Update app.py

app.py

@@ -18,6 +18,8 @@ import categories
 from categories import Category
 from main import process_image, process_pdf
 
+from forex_python.converter import CurrencyRates
+
 HF_TOKEN = os.getenv("HF_TOKEN")
 PDF_IFRAME = """
 <div style="border-radius: 10px; width: 100%; overflow: hidden;">
@@ -109,6 +111,52 @@ def extract_text(input_file):
     return text
 
 
+def find_currency_symbol(text):
+    currency_symbols = {
+        'USD': ['$', 'US$', 'US Dollar', 'United States Dollar'],
+        'EUR': ['€', 'Euro'],
+        'GBP': ['£', 'British Pound', 'Pound Sterling'],
+        'JPY': ['¥', 'Japanese Yen'],
+        'AUD': ['A$', 'AU$', 'Australian Dollar'],
+        'CAD': ['C$', 'CA$', 'Canadian Dollar'],
+        'CHF': ['Swiss Franc'],
+        'CNY': ['CN¥', 'Chinese Yuan', 'Renminbi'],
+        'HKD': ['HK$', 'Hong Kong Dollar'],
+        'NZD': ['NZ$', 'New Zealand Dollar'],
+        'SEK': ['Swedish Krona'],
+        'KRW': ['₩', 'South Korean Won'],
+        'SGD': ['S$', 'Singapore Dollar'],
+        'NOK': ['Norwegian Krone'],
+        'MXN': ['Mexican Peso'],
+        'INR': ['₹', 'Indian Rupee'],
+        'RUB': ['₽', 'Russian Ruble'],
+        'ZAR': ['South African Rand'],
+        'BRL': ['R$', 'Brazilian Real'],
+    }
+    
+    detected_currency = None
+    
+    for currency, symbols in currency_symbols.items():
+        for symbol in symbols:
+            if symbol in text:
+                detected_currency = currency
+                break
+        if detected_currency:
+            break
+    
+    return detected_currency
+
+def get_exchange_rate_to_inr(currency):
+    c = CurrencyRates()
+    if currency == 'INR' or currency == None:
+        return 1 
+    else:
+        try:
+            exchange_rate = c.get_rate(currency, 'INR')
+            return exchange_rate
+        except:
+            return None
+
 def categorize_text(text):
     """Takes the extracted text and updates the category"""
     category = categories.categorize_text(text)
@@ -241,6 +289,7 @@ def process_and_output_files(input_files):
     for file in input_files:
         # Extract and categorize text for each file
         text = extract_text(file)
+        currency = find_currency_symbol(text)
         category = categorize_text(text)
 
         chatbot_response = query(category, text)  # Convert the generator to a list
@@ -256,6 +305,9 @@ def process_and_output_files(input_files):
         #     chats[1]["value"][0][1] ,
         # )
 
+        exchange_rate = get_exchange_rate_to_inr(currency)
+        exchange_rate = float("{:.2f}".format(exchange_rate))
+        
         response_dict = json.loads(chats[1]["value"][0][1])
         if category.name == "TRAVEL_CAB" : 
         # Extract the relevant data
@@ -265,7 +317,7 @@ def process_and_output_files(input_files):
                 "Nature of Expenditure": response_dict.get("summary"),
                 "Billing Date": response_dict.get("issue_date"),
                 "Bill/Invoice No.": "NA",
-                "Amount(Rs.)": response_dict.get("total"),
+                "Amount(Rs.)": response_dict.get("total") * exchange_rate,
     
             }
         else:
@@ -274,12 +326,12 @@ def process_and_output_files(input_files):
                 "Nature of Expenditure": response_dict.get("summary"),
                 "Billing Date": response_dict.get("issue_date"),
                 "Bill/Invoice No.": response_dict.get("uids"),
-                "Amount(Rs.)": response_dict.get("total")
+                "Amount(Rs.)": response_dict.get("total") * exchange_rate
                 
             }
             
 
-        total_amount+=response_dict.get("total")
+        total_amount+=response_dict.get("total") * exchange_rate
 
         # Append the relevant data for this file to the data list
         data.append(extracted_data)