Update app.py

app.py

@@ -258,65 +258,82 @@ def load_previous_user_request_from_github():
 
 
 def get_reference_message(gender, generation, psychotype, business_stage, industry, legal_form):
-    # Import io if not already done
     import io
-    
-    # Define the repository and file path
+
     repo = "fruitpicker01/Storage_dev"
-    file_path = "messages.csv"  # Or 'messages.xlsx' if you prefer Excel
-    
-    # Get the file from the repository
+    file_path = "messages.csv"
+
     url = f"https://api.github.com/repos/{repo}/contents/{file_path}"
     headers = {
         "Authorization": f"token {token}",
         "Content-Type": "application/json"
     }
-    
+
     response = requests.get(url, headers=headers)
-    
+
     if response.status_code == 200:
-        # File exists, download and load it
         content = response.json()
         file_content = base64.b64decode(content['content'])
-        if file_path.endswith('.xlsx'):
-            df = pd.read_excel(io.BytesIO(file_content))
-        else:
-            df = pd.read_csv(io.StringIO(file_content.decode('utf-8')))
+        df = pd.read_csv(io.StringIO(file_content.decode('utf-8')))
     else:
-        # File does not exist or error
         print(f"Error accessing the file: {response.status_code}")
         return None
-    
-    # Filter the DataFrame based on the personalization parameters
-    filter_condition = (
-        (df["Пол"] == gender) &
-        (df["Поколение"] == generation) &
-        (df["Психотип"] == psychotype) &
-        (df["Стадия бизнеса"] == business_stage) &
-        (df["Отрасль"] == industry) &
-        (df["ОПФ"] == legal_form)
-    )
-    
+
+    # Normalize the DataFrame columns
+    for col in ["Пол", "Поколение", "Психотип", "Стадия бизнеса", "Отрасль", "ОПФ"]:
+        df[col] = df[col].astype(str).str.strip().str.lower()
+
+    # Normalize the input parameters
+    params = {
+        "Пол": str(gender).strip().lower() if gender else None,
+        "Поколение": str(generation).strip().lower() if generation else None,
+        "Психотип": str(psychotype).strip().lower() if psychotype else None,
+        "Стадия бизнеса": str(business_stage).strip().lower() if business_stage else None,
+        "Отрасль": str(industry).strip().lower() if industry else None,
+        "ОПФ": str(legal_form).strip().lower() if legal_form else None
+    }
+
+    # Debugging: Print DataFrame and parameters
+    print("DataFrame columns:", df.columns)
+    print("DataFrame head:\n", df.head())
+    print("Personalization parameters:", params)
+
+    # Build the filter conditions dynamically
+    filter_conditions = []
+    for col, value in params.items():
+        if value and value.lower() != 'none':
+            filter_conditions.append(df[col] == value)
+
+    if not filter_conditions:
+        print("No personalization parameters provided.")
+        return None
+
+    # Combine filter conditions
+    filter_condition = filter_conditions[0]
+    for condition in filter_conditions[1:]:
+        filter_condition &= condition
+
     filtered_df = df[filter_condition]
-    
+
     if filtered_df.empty:
-        # No previous messages with these parameters
+        print("No messages found with the given personalization parameters.")
         return None
-    
-    # Sort the filtered DataFrame by timestamp in descending order
+
+    # Sort by Timestamp descending
     filtered_df = filtered_df.sort_values(by="Timestamp", ascending=False)
-    
+
     # Get the latest message
     latest_row = filtered_df.iloc[0]
-    
-    # Get the "Откорректированное сообщение" if it exists, else "Персонализированное сообщение"
-    if pd.notnull(latest_row["Откорректированное сообщение"]) and latest_row["Откорректированное сообщение"].strip():
+
+    # Choose the appropriate message
+    if pd.notnull(latest_row.get("Откорректированное сообщение", None)) and latest_row["Откорректированное сообщение"].strip():
         reference_message = latest_row["Откорректированное сообщение"]
     else:
-        reference_message = latest_row["Персонализированное сообщение"]
-    
+        reference_message = latest_row.get("Персонализированное сообщение", "")
+
     return reference_message
 
+
 def adapt_messages_to_best_example(
     personalized_gigachat_pro,
     personalized_gigachat_lite,