Update app.py

app.py

@@ -1,36 +1,79 @@
 import gradio as gr
 import openai
+from huggingface_hub import InferenceClient
 
 # Replace with your OpenAI API key
 api_key = "sk-proj-c5tQOfP9FQbtQvnkg6DXT3BlbkFJXURwbOL7LUwivG7cGAtj"
 openai.api_key = api_key
 
-def chatbot(input_text):
-    prompt = "The following interview dialogue is a mock-up scenario:\n" \
-             "Interviewer: Tell me about yourself.\n" \
-             f"Candidate: {input_text}\n" \
-             "Interviewer: Why do you want this job?\n" \
-             "Candidate: (Your response)\n" \
-             "Interviewer: (Follow-up question)\n" \
-             "Candidate: (Your response)\n" \
-             "Interviewer: (Closing remarks)\n" \
-             "Candidate: (Your response)\n"
-
-    response = openai.Completion.create(
-        engine="text-davinci-002",
-        prompt=prompt,
-        max_tokens=150
-    )
-    
-    return response.choices[0].text.strip()
-
-iface = gr.Interface(
-    fn=chatbot,
-    inputs=gr.Textbox(lines=5, label="Candidate's Response"),
-    outputs=gr.Textbox(label="Interviewer's Question"),
-    title="Mock Interview Chatbot",
-    description="This chatbot simulates a mock interview scenario. Enter your response as a candidate and see the interviewer's question.",
-    theme="compact"
+# Hugging Face model client
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Initial welcome message and instructions
+welcome_message = (
+    "Welcome! This GPT acts as an interviewer for candidates from various fields. "
+    "Please choose a stream/category to begin the interview."
+)
+
+# Track interview questions and candidate information
+interview_history = []
+
+def chatbot(message, system_message, max_tokens, temperature, top_p):
+    global interview_history
+    
+    # Initialize messages
+    messages = [{"role": "system", "content": system_message}]
+    
+    # Append user message to history
+    interview_history.append((message, ""))
+    
+    # Check if candidate has selected a stream/category
+    if len(interview_history) == 1:
+        system_message = welcome_message
+    elif len(interview_history) == 2:
+        system_message = f"Great choice! Let's start asking questions related to {message}."
+    elif len(interview_history) > 10:
+        system_message = "We have asked enough questions. What are your expectations and notice period?"
+    
+    # Append system message to history
+    interview_history[-1] = (message, system_message)
+    
+    # Append user message to history
+    interview_history.append((message, ""))
+    
+    # Check if we have asked enough questions
+    if len(interview_history) == 11:
+        system_message = "We have asked enough questions. What are your expectations and notice period?"
+    
+    # Generate response from GPT model
+    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
+
+iface = gr.ChatInterface(
+    chatbot,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", 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)",
+        ),
+    ],
 )
 
 if __name__ == "__main__":