | |
| 'fresh-beans-field', 'lettuce-field', 'onions-field', | |
| 'red-pepper-field', 'chicory-field', 'melon-field', 'fresh-tomato']) | |
| city = st.selectbox('City', ['Agrigento', 'Alessandria', 'Ancona', 'Arezzo', 'Ascoli Piceno', | |
| 'Asti', 'Avellino', 'Bari', 'Belluno', 'Benevento', 'Bergamo', | |
| 'Biella', 'Bologna', 'Brescia', 'Brindisi', 'Caltanissetta', | |
| 'Campobasso', 'Caserta', 'Catania', 'Catanzaro', 'Chieti', | |
| 'Cosenza', 'Cremona', 'Crotone', 'Enna', 'Ferrara', 'Firenze', | |
| 'Foggia', 'Frosinone', 'Genova', 'Gorizia', 'Grosseto', 'Imperia', | |
| 'Isernia', 'La Spezia', 'Latina', 'Lecce', 'Livorno', 'Lodi', | |
| 'Lucca', 'Macerata', 'Mantova', 'Matera', 'Messina', 'Milano', | |
| 'Modena', 'Napoli', 'Novara', 'Nuoro', 'Oristano', 'Padova', | |
| 'Palermo', 'Parma', 'Pavia', 'Perugia', 'Pesaro e Urbino', | |
| 'Pescara', 'Piacenza', 'Pisa', 'Pistoia', 'Pordenone', 'Potenza', | |
| 'Prato', 'Ragusa', 'Ravenna', 'Reggio di Calabria', | |
| "Reggio nell'Emilia", 'Rimini', 'Roma', 'Rovigo', 'Salerno', | |
| 'Sassari', 'Savona', 'Siena', 'Siracusa', 'Taranto', 'Teramo', | |
| 'Terni', 'Torino', 'Trapani', 'Treviso', 'Trieste', 'Udine', | |
| 'Varese', 'Venezia', 'Verbano-Cusio-Ossola', 'Vercelli', 'Verona', | |
| 'Vibo Valentia', 'Vicenza', 'Viterbo', 'Carbonia-Iglesias', | |
| 'Medio Campidano', 'Ogliastra', 'Olbia-Tempio', 'Barletta-Andria-Trani', | |
| 'Fermo', 'Monza e della Brianza']) | |
| output4="" | |
| input_dict4 = {'T2M_MAX': temperature_max, 'T2M_MIN':temperature_min,'RH2M' : relative_humidity, 'total_area_ha': total_area_ha, | |
| 'GWETROOT' : root_moisture, 'Type_crop' : crop, 'Type_fertilizer': fertilizer, 'Fertilizers_tonnes': fertilizer_tonnes ,'City' : city} | |
| input_df4 = pd.DataFrame([input_dict4]) | |
| if st.button("Predict Fresh Vegetables"): | |
| output4 = predict4(model4=model4, input_df4=input_df4) | |
| output4 = 'Tons ' + "{:.2f}".format(output4) | |
| st.success('The output is {}'.format(output4)) | |
| if add_selectbox == 'Industrial crops': | |
| temperature_max = st.number_input('Temperature max (°C)', min_value= 20, max_value= 50) | |
| temperature_min = st.number_input('Temperature min (°C)', min_value= -5, max_value= 20) | |
| relative_humidity = st.number_input('Relative humidity (%)', min_value=0, max_value=100) | |
| root_moisture = st.number_input('Root moisture', max_value=1) | |
| total_area_ha = st.number_input('Total area(ha)', min_value=0, max_value=1440) | |
| fertilizer_tonnes = st.number_input('Fertilizer (tonnes)', min_value=0, max_value=4824) | |
| fertilizer = st.selectbox('Type of fertilizer', ['calcium cyanamide', 'nitrogen-potassium', 'peaty-amend', | |
| 'organic-nitrogen', 'organic', 'ammonium sulphate', | |
| 'nitrogen-phosphorous', 'phosphorus-potassium', 'urea']) | |
| crop = st.selectbox('Type of crop', ['hemp', 'rape', 'soya beans', 'tobacco', 'flax', 'parsley-field', | |
| 'sunflower']) | |
| city = st.selectbox('City', ['Alessandria', 'Ancona', 'Arezzo', 'Ascoli Piceno', 'Asti', | |
| 'Avellino', 'Bari', 'Belluno', 'Benevento', 'Bergamo', 'Biella', | |
| 'Bologna', 'Brescia', 'Caltanissetta', 'Campobasso', 'Caserta', | |
| 'Catania', 'Catanzaro', 'Chieti', 'Como', 'Cosenza', 'Cremona', | |
| 'Crotone', 'Ferrara', 'Firenze', 'Foggia', 'Frosinone', 'Genova', | |
| 'Gorizia', 'Grosseto', 'Imperia', 'Isernia', 'Latina', 'Lecco', | |
| 'Livorno', 'Lodi', 'Lucca', 'Macerata', 'Mantova', 'Matera', | |
| 'Milano', 'Modena', 'Napoli', 'Novara', 'Nuoro', 'Oristano', | |
| 'Padova', 'Parma', 'Pavia', 'Perugia', 'Pescara', 'Piacenza', | |
| 'Pisa', 'Pistoia', 'Pordenone', 'Potenza', 'Prato', 'Ravenna', | |
| "Reggio nell'Emilia", 'Rieti', 'Rimini', 'Roma', 'Rovigo', | |
| 'Salerno', 'Sassari', 'Savona', 'Siena', 'Taranto', 'Teramo', | |
| 'Terni', 'Torino', 'Treviso', 'Trieste', 'Udine', 'Varese', | |
| 'Venezia', 'Verbano-Cusio-Ossola', 'Vercelli', 'Verona', 'Vicenza', | |
| 'Viterbo', 'Carbonia-Iglesias', 'Medio Campidano', 'Ogliastra', | |
| 'Vibo Valentia', 'Barletta-Andria-Trani', 'Fermo', | |
| 'Monza e della Brianza', 'La Spezia']) | |
| output5="" | |
| input_dict5 = {'T2M_MAX': temperature_max, 'T2M_MIN':temperature_min,'RH2M' : relative_humidity, 'total_area_ha': total_area_ha, | |
| 'GWETROOT' : root_moisture, 'Type_crop' : crop, 'Type_fertilizer': fertilizer, 'Fertilizers_tonnes': fertilizer_tonnes ,'City' : city} | |
| input_df5 = pd.DataFrame([input_dict5]) | |
| if st.button("Predict Industrial crops"): | |
| output5 = predict5(model5=model5, input_df5=input_df5) | |
| output5 = 'Tons ' + "{:.2f}".format(output5) | |
| st.success('The output is {}'.format(output5)) | |
| if __name__ == '__main__': | |
| run() | |