app.py

import gradio as gr
import requests
import random
import os

api_key = os.getenv("apikey")
url = "https://talent-interview-prep-conversational-model.multimodal.dev/"

# Predefined questions
questions = {
    "Can you start by giving us an overview of your background and how your experience has prepared you specifically for the Product Manager role here at InnovateTech Solutions?",
    "What specifically interested you in this Product Manager role at InnovateTech Solutions, and how do you see your skills and experiences aligning with the responsibilities outlined in the job description?",
    "Can you tell us about your career aspirations and motivations? How do you see this role helping you achieve your long-term career goals?",
    "What do you consider to be your unique professional strengths and competencies that would contribute to your success as a Product Manager at our company? Can you provide an example where one of these strengths played a critical role in a project?",
    "Could you walk us through a time when you defined and communicated a product vision and developed a roadmap? How did you ensure alignment with the company's strategic goals and market trends?",
    "You've mentioned expertise in agile methodologies. Can you describe a specific project where you led a cross-functional team using Scrum or Kanban? What challenges did you face, and how did you overcome them?",
    "Can you provide an example of a situation where you had to manage conflicting priorities among stakeholders? How did you handle it, and what was the outcome?",
    "Now that we've discussed the role and responsibilities, do you have any questions about our company or the Product Manager position at InnovateTech Solutions?"
}

# Create a list of full questions (key + value) for the dropdown
full_questions = [f"{key}: {value}" for key, value in questions.items()]

# Initialize the session ID
session_id = random.randint(3000, 20000)
print(f"Session ID initialized: {session_id}")

# Define the API calling function
def api_call(interview_question, user_input):
    print(f"Calling API with question: {interview_question}")
    print(f"User input: {user_input}")
    headers = {
        "x-api-key": api_key,
        "Content-Type": "application/json"
    }
    data = {
        "session_id": session_id,
        "job_title": "Product Manager",
        "company_name": "InnovateTech Solutions",
        "interview_question": interview_question,
        "user_input": user_input
    }
    response = requests.post(url, headers=headers, json=data)

    print(f"API Response status code: {response.status_code}")
    try:
        json_response = response.json()
        print(f"API Response: {json_response['response']}")
        return json_response["response"]
    except:
        print("Error: Invalid JSON response")
        return "Error: Invalid JSON response"

# Gradio interface
with gr.Blocks() as demo:
    question_dropdown = gr.Dropdown(choices=full_questions, label="Select Question")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Your Response")
    send_btn = gr.Button(value="Send", variant="primary")

    def respond(message, history):
        print(f"Respond function called with message: {message}")
        if not history:
            # This is the first interaction, so we use the selected question
            question = question_dropdown.value
            print(f"First interaction, selected question: {question}")
            last_question = question
        else:
            # Get the last question asked from the local history
            last_question = history[-1][0] if history[-1][0] != "" else history[-2][0]
            print(f"Using last question: {last_question}")
        
        # Make the API call with the user's response
        bot_message = api_call(last_question, message)
        
        print(f"Bot response (now shown in Gradio interface): {bot_message}")
        return history + [(message, bot_message)], ""  # Return empty string to clear the textbox

    def update_chatbot(question):
        global session_id
        print(f"Updating chatbot with new question: {question}")
        # Change session ID only when a new question is selected
        session_id = random.randint(3000, 20000)
        print(f"New session ID: {session_id}")
        return [(question, "")]

    # Link the Textbox and Button to trigger the response function
    msg.submit(fn=respond, inputs=[msg, chatbot], outputs=[chatbot, msg])
    send_btn.click(fn=respond, inputs=[msg, chatbot], outputs=[chatbot, msg])

    question_dropdown.change(fn=update_chatbot, inputs=[question_dropdown], outputs=[chatbot])

    # Arrange the elements horizontally: Textbox and Button
    with gr.Row():
        msg
        send_btn

print("Launching Gradio interface...")
demo.launch()