app.py

import gradio as gr
import openai
import os
import tiktoken
import re
import time 
import pandas as pd 
# Set your OpenAI API key
openai.api_key = os.getenv('OPENAI_API_KEY')

# Pricing constants
INPUT_COST_PER_TOKEN = 0.50 / 1_000_000
OUTPUT_COST_PER_TOKEN = 1.50 / 1_000_000

def print_like_dislike(x: gr.LikeData):
    print(x.index, x.value, x.liked)

def add_text(history, text):
    history.append((text, "**That's cool!**"))
    return history

def add_file(history, file):
    # Assuming you want to display the name of the uploaded file
    file_info = (f"Uploaded file: {file.name}", "")
    history.append(file_info)
    return history

def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
    encoding = tiktoken.encoding_for_model(model)
    num_tokens = 0
    for message in messages:
        num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":  # if there's a name, the role is omitted
                num_tokens += 1  # role is always required and always 1 token
    num_tokens += 2  # every reply is primed with <im_start>assistant
    return num_tokens

def initialize_chat(initial_question):
    # This function initializes the chat with the user-provided initial question.
    chat_history = [(None, initial_question)]
    response, follow_up_questions, token_info = generate_response(initial_question, 0)
    chat_history.append((None, response))
    
    # Extract follow-up questions as examples
    follow_up_questions_formatted = [q.strip() for q in follow_up_questions.split('\n') if q.strip()]
    examples_state = [[q] for q in follow_up_questions_formatted]
    
    return chat_history, follow_up_questions, token_info, examples_state

def generate_response(prompt, token_count=0):
    print(f"Received prompt: {prompt}")
    messages = [
        {"role": "system", "content": "You are a friendly and helpful chatbot."},
        {"role": "user", "content": prompt}
    ]
    
    try:
        input_tokens = num_tokens_from_messages(messages, model="gpt-3.5-turbo")
        
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=messages,
            max_tokens=150,
            temperature=0.7,
        )
        
        output_text = response.choices[0].message['content'].strip()
       
        output_tokens = response.usage['completion_tokens']
        
        follow_up_prompt = f"Based on the following response, suggest three follow-up questions that a young person should ask in first person: {output_text}"
        follow_up_messages = [
            {"role": "system", "content": "You are a friendly and helpful chatbot."},
            {"role": "user", "content": follow_up_prompt}
        ]
        follow_up_response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=follow_up_messages,
            max_tokens=100,
            temperature=0.7,
        )

        follow_up_questions = follow_up_response.choices[0].message['content'].strip().split('\n')
        follow_up_questions = "\n".join(follow_up_questions)
        print(f"Follow up questions: {follow_up_questions}")
        topics_str = "Topic analysis not available"
        
        # Calculate the total tokens used
        total_input_tokens = input_tokens + num_tokens_from_messages(follow_up_messages, model="gpt-3.5-turbo")
        total_output_tokens = output_tokens + follow_up_response.usage['completion_tokens']
        
        # Calculate cost
        input_cost = total_input_tokens * INPUT_COST_PER_TOKEN
        output_cost = total_output_tokens * OUTPUT_COST_PER_TOKEN
        total_cost = input_cost + output_cost

        # Adjusted to return the response and follow-up questions
        new_response = output_text + "\n\nTopics: " + topics_str
        token_info = f"### Token Usage:\n\n* Input Tokens: {total_input_tokens}\n* Output Tokens: {total_output_tokens}\n* Total Cost: ${total_cost:.4f}"
    except Exception as e:
        new_response = f"Error generating response: {e}"
        follow_up_questions = []
       
        token_info = "### Token Usage:\n\n* Input Tokens: 0\n* Output Tokens: 0\n* Total Cost: $0.0000"
    
    return new_response, follow_up_questions, token_info, 

def process_response(prompt, chat_history, token_count, examples_state):
    global examples
    response, new_follow_up_questions, token_info = generate_response(prompt, token_count)
    chat_history.append((prompt, response))

    # Split and format the new follow-up questions into separate values
    new_follow_up_questions_formatted = [q.strip() for q in new_follow_up_questions.split('\n')[:3]]
    question1, question2, question3 = new_follow_up_questions_formatted

     # Update examples state with the new follow-up questions
    examples_state = [[question1], [question2], [question3]]
   
    
    follow_up_questions_md = "\n".join(new_follow_up_questions_formatted)


    return chat_history, token_info, follow_up_questions_md, examples_state

# CSS for the phone layout and background
css = """
#chat-container {
    max-width: 400px!Important;
    margin: auto;
    border: 1px solid #ccc;
    border-radius: 20px;
    overflow: hidden;
    background: url('https://path-to-your-phone-background-image.png') no-repeat center center;
    background-size: cover;

    padding: 20px;
    box-sizing: border-box;
    display: flex;
    flex-direction: column;
}

#chatbot {
    height: calc(100% - 50px);
    overflow-y: auto;
    background: transparent;
    width: 100%;
}

#component-10 {
    font-size: 7px;
    padding: 5px;
    margin: 5px 0;
    width: 100%;
}

#example-container .gr-examples {
    font-size: 0.9em;
    padding: 5px;
    margin: 5px 0;
}
"""

# Initialize the chat history and suggested questionsw
# Initialize the chat history and suggested questions
#chat_history, follow_up_questions, initial_token_info = initialize_chat("I'm 14 years old female and want to become a graphic designer. I'm living in Uttar Pradesh in India. How can I start?")


# Initial example questions from the initialization
questions = [["I'm 14 years old girl living in Utar Pradesh show where I can take HPV vaccine and more information about the HPV"], ["I'm 14 years old female and want to become a graphic designer. I'm living in Uttar Pradesh in India. How can I start?"],["Im a 15 years old bout living in New Delhi how i can lear more about the climate change and What I can do myself?"]]

with gr.Blocks(css=css) as demo:
    examples_state = gr.State([])
    chat_history = gr.State([])
    token_info = gr.State("")
    follow_up_questions_md = gr.State("")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown(
                """
                # Child safe chatbot project!
                In the realm of digital communication, the development of an advanced chatbot that incorporates topic modeling represents a significant leap towards enhancing user interaction and maintaining focus during conversations. This innovative chatbot design is specifically engineered to streamline discussions by guiding users to select from a curated list of suggested questions. This approach is crafted to mitigate the risk of diverging into off-topic dialogues, which are common pitfalls in conventional chatbot systems.
                """
            )
            token_info_display = gr.Markdown(
                value="",
                elem_id="token-info"
            )
            follow_up_questions_display = gr.Markdown(
                value="",
                elem_id="follow-up-questions",
                label="Follow up Questions"
            )
            initial_question_input = gr.Textbox(
                placeholder="Type your initial question here...", 
                label="Initial Question"
            )
            initialize_button = gr.Button("Initialize Chat")

            question_examples = gr.Examples(
                examples = questions,
                inputs = initial_question_input,
                label = "Intial Questions"
            )
        
        with gr.Column(scale=1, elem_id="chat-container"):
            chatbot = gr.Chatbot(
                value=[],
                elem_id="chatbot",
                bubble_full_width=False,
                label="Safe Chatbot v1"
            )
            
            with gr.Row():
                txt = gr.Textbox(scale=4, show_label=False, placeholder="Select question...", container=False, interactive=False)
                btn = gr.Button("Submit")

                btn.click(
                    fn=process_response,
                    inputs=[txt, chat_history, gr.State(0), examples_state],
                    outputs=[chatbot, token_info_display, follow_up_questions_display, examples_state]
                )
                examples_component = gr.Examples(
                    examples=examples_state.value,
                    inputs=[txt],
                    label="Questions"
                )
            
            chatbot.like(print_like_dislike, None, None)

    initialize_button.click(
        fn=initialize_chat,
        inputs=[initial_question_input],
        outputs=[chat_history, follow_up_questions_display, token_info_display, examples_state]
    ).then(
        fn=lambda chat_history: chat_history,
        inputs=[chat_history],
        outputs=[chatbot]
    )

if __name__ == "__main__":
    demo.launch(share=False)