app.py

import os
import requests
import gradio as gr
requests.adapters.DEFAULT_TIMEOUT = 60
import time
import openai
from openai import OpenAI
from utils import ai_audit_analysis_categories, get_system_prompt, ANALYSIS_TYPES
import json


# Create Global Variables
client = OpenAI(api_key= "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")

global complete_chat_history, bot_last_message
bot_last_message = "" 
complete_chat_history = []


# /////////////////// *****************************///////////////// Utitlity Functions
#region Utility Functions

# Function to update OpenAPI key the API key
def update_api_key(new_api_key):
    global client
    if new_api_key.strip() != "":
        client = OpenAI(api_key=new_api_key)
    return "API Key updated successfully"


def load_chatboat_last_message():
    return bot_last_message

def load_chatboat_complet_history():
    complete_text = ""
    for turn in complete_chat_history:
        user_message, bot_message = turn
        complete_text = f"{complete_text}\nUser: {user_message}\nAssistant: {bot_message}"
    return complete_text


def format_json_result_to_html(result):
    formatted_result = ""
    for key, value in result.items():
        if isinstance(value, list):
            formatted_result += f"<strong>{key.title()}:</strong><br>" + "<br>".join(value) + "<br><br>"
        else:
            formatted_result += f"<strong>{key.title()}:</strong> {value}<br>"
    return formatted_result.strip()

def format_json_result(result):
    formatted_result = ""
    for key, value in result.items():
        if isinstance(value, list):
            formatted_result += f"{key.title()}:\n" + "\n".join(value) + "\n\n"
        else:
            formatted_result += f"{key.title()}: {value}\n"
    return formatted_result.strip()

# Function to dynamically format the JSON result into Markdown format
def format_result_to_markdown(result):
    formatted_result = ""
    for key, value in result.items():
        formatted_result += f"**{key.title()}**: "
        if isinstance(value, list):
            formatted_result += "\n" + "\n".join(f"- {item}" for item in value) + "\n\n"
        else:
            formatted_result += f"{value}\n\n"
    return formatted_result.strip()

#endregion






# /////////////////// *****************************///////////////// Conversation with Open Ai Chatboat
#region Conversation with Open Ai Chatboat
# A Normal call to OpenAI API '''
def chat(system_prompt, user_prompt, model = 'gpt-3.5-turbo', temperature = 0):
    response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ],
    model="gpt-3.5-turbo",
    )
    
    res = response.choices[0].message.content  
    return res

# Lets format the prompt from the chat_history so that its looks good on the UI
def format_chat_prompt(message, chat_history, max_convo_length):
    prompt = ""
    for turn in chat_history[-max_convo_length:]:
        user_message, bot_message = turn
        prompt = f"{prompt}\nUser: {user_message}\nAssistant: {bot_message}"
    prompt = f"{prompt}\nUser: {message}\nAssistant:"
    return prompt


# This function gets a message from user, passes it to chat gpt and return the output
def get_response_from_chatboat(message,chat_history, max_convo_length=10):
    global bot_last_message, complete_chat_history
    formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
    bot_message = chat(system_prompt='You are a friendly chatbot. Generate the output for only the Assistant.',user_prompt=formatted_prompt)

    chat_history.append((message, bot_message))
    complete_chat_history.append((message, bot_message))
    bot_last_message = bot_message
    return "", chat_history

#endregion



def analyse_current_conversation(text, analysis_type):
    
    if(ANALYSIS_TYPES.get(analysis_type, None) is None):
        return f"Analysis type {analysis_type} is not implemented yet, please choose another category"
    
    if not text:
        return f"No text provided to analyze for {analysis_type}, please provide text or load from chatboat history"
    
    word_count = len(text.split())

    if(word_count < 20 ):
        return f" The text is too short to analyze for {analysis_type}, please provide a large text"

    system_prompt = get_system_prompt(analysis_type)
    text_to_analyze = text

    response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": text_to_analyze}
    ],
    model="gpt-3.5-turbo",
    )
    
    analysis_result = response.choices[0].message.content  
    print(analysis_result)
    parsed_result = json.loads(analysis_result)

    formated_json = format_result_to_markdown(parsed_result)

    print(parsed_result)
    # Your implementation for counting words and performing analysis
    return formated_json





#region UI Related Functions

def update_dropdown(main_category):
    # Get the subcategories based on the selected main category
    subcategories = ai_audit_analysis_categories.get(main_category, [])
    print(subcategories)
    return gr.Dropdown(choices=subcategories, value=subcategories[0] if subcategories else None)


def update_analysis_type(subcategory):
    pass
    print(subcategory)



#endregion



with gr.Blocks() as demo:

    gr.Markdown("<center><img src='https://huggingface.co/spaces/abdulnim/GRC_framework/resolve/main/logo.png' alt='Align X' width='150'/></center>")


    # Add a text field for the API key
    api_key_field = gr.Textbox(label="Enter your Chatgpt OpenAI API Key")
    update_api_key_btn = gr.Button("Update API Key")
    update_api_key_btn.click(update_api_key, inputs=[api_key_field], outputs=[])

    # gr.Markdown("# AI Audit and GRC Framework!")
    gr.Markdown("# AlignX Demo")

    with gr.Tabs():
        with gr.TabItem("Prompt Testing"):
            gr.Markdown("## Prompt Testing")
            chatbot = gr.Chatbot(height=600)
            msg = gr.Textbox(label="Write something for the chatbot here")
            clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
            submit_btn = gr.Button("Submit")
            submit_btn.click(get_response_from_chatboat, inputs=[msg, chatbot], outputs=[msg, chatbot])
            msg.submit(get_response_from_chatboat, inputs=[msg, chatbot], outputs=[msg, chatbot])

        with gr.TabItem("Prompt Assessment"):
            gr.Markdown("## Prompt Assessment")
            gr.Markdown("Load your chatbot text or write your own to  and analyze it")
            text_field = gr.Textbox(label="Text to Process", interactive=True, lines=2)

            # Radio button and dropdown list
            initial_main_category = next(iter(ai_audit_analysis_categories))
            initial_sub_categories = ai_audit_analysis_categories[initial_main_category]

            main_category_radio = gr.Radio(list(ai_audit_analysis_categories.keys()), label="Main Audit Categories", value=initial_main_category)
            sub_category_dropdown = gr.Dropdown(choices=initial_sub_categories, label="Sub Categories", value=initial_sub_categories[0])
            # Update the dropdown based on the radio selection
            main_category_radio.change(fn=update_dropdown, inputs= main_category_radio, outputs=sub_category_dropdown)
            sub_category_dropdown.change(fn=update_analysis_type, inputs=sub_category_dropdown)

            load_last_message_btn = gr.Button("Load Last Message")
            load_complete_conv_btn = gr.Button("Load Complete Chat History")
            process_btn = gr.Button("Process")
            # analysis_result = gr.Label()
            analysis_result = gr.Markdown()
            load_last_message_btn.click(load_chatboat_last_message, inputs=[], outputs=text_field)
            load_complete_conv_btn.click(load_chatboat_complet_history, inputs=[], outputs=text_field)
            process_btn.click(analyse_current_conversation, inputs=[text_field, sub_category_dropdown], outputs=analysis_result)

demo.launch(share=True)