Update app.py

Prompting tuning for interviewer

app.py

@@ -1,63 +1,53 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import random
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
+INTERVIEWER_PROMPT = """
+You are a job interviewer. Your task is to ask the candidate (the user) a series of behavioral interview questions, one at a time. 
+After each candidate response, you should:
+1. Briefly acknowledge their answer.
+2. Ask a new, different behavioral interview question.
+Make your questions specific and varied. Do not repeat questions.
+Do not provide feedback on their answers or make hiring decisions.
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
+
+def generate_question(history):
+    messages = [
+        {"role": "system", "content": INTERVIEWER_PROMPT},
+        {"role": "user", "content": "Let's start the interview. Please ask me the first question."}
+    ]
+    
+    # Add the conversation history
+    for human, ai in history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": ai})
+    
+    # Add a prompt for a new question
+    messages.append({"role": "user", "content": "Please ask the next interview question."})
+    
+    response = client.chat_completion(messages, max_tokens=150, temperature=0.7)
+    return response.choices[0].message.content
+
+def respond(message, history):
+    if not history:
+        # First interaction: generate the first question
+        yield generate_question([])
+    else:
+        # Acknowledge the user's answer
+        acknowledgement = "Thank you for your response. "
+        yield acknowledgement
+        
+        # Generate and ask a new question
+        new_question = generate_question(history)
+        yield acknowledgement + new_question
+
+iface = gr.ChatInterface(
     respond,
-    additional_inputs=[
-        gr.Textbox(value= 'You are a job interviewer. Throughout this conversation, you will ask me a series of questions, one question at a time.Your goal is to assess my behavioral competence for the position. Come up with a creative way to challenge me to evaluate how I solve problems. The answer to the question should be a prompt (NOT A TYPICAL JOB INTERVIEW QUESTION). The question should be related to behavioral interview questions. Make your question to me require a very specific answer.', label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    title="Job Interview Simulator",
+    description="I'm your job interviewer today. I'll ask you behavioral questions one at a time. Let's begin!",
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()