first

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

+main.py
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="C:\Users\ramya.r\Anaconda3 (2)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/task3.iml" filepath="$PROJECT_DIR$/.idea/task3.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

.idea/task3.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

conf/application.conf

+[SERVICE]
+
+port=8000
+host=0.0.0.0
\ No newline at end of file

main.py

+import uvicorn
+from fastapi import FastAPI
+from dotenv import load_dotenv
+import script
+
+load_dotenv()
+
+from script.config.app_config import Service
+from script.service.weather import router
+
+app = FastAPI(title="Weather")
+app.include_router(router)
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host=script.config.app_config.Service.host, port=script.config.app_config.Service.port)

script/config/app_config.py

+from configparser import SafeConfigParser
+config = SafeConfigParser()
+config.read('conf/application.conf')
+
+class Service:
+    port = int(config.getint("SERVICE", "port"))
+    host = config.get("SERVICE", "host")

script/constant/__init__.py

+class APIEndpoints:
+    report_base= "/report_base"
\ No newline at end of file

script/core/errors/__init__.py

+class NameDoesNotExist(Exception):
+    pass
\ No newline at end of file

script/core/handlers/weather_connection.py

+import smtplib
+from typing import Optional
+
+import pandas as pd
+import ssl
+from email.message import EmailMessage
+from email.mime.multipart import MIMEMultipart
+
+from openpyxl.utils.datetime import to_excel
+
+
+class WeatherReport:
+
+    @staticmethod
+    def daily_data(df, date):
+        try:
+
+            df[['Date', 'Time']] = df['Formatted Date'].str.split(' ', n=1, expand=True)
+            filtered_df = df[df['Date'] == date]
+            condition = "daily"
+
+            # Temperature
+            min_temp = filtered_df['Temperature (C)'].min(axis=0)
+            max_temp = filtered_df['Temperature (C)'].max(axis=0)
+            avg_temp = filtered_df['Temperature (C)'].mean(axis=0)
+
+            # Apparent Temperature
+            min_at = filtered_df['Apparent Temperature (C)'].min(axis=0)
+            max_at = filtered_df['Apparent Temperature (C)'].max(axis=0)
+            avg_at = filtered_df['Apparent Temperature (C)'].mean(axis=0)
+
+            # Humidity
+            min_hum = filtered_df['Humidity'].min(axis=0)
+            max_hum = filtered_df['Humidity'].max(axis=0)
+            avg_hum = filtered_df['Humidity'].mean(axis=0)
+
+            # Wind Speed
+            min_ws = filtered_df['Wind Speed (km/h)'].min(axis=0)
+            max_ws = filtered_df['Wind Speed (km/h)'].max(axis=0)
+            avg_ws = filtered_df['Wind Speed (km/h)'].mean(axis=0)
+
+            # Wind Bearing
+            min_wb = filtered_df['Wind Bearing (degrees)'].min(axis=0)
+            max_wb = filtered_df['Wind Bearing (degrees)'].max(axis=0)
+            avg_wb = filtered_df['Wind Bearing (degrees)'].mean(axis=0)
+
+            # Visibility
+            min_vis = filtered_df['Visibility (km)'].min(axis=0)
+            max_vis = filtered_df['Visibility (km)'].max(axis=0)
+            avg_vis = filtered_df['Visibility (km)'].mean(axis=0)
+
+            # Pressure
+            min_pre = filtered_df['Pressure (millibars)'].min(axis=0)
+            max_pre = filtered_df['Pressure (millibars)'].max(axis=0)
+            avg_pre = filtered_df['Pressure (millibars)'].mean(axis=0)
+
+            value_return = {"date": date,
+
+                            "Minimum": {"Temperature": round(min_temp, 2), "Apparent Temperature": round(min_at, 2),
+                                        "Humidity": round(min_hum, 2),
+                                        "Wind Speed": round(min_ws, 2), "Wind Bearing": round(min_wb, 2),
+                                        "Visibility": round(min_vis, 2),
+                                        "Pressure": round(min_pre, 2)},
+
+                            "Maximum": {"Temperature": round(max_temp, 2), "Apparent Temperature": round(max_at, 2),
+                                        "Humidity": round(max_hum, 2),
+                                        "Wind Speed": round(max_ws, 2), "Wind Bearing": round(max_wb, 2),
+                                        "Visibility": round(max_vis, 2),
+                                        "Pressure": round(max_pre, 2)},
+
+                            "Average": {"Temperature": round(avg_temp, 2), "Apparent Temperature": round(avg_at, 2),
+                                        "Humidity": round(avg_hum, 2),
+                                        "Wind Speed": round(avg_ws, 2), "Wind Bearing": round(avg_wb, 2),
+                                        "Visibility": round(avg_vis, 2),
+                                        "Pressure": round(avg_pre, 2)}}
+            new_data = value_return
+            WeatherReport.get_excel(condition, value_return)
+            return new_data
+        except ValueError:
+            raise ValueError
+        except Exception as e:
+            print(e, "Error Detected")
+
+    @staticmethod
+    def weekly_data(df, start_date, end_date):
+        try:
+
+            df[["date", "time"]] = df["Formatted Date"].str.split(" ", n=1, expand=True)
+            df["date"] = pd.to_datetime(df["date"])
+            start_date = pd.to_datetime(start_date)
+            end_date = pd.to_datetime(end_date)
+            filtered_df = df.loc[(df["date"] >= start_date) & (df["date"] <= end_date)]
+            condition = "weekly"
+
+            # Temperature
+            min_temp = filtered_df['Temperature (C)'].min(axis=0)
+            max_temp = filtered_df['Temperature (C)'].max(axis=0)
+            avg_temp = filtered_df['Temperature (C)'].mean(axis=0)
+
+            # Apparent Temperature
+            min_at = filtered_df['Apparent Temperature (C)'].min(axis=0)
+            max_at = filtered_df['Apparent Temperature (C)'].max(axis=0)
+            avg_at = filtered_df['Apparent Temperature (C)'].mean(axis=0)
+
+            # Humidity
+            min_hum = filtered_df['Humidity'].min(axis=0)
+            max_hum = filtered_df['Humidity'].max(axis=0)
+            avg_hum = filtered_df['Humidity'].mean(axis=0)
+
+            # Wind Speed
+            min_ws = filtered_df['Wind Speed (km/h)'].min(axis=0)
+            max_ws = filtered_df['Wind Speed (km/h)'].max(axis=0)
+            avg_ws = filtered_df['Wind Speed (km/h)'].mean(axis=0)
+
+            # Wind Bearing
+            min_wb = filtered_df['Wind Bearing (degrees)'].min(axis=0)
+            max_wb = filtered_df['Wind Bearing (degrees)'].max(axis=0)
+            avg_wb = filtered_df['Wind Bearing (degrees)'].mean(axis=0)
+
+            # Visibility
+            min_vis = filtered_df['Visibility (km)'].min(axis=0)
+            max_vis = filtered_df['Visibility (km)'].max(axis=0)
+            avg_vis = filtered_df['Visibility (km)'].mean(axis=0)
+
+            # Pressure
+            min_pre = filtered_df['Pressure (millibars)'].min(axis=0)
+            max_pre = filtered_df['Pressure (millibars)'].max(axis=0)
+            avg_pre = filtered_df['Pressure (millibars)'].mean(axis=0)
+
+            value_return = {"date": start_date,
+                            "date": end_date,
+
+                            "Minimum": {"Temperature": round(min_temp, 2), "Apparent Temperature": round(min_at, 2),
+                                        "Humidity": round(min_hum, 2),
+                                        "Wind Speed": round(min_ws, 2), "Wind Bearing": round(min_wb, 2),
+                                        "Visibility": round(min_vis, 2),
+                                        "Pressure": round(min_pre, 2)},
+
+                            "Maximum": {"Temperature": round(max_temp, 2), "Apparent Temperature": round(max_at, 2),
+                                        "Humidity": round(max_hum, 2),
+                                        "Wind Speed": round(max_ws, 2), "Wind Bearing": round(max_wb, 2),
+                                        "Visibility": round(max_vis, 2),
+                                        "Pressure": round(max_pre, 2)},
+
+                            "Average": {"Temperature": round(avg_temp, 2), "Apparent Temperature": round(avg_at, 2),
+                                        "Humidity": round(avg_hum, 2),
+                                        "Wind Speed": round(avg_ws, 2), "Wind Bearing": round(avg_wb, 2),
+                                        "Visibility": round(avg_vis, 2),
+                                        "Pressure": round(avg_pre, 2)}}
+
+            new_data = value_return
+            WeatherReport.get_excel(condition, value_return)
+            return new_data
+        except ValueError:
+            raise ValueError
+        except Exception as e:
+            print(e, "Error Detected")
+
+    @staticmethod
+    def monthly_data(df, year, month):
+        try:
+
+            df[["date", "time"]] = df["Formatted Date"].str.split(" ", n=1, expand=True)
+            df["date"] = pd.to_datetime(df["date"])
+            df["date"] = pd.to_datetime(df["date"])
+            filtered_df = df.loc[(df["date"].dt.year == year) & (df["date"].dt.month == month)]
+            filtered_df = filtered_df.drop(columns=["time"])
+            condition = "monthly"
+
+
+            # Temperature
+            min_temp = filtered_df['Temperature (C)'].min(axis=0)
+            max_temp = filtered_df['Temperature (C)'].max(axis=0)
+            avg_temp = filtered_df['Temperature (C)'].mean(axis=0)
+
+            # Apparent Temperature
+            min_at = filtered_df['Apparent Temperature (C)'].min(axis=0)
+            max_at = filtered_df['Apparent Temperature (C)'].max(axis=0)
+            avg_at = filtered_df['Apparent Temperature (C)'].mean(axis=0)
+
+            # Humidity
+            min_hum = filtered_df['Humidity'].min(axis=0)
+            max_hum = filtered_df['Humidity'].max(axis=0)
+            avg_hum = filtered_df['Humidity'].mean(axis=0)
+
+            # Wind Speed
+            min_ws = filtered_df['Wind Speed (km/h)'].min(axis=0)
+            max_ws = filtered_df['Wind Speed (km/h)'].max(axis=0)
+            avg_ws = filtered_df['Wind Speed (km/h)'].mean(axis=0)
+
+            # Wind Bearing
+            min_wb = filtered_df['Wind Bearing (degrees)'].min(axis=0)
+            max_wb = filtered_df['Wind Bearing (degrees)'].max(axis=0)
+            avg_wb = filtered_df['Wind Bearing (degrees)'].mean(axis=0)
+
+            # Visibility
+            min_vis = filtered_df['Visibility (km)'].min(axis=0)
+            max_vis = filtered_df['Visibility (km)'].max(axis=0)
+            avg_vis = filtered_df['Visibility (km)'].mean(axis=0)
+
+            # Pressure
+            min_pre = filtered_df['Pressure (millibars)'].min(axis=0)
+            max_pre = filtered_df['Pressure (millibars)'].max(axis=0)
+            avg_pre = filtered_df['Pressure (millibars)'].mean(axis=0)
+
+            value_return = {"month": month,
+                            "year": year,
+
+                            "Minimum": {"Temperature": round(min_temp, 2), "Apparent Temperature": round( min_at, 2), "Humidity": round(min_hum, 2),
+                                        "Wind Speed": round(min_ws, 2), "Wind Bearing": round(min_wb, 2), "Visibility": round (min_vis, 2),
+                                        "Pressure": round(min_pre, 2)},
+
+                            "Maximum": {"Temperature": round(max_temp, 2), "Apparent Temperature": round(max_at, 2), "Humidity" : round(max_hum, 2),
+                                        "Wind Speed": round(max_ws, 2), "Wind Bearing": round(max_wb, 2), "Visibility": round (max_vis, 2),
+                                        "Pressure": round(max_pre, 2)},
+
+                            "Average": {"Temperature": round(avg_temp, 2), "Apparent Temperature": round(avg_at, 2),  "Humidity": round(avg_hum, 2),
+                                        "Wind Speed": round(avg_ws, 2), "Wind Bearing": round(avg_wb, 2), "Visibility": round(avg_vis, 2),
+                                        "Pressure": round(avg_pre, 2)}}
+            new_data = value_return
+            WeatherReport.get_excel(condition, value_return)
+            return new_data
+        except ValueError:
+            raise ValueError
+        except Exception as e:
+            print(e, "Error Detected")
+
+    @staticmethod
+    def get_excel(condition, value_return):
+        try:
+            df = pd.DataFrame(value_return)
+            df.to_excel(f"Weather Report_{condition}.xlsx", index=True)
+            return "Dictionary converted into excel"
+        except ValueError:
+            raise ValueError
+        except Exception as e:
+            print(e, "Excel Error Detected")
+
+    @staticmethod
+    def send_file(sender, receiver, sender_password):
+        try:
+            files = ['Weather Report_daily.xlsx', 'Weather Report_weekly.xlsx', 'Weather Report_monthly.xlsx']
+            msg = EmailMessage()
+            msg['From'] = sender
+            msg['To'] = receiver
+            msg['Subject'] = "weather report"
+            context = ssl.create_default_context()
+
+            for file_read in files:
+                with open(file_read, "rb") as file:
+                    file_data = file.read()
+                    file_name = file.name
+                    print(file_name)
+                    msg.add_attachment(file_data, maintype="application", subtype="xlsx", filename=file_name)
+            with smtplib.SMTP_SSL('smtp.gmail.com', 465, context=context) as smtp:
+                smtp.login(sender, sender_password)
+                smtp.sendmail(sender, receiver, msg.as_string())
+        except ValueError:
+            raise ValueError
+        except Exception as e:
+            print(e, "Excel Error Detected")
+
+
+

script/core/schema/report.py

+from numba import optional
+from pydantic import BaseModel
+
+
+# define a model for Item
+class Weather(BaseModel):
+    minimum = optional[int]
+    maximum = optional[int]
+    average = optional[int]

script/core/schema/response.py

+from typing import Any, Optional
+from pydantic import BaseModel
+
+class DefaultResponse(BaseModel):
+    status: str = "failed"
+    message: str
+    data: Optional[Any]
+

script/service/weather.py

+from fastapi import FastAPI, File, UploadFile, APIRouter
+import pandas as pd
+from io import StringIO
+import logging
+from script.constant import APIEndpoints
+from script.core.handlers.weather_connection import WeatherReport
+from script.core.schema.response import DefaultResponse
+
+router = APIRouter(prefix=APIEndpoints.report_base)
+
+data = None
+
+
+@router.post("/upload")
+def upload_file(file: UploadFile = File(...)):
+    contents = file.file.read()
+    s = str(contents, 'utf-8')
+    raw_data = StringIO(s)
+    global data
+    data = pd.read_csv(raw_data)
+    raw_data.close()
+    file.file.close()
+    return {"filename": file.filename}
+
+
+@router.get("/daily")
+def daily_data(date: str):
+    try:
+        global data
+        op = WeatherReport.daily_data(data, date)
+        return DefaultResponse(message="Successfully found", status="success", data=op)
+    except ValueError:
+        return DefaultResponse(message="Due to Value Error")
+    except Exception as e:
+        logging.exception(e)
+        return DefaultResponse(message="Finding failed due to server error")
+
+
+@router.get("/weekly")
+def weekly_data(start_date: str, end_date: str):
+    try:
+        global data
+        op = WeatherReport.weekly_data(data, start_date, end_date)
+        return DefaultResponse(message="Successfully found", status="success", data=op)
+    except ValueError:
+        return DefaultResponse(message="Due to Value Error")
+    except Exception as e:
+        logging.exception(e)
+        return DefaultResponse(message="Finding failed due to server error")
+
+
+@router.get("/monthly")
+def monthly_data(year: int, month: int):
+    try:
+        global data
+        op = WeatherReport.monthly_data(data, year, month)
+        return DefaultResponse(message="Successfully found", status="success", data=op)
+    except ValueError:
+        return DefaultResponse(message="Due to Value Error")
+    except Exception as e:
+        logging.exception(e)
+        return DefaultResponse(message="Finding failed due to server error")
+
+
+@router.get("/email")
+def get_email(sender: str, receiver: str, password: str):
+    try:
+        WeatherReport.send_file(sender, receiver, password)
+        return DefaultResponse(message="Successfully found", status="success")
+    except ValueError:
+        return DefaultResponse(message="Due to Value Error")
+    except Exception as e:
+        logging.exception(e)
+        return DefaultResponse(message="Finding failed due to server error")
+