Update excel_chat.py

excel_chat.py

@@ -12,7 +12,7 @@ import zipfile
 
 #users = ['maksG', 'AlmaA', 'YchK']
 
-def ask_llm(query, user_input, client_index, user):
+def ask_llm(query, user_input, client_index, user, keys):
     messages = [
         {
             "role": "system",
@@ -58,11 +58,21 @@ def ask_llm(query, user_input, client_index, user):
         return response.content[0].text
 
     elif client_index == "Groq (mixtral)":
-        client = Groq(api_key=os.environ[user['api_keys']['groq']])
-        chat_completion = client.chat.completions.create(
-            messages=messages,
-            model='mixtral-8x7b-32768',
-        )
+        try:
+            client = Groq(api_key=os.environ[user['api_keys'][keys[0]]])
+            chat_completion = client.chat.completions.create(
+                messages=messages,
+                model='mixtral-8x7b-32768',
+            )
+            response = chat_completion.choices[0].message.content
+        except Exception as e:
+            print("Change key")
+            if keys[0] == keys[1][0]:
+                keys[0] = keys[1][1]
+            elif keys[0] == keys[1][1]:
+                keys[0] = keys[1][2]
+            else:
+                keys[0] = keys[1][0]
 
     else:
         raise ValueError("Unsupported client index provided")
@@ -86,6 +96,10 @@ def filter_df(df, column_name, keywords):
     return filtered_df
 
 def chat_with_mistral(source_cols, dest_col, prompt, excel_file, url, search_col, keywords, client, user):
+    # API Keys for Groq :
+    KEYS = ['groqkey', 'groqkey2', 'groqkey3']
+    GroqKey = KEYS[0]
+    
     new_prompts, new_keywords, new_user, conf_file_path = update_json(user, prompt, keywords)
     print(f'xlsxfile = {excel_file}')
     df = pd.read_excel(excel_file)
@@ -105,7 +119,7 @@ def chat_with_mistral(source_cols, dest_col, prompt, excel_file, url, search_col
     for index, row in filtred_df.iterrows():
         concatenated_content = "\n\n".join(f"{column_name}: {str(row[column_name])}" for column_name in source_cols)
         if not concatenated_content == "\n\n".join(f"{column_name}: nan" for column_name in source_cols):
-            llm_answer = ask_llm(prompt[0], concatenated_content, client, user)
+            llm_answer = ask_llm(prompt[0], concatenated_content, client, user, [GroqKey, KEYS])
             print(f"{cpt}/{len(filtred_df)}\nQUERY:\n{prompt[0]}\nCONTENT:\n{concatenated_content[:200]}...\n\nANSWER:\n{llm_answer}")
             df.at[index, dest_col] = llm_answer
             cpt += 1