Delete app.py

app.py

@@ -1,109 +0,0 @@
-import gradio as gr
-from api import *
-from processing import *
-import pandas as pd
-from indices import indices
-import xgboost as xgb
-from lightgbm import LGBMRegressor
-import pickle
-import json
-#import boto3
-from shapely.geometry import MultiPolygon,shape
-from shapely.geometry import Point
-from shapely.geometry.polygon import Polygon
-from glob import glob
-import wget
-
-
-def predict(location_name,lat, lon):
-    cord = [lon,lat]
-    lon = round(lon,4)
-    lat = round(lat,4)
-    x1 = [lon,lat]
-    x2 = [lat,lon]
-    with open("data/CIV_0.json","r") as file:
-        data = json.load(file)
-    # extract ivory coast polygone
-    features = [data['features'][0]['geometry']['coordinates'][0]+data['features'][0]['geometry']['coordinates'][1]+data['features'][0]['geometry']['coordinates'][2]]
-    data['features'][0]['geometry']['coordinates'] = features
-    ci_polygone = data['features'][0]['geometry']['coordinates'][0][0]  
-    point1 = Point(x1)
-    point2 = Point(x2)
-    polygon = Polygon(ci_polygone)
-    result = polygon.contains(point1)
-
-    if not result:
-        return "Choisissez une zone de la CI","","","",""
-    
-    else:
-        df = pd.read_csv("data/frame.csv")
-        name = find_good_tile(df,point2)
-        if name ==404:
-            reponse = "Sentinel-2 ne dispose pas de données ce sur ce lieu à ce jour"
-            return reponse,"","","",""
-        else:
-            path = "https://data354-public-assets.s3.eu-west-3.amazonaws.com/cisentineldata/"
-            url = path+name
-            wget.download(url)
-            unzip()
-            name,cld_prob,days_ago = select_best_cloud_coverage_tile()
-            bandes_path_10,bandes_path_20,bandes_path_60,tile_path,path_cld_20,path_cld_60 =paths(name)
-            # create image dataset
-            images_10 = extract_sub_image(bandes_path_10,tile_path,cord)
-
-            ## bandes with 20m resolution
-            #path_cld_20
-            images_20 = extract_sub_image(bandes_path_20,tile_path,cord,20,1)
-
-            # bandes with 60m resolution
-            #path_cld_60
-            images_60 = extract_sub_image(bandes_path_60,tile_path,cord,60)
-            #
-            feature = images_10.tolist()+images_20.tolist()+images_60.tolist()
-            bands = ['B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B11', 'B12','B01','B09']
-            X = pd.DataFrame([feature],columns = bands)
-            # vegetation index calculation
-            X = indices(X)
-            # load the model from disk
-            filename = "data/new_version_model.sav"
-            loaded_model = pickle.load(open(filename, 'rb'))
-            # make prediction
-            biomass = loaded_model.predict(X)[0]
-            carbon = 0.55*biomass
-
-            # NDVI
-            ndvi_index = ndvi(cord,name)
-
-            # deleted download files
-            delete_tiles()
-
-            return str(cld_prob)+ " % cloud coverage", str(days_ago)+" days ago",str(biomass)+" Kg/ha", str(carbon)+" KgC/ha","NDVI: "+ str(ndvi_index)
-
-# Create title, description and article strings
-title = "🌴BEEPAS : Biomass estimation to Evaluate the Environmental Performance of Agroforestry Systems🌴"
-description = "This application estimates the biomass of certain areas using AI and satellite images (S2)."
-article = "Created by data354."
-
-# Create examples list from "examples/" directory
-#example_list = [["examples/" + example] for example in os.listdir("examples")]
-example_list = [["Foret du banco :",5.379913, -4.050445],["Pharmacie Y4 :",5.363292, -3.9481601],["Hotel ivoire :",5.316458, -4.017172],["Adjamé :",5.346938, -4.027849]]
-
-outputs = [
-    gr.Textbox(label="Cloud coverage"),
-    gr.Textbox(label="Number of days since sensing"),
-    gr.Textbox(label="Above ground biomass density(AGBD) Kg/ha"),
-    gr.Textbox(label="Carbon stock density KgC/ha "),
-    gr.Textbox(label="Mean NDVI"),]
-
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=["text","number", "number"],
-    outputs=outputs, #[ "text", "text","text","text","text"],
-    examples=example_list,
-    title=title,
-    description=description,
-    article=article,
-    )
-
-demo.launch(share=True)