Update utils/model_inference.py

utils/model_inference.py

@@ -2,10 +2,30 @@ import numpy as np
 import pandas as pd
 from datetime import datetime
 import pytz
+import requests
 
-# Import your models and other necessary utilities here
+# Access token for ipinfo.io
+ACCESS_TOKEN = '37b621e95809fa'
 
-def generate_forex_signals(trading_capital, market_risk, user_timezone):
+# Function to get user timezone based on their IP address
+def get_user_timezone():
+    try:
+        # Get the public IP address of the user
+        response = requests.get(f'https://ipinfo.io?token={ACCESS_TOKEN}')
+        data = response.json()
+        
+        # Extract the timezone information from the response
+        user_timezone = data['timezone']
+        return user_timezone
+    except Exception as e:
+        print(f"Error fetching timezone: {e}")
+        return 'UTC'  # Fallback to UTC if there's an error
+
+# Function to generate forex signals
+def generate_forex_signals(trading_capital, market_risk):
+    # Get the user's timezone based on their IP address
+    user_timezone = get_user_timezone()
+    
     # Ensure the user timezone is valid
     try:
         user_tz = pytz.timezone(user_timezone)
@@ -13,7 +33,6 @@ def generate_forex_signals(trading_capital, market_risk, user_timezone):
         raise ValueError("Invalid timezone entered. Please check the format.")
     
     # Example of how you might process trading capital and risk level:
-    # Assume this logic is based on the user input for market risk
     risk_level = {'Low': 0.01, 'Medium': 0.03, 'High': 0.05}
     
     if market_risk not in risk_level:
@@ -21,14 +40,13 @@ def generate_forex_signals(trading_capital, market_risk, user_timezone):
     
     risk_percentage = risk_level[market_risk]
     
-    # Perform model inference based on the user's inputs:
-    # For example, load the model and predict
-    # signal = model.predict(features)
-    
     # Dummy signal generation (Replace with your model inference logic)
     currency_pair = "EUR/USD"
-    entry_time = datetime.now(user_tz).strftime("%Y-%m-%d %H:%M:%S")
-    exit_time = (datetime.now(user_tz) + pd.Timedelta(hours=2)).strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Get current time in the user's timezone and format it
+    entry_time = datetime.now(user_tz).strftime("%Y-%m-%d %I:%M:%S %p")
+    exit_time = (datetime.now(user_tz) + pd.Timedelta(hours=2)).strftime("%Y-%m-%d %I:%M:%S %p")
+    
     roi = np.random.uniform(5, 15)  # Random ROI between 5% and 15%
     signal_strength = np.random.uniform(0.7, 1.0)  # Random strength between 0.7 and 1.0