Update app.py

app.py

@@ -6,17 +6,6 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Predefined list of interview questions
-interview_questions = [
-    "Can you tell me about yourself?",
-    "Why are you interested in this position?",
-    "What are your strengths and weaknesses?",
-    "Can you describe a challenging work situation and how you handled it?",
-    "Where do you see yourself in five years?",
-]
-
-# Keep track of the current question index
-question_index = 0
 
 def respond(
     message,
@@ -26,8 +15,6 @@ def respond(
     temperature,
     top_p,
 ):
-    global question_index
-
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -36,11 +23,10 @@ def respond(
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
-    # Add the user's latest message
     messages.append({"role": "user", "content": message})
 
-    # Generate the assistant's response
     response = ""
+
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -49,20 +35,10 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
+
         response += token
         yield response
 
-    # Prepare the next question if there are more questions left
-    if question_index < len(interview_questions):
-        next_question = interview_questions[question_index]
-        question_index += 1
-    else:
-        next_question = "Thank you for answering all the questions. Do you have any questions for me?"
-
-    # Append the response to the history and add the next question
-    history.append((message, response))
-    yield response + "\n\n" + next_question
-
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
@@ -82,5 +58,6 @@ demo = gr.ChatInterface(
     ],
 )
 
+
 if __name__ == "__main__":
     demo.launch()