app.py

import os
import openai
import datetime
import time
import gradio as gr
import json
from jinja2 import Template
import requests
from pdfminer.high_level import extract_text
import pdfkit

# Initialize OpenAI
openai.api_key = os.environ.get('OPENAI_API_KEY')

# Configuration variables
airtable_api_key = os.environ.get('AIRTABLE_API_KEY')

# Airtable table names
prompts_table_name = 'tblYIZEB8m6JkGDEP'
users_table_name = 'tblLNe5ZL47SvrAEk'
user_log_table_name = 'tblrlTsRrkl6BqMAJ'
scoutlog_table_name = 'tblF30p0krA1FwOiD'
policies_table_name = 'tbla6PC65qZfqdJhE'


# Define the style and content for the response field
label_text = "Contract Redline"
color = "#6562F4"
background_color = "white"
border_radius = "10px"
# response_label = f'<h3 style="color: {color}; background-color: {background_color}; border-radius: {border_radius}; padding: 10px;display: inline-block;">{label_text}</h3>'
response_label = f'<span style="display: inline-block; position: relative; z-index: var(--layer-4); border: solid var(--block-title-border-width) var(--block-title-border-color); border-radius: var(--block-title-radius); background: var(--block-title-background-fill); padding: var(--block-title-padding); color: var(--block-title-text-color); font-weight: var(--block-title-text-weight); font-size: var(--block-title-text-size); line-height: var(--line-sm); margin-bottom: var(--spacing-lg);">{label_text}</span>'
base_id = 'appcUK3hUWC7GM2Kb'

# App name for user login logging
app = "Scout"

headers = {
    "Authorization": f"Bearer {airtable_api_key}",
    "Content-Type": "application/json",
    "Accept": "application/json",
}


def prompt_trim(prompt: str) -> str:
    lines = prompt.split('\n')
    trimmed = '\n'.join([l.strip() for l in lines])
    return trimmed

def get_policy_text(school):

    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{policies_table_name}'

    # Parameters for the API request to select only the 'school' field
    params = {
        'filterByFormula': f'school="{school}"',
        'fields[]': 'policy_text'
    }

    global policy_text
    policy_text = ''

    try:
        # Send a GET request to the Airtable API
        response = requests.get(airtable_endpoint, headers=headers, params=params)

        # Check if the request was successful (status code 200)
        if response.status_code == 200:
            # Parse the JSON response
            data = response.json()

            # Check if there are records in the response
            if data.get('records'):
                # Extract the 'school' values from each record
                policy_text = [record['fields']['policy_text'] for record in data['records']]

            else:
                print("No records found in the 'policies' table.")
        else:
            print(f"Failed to retrieve data. Status code: {response.status_code}")
    except Exception as e:
        print(f"An error occurred: {str(e)}")

    #print(policy_text)

    return policy_text

def get_schools():

    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{policies_table_name}'

    # Parameters for the API request to select only the 'school' field
    params = {
        'fields[]': 'school',  # Replace with the name of your field
        'sort[0][field]': 'school',  # Sort by the 'school' field
        'sort[0][direction]': 'asc',  # Sort in ascending order
    }

    schools = ''

    try:
        # Send a GET request to the Airtable API
        response = requests.get(airtable_endpoint, headers=headers, params=params)

        # Check if the request was successful (status code 200)
        if response.status_code == 200:
            # Parse the JSON response
            data = response.json()

            # Check if there are records in the response
            if data.get('records'):
                # Extract the 'school' values from each record
                schools = [record['fields']['school'] for record in data['records']]

            else:
                print("No records found in the 'policies' table.")
        else:
            print(f"Failed to retrieve data. Status code: {response.status_code}")
    except Exception as e:
        print(f"An error occurred: {str(e)}")

    return schools

def get_prompt(header, template_content):
    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{prompts_table_name}'

    params = {
        'filterByFormula': "prompt_name='Compliance_v1'",
    }

    response = requests.get(airtable_endpoint, headers=headers, params=params)

    # Check for errors
    response.raise_for_status()

    data = response.json()

    # Check if there is at least one record matching the condition
    if data.get('records'):
        # Get the first record (there should be only one)
        record = data['records'][0]['fields']

        # Assign system_prompt and user_prompt to variables
        header = record.get('system_prompt', '')
        template_content = record.get('user_prompt', '')

    return header, template_content


def append_to_at_scountlog(school_selection, output_format, gender, interests, sports, gpt_response, number_of_athletes,
                           social_media_channel, response_time, question_cost, prompt_tokens, completion_tokens):
    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{scoutlog_table_name}'

    # Organize data for Airtable
    new_fields = {
        'school_selection': str(school_selection),
        'gender': str(gender),
        'social_media_channel': str(social_media_channel),
        'sports': str(sports),
        'interests': str(interests),
        'number_of_athletes': int(number_of_athletes),
        'output_format': str(output_format),
        'gpt_response': str(gpt_response),
        'response_time': str(response_time),
        'question_cost': question_cost,
        'user_name': str(logged_in_user),
        'prompt_tokens': prompt_tokens,
        'completion_tokens': completion_tokens
    }

    data = {
        'fields': new_fields
    }
    try:

        # Post data to Airtable
        response = requests.post(airtable_endpoint, headers=headers, json=data)

        # print(response.json())

        # Check for errors
        response.raise_for_status()

    except requests.exceptions.HTTPError as http_error:
        # Handle the HTTP error (e.g., log it or display an error message)
        print(f"HTTP error occurred: {http_error}")

    except Exception as e:
        # Handle exceptions, log errors, or raise them as needed
        print(f"An error occurred: {str(e)}")


# Chatbot Function
def chatbot(policy_name,contract_text,progress=gr.Progress()):

    start_time = datetime.datetime.now()

    progress(progress=None)

    """
    time.sleep(10)
    for i in progress.tqdm(range(100)):
        time.sleep(1)
    """

    #print(policy_name)

    #students = get_students(school_selection)
    get_policy_text(policy_name)

    #print(policy_text)
    #print(contract_text)

    template_content = ''
    header = ''

    header, template_content = get_prompt(header, template_content)

    # print(header)
    # print(template_content)

    # Create a Jinja2 template from the content
    template = Template(template_content)

    # Render the template with the inputs
    analysis_input = template.render(contract_text=contract_text,policy_text=policy_text)

    trimmed_input = prompt_trim(analysis_input)

    with open('analysis_input.txt', 'w', encoding='utf-8') as out_file:
        out_file.write(trimmed_input)

    response = openai.ChatCompletion.create(
        model="gpt-4",
        # model="gpt-3.5-turbo",
        temperature=0,
        messages=[
            {
                "role": "system",
                "content": header
            },
            {
                "role": "user",
                "content": analysis_input
            }
        ]
    )

    gpt_response = response.choices[0].message["content"]

    tokens_used = response.usage

    question_cost = (tokens_used.get('total_tokens', 0) / 1000) * .03
    prompt_tokens = tokens_used.get('prompt_tokens', )
    completion_tokens = tokens_used.get('completion_tokens', 0)

    with open('response.txt', 'w', encoding='utf-8') as out_file:
        out_file.write(gpt_response)

    end_time = datetime.datetime.now()

    response_time = end_time - start_time

    contract_redline = gpt_response

    #print(contract_redline)

    #append_to_at_scountlog(school_selection, output_format, gender, interests, sports, gpt_response, number_of_athletes,social_media_channel, response_time, question_cost, prompt_tokens, completion_tokens)

    return {contract_redline_html: contract_redline, download_row: gr.Row(visible=True)}

def log_login(username):
    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{user_log_table_name}'

    # Organize data for Airtable
    new_fields = {
        'user_name': str(username),
        'app': str(app)
    }

    data = {
        'fields': new_fields
    }
    try:

        # Post data to Airtable
        response = requests.post(airtable_endpoint, headers=headers, json=data)

        # Check for errors
        response.raise_for_status()

    except requests.exceptions.HTTPError as http_error:
        # Handle the HTTP error (e.g., log it or display an error message)
        print(f"HTTP error occurred: {http_error}")

    except Exception as e:
        # Handle exceptions, log errors, or raise them as needed
        print(f"An error occurred: {str(e)}")


def login_auth(username, password):
    airtable_endpoint = f'https://api.airtable.com/v0/{base_id}/{users_table_name}'

    # Query the 'users' table to check for a match with the provided username and password
    params = {
        'filterByFormula': f'AND(user_name = "{username}", password = "{password}")'
    }

    response = requests.get(airtable_endpoint, headers=headers, params=params)

    if response.status_code == 200:
        data = response.json()
        if data.get('records'):
            log_login(username)
            global logged_in_user
            logged_in_user = username

            return True

        print(f"Invalid user/password combination")

    return False

def pdf_to_text(contract_file_cmpt):

    file_text = extract_text(contract_file_cmpt.name)

    original_file_name = contract_file_cmpt.name.split("/")[-1]
    redline_file_name = original_file_name.split(".")[0]+" Redline.pdf"

    return file_text,redline_file_name

def download_pdf(file_name,compliance_comments,contract_redline_html):

    contract_redline_comments = "<h2><u>Contract Redline:</u></h2><br>"+contract_redline + "<br><h2><u>Compliance Comments:</u></h2><br>"+compliance_comments

    pdf_download = pdfkit.from_string(contract_redline_comments,False)

    with open(file_name, 'wb') as f:
        f.write(pdf_download)

    return file_name

# Gradio UI
CIMStheme = gr.themes.Soft().set(
    button_primary_background_fill='#6562F4',
)

schools = get_schools()

#policy_text = get_policy_text("LSU") #for testing the function call

# scout_response = "<!DOCTYPEhtml><html><head><title>HTMLTableGenerator</title><style>table{width:100%;border:2pxsolid#000000;padding:5px;}tableth{border:2pxsolid#000000;padding:5px;background:#f0f0f0;color:#000000;}tabletd{border:2pxsolid#000000;text-align:left;padding:5px;background:#ffffff;color:#000000;}</style></head><body><table><thead><tr><th>Rank</th><th>CIMS.AI <br>Scout Ranking</th><th>Athlete Name</th><th>Gender</th><th>Primary Sport</th><th>Social Media <br>Fan Total</th><th>Interests</th><th>Commentary on Ranking</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table></body></html>"
scout_response = "<!DOCTYPEhtml><html><head><title>HTMLTableGenerator</title><style>table{width:100%;border:2pxsolid#000000;padding:5px;background:#ffffff}tableth{border:2pxsolid#000000;padding:5px;background:#f0f0f0;color:#000000;}tabletd{border:2pxsolid#000000;text-align:left;padding:5px;background:#ffffff;color:#000000;}</style></head><body><table><thead><tr><th>Rank</th><th>CIMS.AI<br>Scout<br>Ranking</th><th>Athlete Name</th><th>Gender</th><th>Primary Sport</th><th>Social Media<br>Fan Total</th><th>Interests</th><th>Commentary on Ranking</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table></body></html>"

contract_redline = "<p><strong>Campaign:</strong></p><p><strong>Engagement Name:</strong> Applebee's Fall Burger Promo</p><p><strong>Engagement Id:</strong> 7015j000001DvCAAA0</p><p><strong>Sponsor:</strong> Applebee's</p><p><strong>Start Date:</strong> 2023-10-28</p><p><strong>End Date:</strong> 2023-11-11</p><p><strong>Engagement Description</strong></p><p>The goal of the engagement is to Increase Sales by having the Student-Athlete Social Media Post. For the Social Media Post, the sponsor is requesting the student athlete take a photo in front of the <span style='text-decoration: line-through;'>football stadium</span> in eating a Applebee's burger <span style='text-decoration: line-through;'>in your team jersey</span>. The Media rights for the content will be 90 Days.</p><p><strong>Engagement Compensation.</strong></p><p>For successful completion of the engagement the student-athlete will receive payment in the form of Cash.</p><p>Part or all of the payment will be in cash, paid via PayPal.</p><p>The total value of compensation will be 250.</p>"
pdf_download = pdfkit.from_string(contract_redline, False)

#print(pdf_download)

logged_in_user = 'admin'
file_text = ''
contract_text = ''
policy_text = ''
compliance_comments = ''
file_name = 'redline.pdf'

with gr.Blocks(CIMStheme) as iface:
    with gr.Row():
        with gr.Column(scale=2):
            gr.Image(label="Logo", value="CIMS Logo Purple.png", width=10, show_download_button=False,
                     interactive=False, show_label=False, elem_id="logo", container=False)
        with gr.Column(scale=2):
            gr.Markdown(value="<H1 style='text-align: center;'>Compliance Desktop - Powered by CIMS.AI</h1>")
        with gr.Column(scale=2):
            gr.Markdown("")

    with gr.Row():
        with gr.Column(variant='panel',scale=1):
            policy_name = gr.components.Dropdown(schools, multiselect=False,label="NIL Policy Selection")
            contract_file_cmpt = gr.File(label="Select Contract File",file_count="single",file_types=[".pdf"],height=150)
        with gr.Column(variant='panel',scale=3):
            contract_text_tbox = gr.Textbox(label="Contract Text",interactive=True,info="Upload .pdf or paste in text")
    with gr.Row():
        with gr.Column():
            btn_upload = gr.components.Button(value="Upload Contract", size='sm', variant="primary")
        with gr.Column():
            gr.Markdown("")
        with gr.Column():
            btn_redline = gr.components.Button(value="Redline Contract", size='sm', variant="primary")
        with gr.Column():
            gr.Markdown("")
    with gr.Row():
        with gr.Column(variant='panel'):
            gr.components.Markdown(response_label)
            contract_redline_html = gr.components.HTML(label="Contract Redline")
            compliance_comments_tbox = gr.Textbox(interactive=True,label='Compliance Comments')
    with gr.Row(visible=False) as download_row:
        with gr.Column(variant='panel'):
            file_name_tbox = gr.Textbox(interactive=True,label='File Name')
            btn_download = gr.components.Button(value="Download Redline", size='sm', variant="primary")
            #pdf_download = gr.File(label="Download Redline File",height=150)
            btn_download.click(download_pdf,inputs=[file_name_tbox,compliance_comments_tbox,contract_redline_html])
            btn_redline.click(chatbot,inputs=[policy_name,contract_text_tbox],outputs=[contract_redline_html,download_row])
            btn_upload.click(pdf_to_text,inputs=contract_file_cmpt,outputs=[contract_text_tbox,file_name_tbox])
        with gr.Column():
            gr.Markdown("")
        with gr.Column():
            gr.Markdown("")
        with gr.Column():
            gr.Markdown("")
    with gr.Row():
        with gr.Column():
            gr.HTML('<center><i>CIMS.AI Confidential 2023</i></center>')

iface.queue(concurrency_count=20).launch(auth=login_auth, auth_message= "Enter your username and password that you received from CIMS.AI. To request a login, please email 'info@cims.ai'")
#iface.queue(concurrency_count=20).launch()