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CRMBot / app.py

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Rename main.py to app.py

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	from openai import OpenAI as OPENAI
	import os
	import gradio as gr
	import json

	client = OPENAI()
	client.api_key = os.environ.get("OPENAI_API_KEY")


	conversation_history=[]

	def predict(input):

	completion = client.completion.create(
	model="gpt-4",
	messages=[{"role": "user", "content": input}]
	)
	response = completion.choices[0].messag.content

	return response



	def initial_prompt():

	# client.api_key = os.environ.get("OPENAI_API_KEY")

	f = open('templates/record_types.json')
	crm_temlate_json = json.load(f)

	global conversation_history
	default_message = (f"""
	Microsoft Dynamic CRM için kayıt olusturmada yardımcı bir asistan gibi davranmanı istiyorum.
	Senin isin yalnizca CRM kayit olsuturma ve yonetme konusunda yardimci olmak, bu sebeple kullanici
	CRM haricinde baska bir konuda konusmak isterse onca kibarca gorevini hatirlatarak istedigi islemi
	yapamayacagini belirtiyorsun. Kullanici seninle iletisime gectiginde kendini CRM Asistan Bot olarak
	tanitarak nasil yardimci olabilecegini soracaksin.
	Kullanicinin yapmak istedigi isleme gore {crm_temlate_json} json verisi icinden 'Contact' yada 'Aktivite'
	templatelerine gore kullanicin girmesi gereken input verilerini isteyeceksin.
	Kullanıcı seninle iletişime geçtiğinde amaçlarını ögrenip, amaclanan kayit icin ihtiyac olabilecek
	alan bilgilerini sirasiyla, adim adim kullanicidan isteyecek ve islemler bittiginda bu bilgileri
	json olarak doneceksin. Kullanici kayit olusturman icin onaylamadigi surece
	ek bilgi ekleyip eklemek istemedigini soracaksin. Ne olursa olsun kullanicinin belirtmesi gereken bilgileri kendin girmeyeceksin.
	Kullanici ek bilgi eklemek isterse sirasiyla o bilgileri isteyerek olusturacagin kayit bilgisine ekleyeceksin.
	Ornek: [Kullanici:"Merhaba", Asistan:"Merhaba! Size nasil yardimci olabilirim?",
	Kullanici: "Kontakt kaydi olusturmak istiyorum", ...]. Ilk kayit ile baslayalim
	""")

	conversation_history.append({"role": "system", "content": f"{default_message}"})

	completion = client.chat.completions.create(
	model="gpt-4",
	messages=[{"role": "system", "content": " ".join([str(item) for item in conversation_history])}],
	temperature=0.3,
	max_tokens=7500,
	)

	message = completion.choices[0].message.content
	print(message)

	initial_prompt()

	def handle_input(input_str : str):
	global conversation_history

	if len(conversation_history) >=20:
	conversation_history = conversation_history[:1] + conversation_history[-10:]

	conversation_history.append({"role": "user", "content": f"{input_str}"})

	content = " ".join([str(item) for item in conversation_history])
	completion = client.chat.completions.create(
	model="gpt-4",
	messages=conversation_history,
	# temperature=temperature,
	# max_tokens=max_tokens,
	# presence_penalty=presence_penalty,
	# frequency_penalty=frequency_penalty,
	# top_p = top_p_input,
	#stream = stream_input
	)



	message = completion.choices[0].message.content

	conversation_history.append({"role": "assistant", "content": f"{message}"})

	def get_response_again(content):

	while True:
	completion = client.chat.completion.create(
	model="gpt-4",
	messages=[{"role": "assistant", "content": content}],
	# temperature=temperature,
	# max_tokens=max_tokens,
	# presence_penalty=presence_penalty,
	# frequency_penalty=frequency_penalty,
	# top_p = top_p_input,
	#stream = stream_input
	)

	message = completion.choices[0].message.content

	if "Müşteri" not in message:
	break

	return message

	# if "Müşteri" in message:
	# get_response_again(content)

	# conversation_history.append(f"{message}\n")

	return message

	def get_model_reply(query,context=[]):

	context += [query]

	# client.api_key = api_key

	response = handle_input(query)


	context += [response]

	responses = [(u,b) for u,b in zip(context[::2], context[1::2])]

	return responses, context


	#TODO Thiws feature will be added later
	def speech_2_text(audio,api_key, context=[]):
	client.api_key = api_key
	audio_file= open(audio, "rb")
	transcript = client.Audio.transcribe("whisper-1", audio_file)
	prompt = transcript.text
	context += [prompt]

	completion = client.chat.completion.create(
	model="gpt-4",
	messages=[{"role": "user", "content": prompt}],
	max_tokens=2500,
	)

	response = completion.choices[0].message.content
	context += [response]

	responses = [(u,b) for u,b in zip(context[::2], context[1::2])]

	return responses, context

	# def get_audio(url,api_key,context=[]):
	client.api_key = api_key
	destination = f"./audio/test"
	try:
	ydl_opts = {
	'format': 'bestaudio/best',
	'outtmpl':destination,
	'postprocessors': [{
	'key': 'FFmpegExtractAudio',
	'preferredcodec': 'mp3',
	'preferredquality': '192',
	}],
	}
	with yt_dlp.YoutubeDL(ydl_opts) as ydl:
	ydl.download([url])

	audio_file= open(f'{destination}.mp3', "rb")
	transcript = client.Audio.transcribe("whisper-1", audio_file)

	context += [transcript.text]

	responses = [(u,b) for u,b in zip(context[::2], context[1::2])]

	return responses, context

	except Exception as e:
	print("Connection Error")


	with gr.Blocks(css="#chatbot {overflow-y:auto; height:400px;}") as dialog_app:

	with gr.Tab("ChatBot"):
	with gr.Row():
	# with gr.Column(scale=1, min_width=600):
	# api_key = gr.Textbox(label="Your API Key", type="password")
	# temperature_input = gr.Slider(minimum=0, maximum=1.0, default=0.5, step=0.01, label="Temperature")
	# max_tokens_input = gr.inputs.Slider(minimum=1, maximum=2048, default=50, step=10, label="Max Tokens")
	# top_p_input = gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, step=0.01, label="Top P")
	# presence_penalty_input = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.0, step=0.1, label="Presence Penalty")
	# frequency_penalty_input = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.0, step=0.1, label="Frequency Penalty")
	#stream_input = gr.inputs.Checkbox(label="Stream")

	with gr.Column(scale=2, min_width=600):
	chatbot = gr.Chatbot(elem_id="chatbot")
	state = gr.State([])

	txt = gr.Textbox(
	show_label=False,
	placeholder="Enter text and press enter",
	container=False
	)

	# txt.submit(get_model_reply, [txt, api_key, temperature_input, max_tokens_input,top_p_input, presence_penalty_input,frequency_penalty_input, state], [chatbot, state])
	txt.submit(get_model_reply, [txt, state], [chatbot, state])
	txt.submit(lambda :"", None, txt)


	# with gr.Tab("Voice Chat"):
	# with gr.Row():
	# with gr.Column(scale=1, min_width=600):
	# voice_api_key = gr.Textbox(label="Your API Key", type="password")
	# voice_state = gr.State([])
	# youtube_url = gr.Textbox(
	# show_label=False,
	# type="text",
	# placeholder="Enter an Youtube URL")

	# mic_audio = gr.Audio(source="microphone", type="filepath")

	# with gr.Row():
	# with gr.Column(scale=1, min_width=250):
	# audio_clean_btn = gr.Button(value='Clean')
	# with gr.Column(scale=2, min_width=250):
	# audio_sbmt_btn = gr.Button(value='Submit', variant='primary')

	# with gr.Column(scale=2, min_width=600):
	# voice_bot = gr.Chatbot(elem_id="chatbot")
	# #txt_box = gr.Textbox(type="text")
	# voice_state = gr.State([])

	# txt = gr.Textbox(
	# show_label=False,
	# placeholder="Enter text and press enter"
	# ).style(container=False)

	# #youtube_url.submit(get_audio, [youtube_url,voice_api_key, state], [voice_bot, state])
	# #audio_clean_btn.click(clean_audio, )
	# audio_sbmt_btn.click(speech_2_text, inputs=[mic_audio,voice_api_key,voice_state], outputs=[voice_bot, voice_state])

	# txt.submit(get_model_reply, [txt, voice_api_key, temperature_input, max_tokens_input,top_p_input, presence_penalty_input,frequency_penalty_input, voice_state], [voice_bot, voice_state])
	# txt.submit(lambda :"", None, txt)


	dialog_app.launch()