Update helper.py

helper.py

@@ -4,21 +4,29 @@ from typing import Callable, Generator
 import base64
 from openai import OpenAI
 
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv())
+
+END_POINT = os.environ.get("ENDPOINT")
+SECRET_KEY = os.environ.get("SECRETKEY")
+USERS = os.environ.get("USERS")
+PWD = os.environ.get("PWD")
+
 def get_fn(model_name: str, **model_kwargs) -> Callable:
     """Create a chat function with the specified model."""
     
     # Instantiate an OpenAI client for a custom endpoint
     try:
         client = OpenAI(
-            base_url="http://192.222.58.60:8000/v1", 
-            api_key="tela",  # Replace with your actual API key or use environment variables
+            base_url=END_POINT,
+            api_key=SECRET_KEY,  
         )
     except Exception as e:
         print(f"The API or base URL were not defined: {str(e)}")
-        raise e  # Prevent the app from running without a client
+        raise e  
 
     def predict(
-        messages: list,  # Preprocessed messages from preprocess function
+        messages: list,  
         temperature: float,
         max_tokens: int,
         top_p: float
@@ -32,12 +40,10 @@ def get_fn(model_name: str, **model_kwargs) -> Callable:
                 max_tokens=max_tokens,
                 top_p=top_p,
                 stream=True, 
-                # Ensure response_format is set correctly; typically it's a string like 'text'
                 response_format={"type": "text"},
             )
 
             response_text = ""
-            # Iterate over the streaming response
             for chunk in response:
                 if len(chunk.choices[0].delta.content) > 0:
                     content = chunk.choices[0].delta.content
@@ -100,28 +106,27 @@ def get_interface_args(pipeline: str):
         postprocess = lambda x: x  # No additional postprocessing needed
 
     else:
-        # Add other pipeline types when they are needed
         raise ValueError(f"Unsupported pipeline type: {pipeline}")
     return inputs, outputs, preprocess, postprocess
 
 def registry(name: str = None, **kwargs) -> gr.ChatInterface:
     """Create a Gradio Interface with similar styling and parameters."""
     
-    # Retrieve preprocess and postprocess functions
+    # Retrieving preprocess and postprocess functions
     _, _, preprocess, postprocess = get_interface_args("chat")
     
-    # Get the predict function
+    # Getting the predict function
     predict_fn = get_fn(model_name=name, **kwargs)
     
-    # Define a wrapper function that integrates preprocessing and postprocessing
+    # Defining a wrapper function that integrates preprocessing and postprocessing
     def wrapper(message, history, system_prompt, temperature, max_tokens, top_p):
-        # Preprocess the inputs
+        # Preprocessing the inputs
         preprocessed = preprocess(message, history)
         
-        # Extract the preprocessed messages
+        # Extracting the preprocessed messages
         messages = preprocessed["messages"]
         
-        # Call the predict function and generate the response
+        # Calling the predict function and generate the response
         response_generator = predict_fn(
             messages=messages,
             temperature=temperature,
@@ -129,13 +134,13 @@ def registry(name: str = None, **kwargs) -> gr.ChatInterface:
             top_p=top_p
         )
         
-        # Collect the generated response
+        # Collecting the generated response
         response = ""
         for partial_response in response_generator:
             response = partial_response  # Gradio will handle streaming
             yield response
 
-    # Create the Gradio ChatInterface with the wrapper function
+    # Creating the Gradio ChatInterface with the wrapper function
     interface = gr.ChatInterface(
         fn=wrapper,
         additional_inputs_accordion=gr.Accordion("⚙️ Parameters", open=False),