Update app.py

app.py

@@ -3,30 +3,48 @@ from prophet import Prophet
 import gradio as gr
 import plotly.graph_objs as go
 import numpy as np
-
+import requests
 # Function to train the model and generate forecast
 def predict_sales(time_frame):
-    all_sales_data = pd.read_csv('All sales - House of Pizza.csv')
-
-    # Clean up the 'Total paid' column by splitting based on '₨' symbol and converting to float
-    def clean_total_paid(val):
-        if isinstance(val, str):  # Only process if the value is a string
-            amounts = [float(x.replace(',', '').strip()) for x in val.split('₨') if x.strip()]
-            return sum(amounts)  # Sum if multiple values exist
-        elif pd.isna(val):  # Handle NaN values
-            return 0.0
-        return val  # If it's already a float, return it as-is
-
-    # Apply the cleaning function to the 'Total paid' column
-    all_sales_data['Total paid'] = all_sales_data['Total paid'].apply(clean_total_paid)
+    #login
+    url="https://livesystem.hisabkarlay.com/auth/login"
+    payload={
+      'username':'user@123',
+      'password':'user@123',
+      'client_secret':'kNqJjlPkxyHdIKt3szCt4PYFWtFOdUheb8QVN8vQ',
+      'client_id':'5',
+      'grant_type':'password'
+    }
+    response=requests.post(url,data=payload)
+    #print(response.text)
+    access_token=response.json()['access_token']
+    #print(access_token)
+    #fetch all sell data
+    per_page=-1
+    url=f"https://livesystem.hisabkarlay.com/connector/api/sell?per_page={per_page}"
+    headers={
+      'Authorization':f'Bearer {access_token}'
+    }
+    response=requests.get(url,headers=headers)
+    data=response.json()['data']
+    date=[]
+    amount=[]
+    for item in data:
+      date.append(item.get('transaction_date'))
+      amount.append(float(item.get('final_total')))
+    data_dict={
+        'date':date,
+        'amount':amount
+    }
+    data_frame=pd.DataFrame(data_dict)
+    # Convert 'date' column to datetime format
+    data_frame['date'] = pd.to_datetime(data_frame['date'])
 
-    # Convert the 'Date' column to datetime, coercing errors
-    all_sales_data['Date'] = pd.to_datetime(all_sales_data['Date'], format='%m/%d/%Y %H:%M', errors='coerce')
+    # Extract only the date part
+    data_frame['date_only'] = data_frame['date'].dt.date
 
-    # Drop rows with invalid dates
-    all_sales_data = all_sales_data.dropna(subset=['Date'])
-    all_sales_data['date_only'] = all_sales_data['Date'].dt.date
-    daily_sales = all_sales_data.groupby('date_only').agg(total_sales=('Total paid', 'sum')).reset_index()
+    # Group by date and calculate total sales
+    daily_sales = data_frame.groupby('date_only').agg(total_sales=('amount', 'sum')).reset_index()
 
     # Prepare the DataFrame for Prophet
     df = pd.DataFrame({
@@ -121,7 +139,7 @@ def run_gradio():
         outputs=[
             gr.components.Dataframe(label="Forecasted Sales Table"),  # Forecasted data in tabular form
             gr.components.Dataframe(label="Weekend Forecasted Sales Table"),  # Weekend forecast data
-            gr.components.Plot(label="Sales Forecast Plot")  # Plotly graph output
+            gr.components.Plot(label="Sales Forecast Plot",min_width=500,scale=2)  # Plotly graph output
         ],
         title="Sales Forecasting with Prophet",
         description="Select a time range for the forecast and click on the button to train the model and see the results."