153 lines
5.2 KiB
Python
153 lines
5.2 KiB
Python
import aiohttp
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import io
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import time
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import os
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import random
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import json
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from langdetect import detect
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from gtts import gTTS
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from urllib.parse import quote
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from utils.config_loader import load_current_language, config
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from openai import AsyncOpenAI
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from duckduckgo_search import AsyncDDGS
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from dotenv import load_dotenv
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load_dotenv()
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current_language = load_current_language()
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internet_access = config['INTERNET_ACCESS']
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client = AsyncOpenAI(
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base_url=config['API_BASE_URL'],
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api_key="nah-ha",
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)
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async def generate_response(instructions, history):
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messages = [
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{"role": "system", "name": "instructions", "content": instructions},
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*history,
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]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "searchtool",
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"description": "Searches the internet.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query for search engine",
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}
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},
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"required": ["query"],
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},
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},
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}
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]
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response = await client.chat.completions.create(
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model=config['MODEL_ID'],
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messages=messages,
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tools=tools,
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tool_choice="auto",
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)
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response_message = response.choices[0].message
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tool_calls = response_message.tool_calls
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if tool_calls:
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available_functions = {
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"searchtool": duckduckgotool,
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}
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messages.append(response_message)
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for tool_call in tool_calls:
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function_name = tool_call.function.name
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function_to_call = available_functions[function_name]
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function_args = json.loads(tool_call.function.arguments)
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function_response = await function_to_call(
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query=function_args.get("query")
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)
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messages.append(
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{
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"tool_call_id": tool_call.id,
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"role": "tool",
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"name": function_name,
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"content": function_response,
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}
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)
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second_response = await client.chat.completions.create(
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model=config['MODEL_ID'],
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messages=messages
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)
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return second_response.choices[0].message.content
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return response_message.content
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async def duckduckgotool(query) -> str:
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if config['INTERNET_ACCESS']:
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return "internet access has been disabled by user"
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blob = ''
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results = await AsyncDDGS(proxy=None).text(query, max_results=6)
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try:
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for index, result in enumerate(results[:6]): # Limiting to 6 results
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blob += f'[{index}] Title : {result["title"]}\nSnippet : {result["body"]}\n\n\n Provide a cohesive response base on provided Search results'
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except Exception as e:
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blob += f"Search error: {e}\n"
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return blob
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async def poly_image_gen(session, prompt):
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seed = random.randint(1, 100000)
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image_url = f"https://image.pollinations.ai/prompt/{prompt}?seed={seed}"
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async with session.get(image_url) as response:
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image_data = await response.read()
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return io.BytesIO(image_data)
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async def generate_image_prodia(prompt, model, sampler, seed, neg):
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print("\033[1;32m(Prodia) Creating image for :\033[0m", prompt)
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start_time = time.time()
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async def create_job(prompt, model, sampler, seed, neg):
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url = 'https://api.prodia.com/generate'
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params = {
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'new': 'true',
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'prompt': f'{quote(prompt)}',
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'model': model,
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'steps': '100',
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'cfg': '9.5',
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'seed': f'{seed}',
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'sampler': sampler,
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'upscale': 'True',
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'aspect_ratio': 'square'
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}
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async with aiohttp.ClientSession() as session:
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async with session.get(url, params=params) as response:
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data = await response.json()
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return data['job']
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job_id = await create_job(prompt, model, sampler, seed, neg)
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url = f'https://api.prodia.com/job/{job_id}'
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headers = {
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'authority': 'api.prodia.com',
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'accept': '*/*',
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}
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async with aiohttp.ClientSession() as session:
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while True:
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async with session.get(url, headers=headers) as response:
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json = await response.json()
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if json['status'] == 'succeeded':
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async with session.get(f'https://images.prodia.xyz/{job_id}.png?download=1', headers=headers) as response:
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content = await response.content.read()
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img_file_obj = io.BytesIO(content)
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duration = time.time() - start_time
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print(f"\033[1;34m(Prodia) Finished image creation\n\033[0mJob id : {job_id} Prompt : ", prompt, "in", duration, "seconds.")
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return img_file_obj
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async def text_to_speech(text):
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bytes_obj = io.BytesIO()
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detected_language = detect(text)
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tts = gTTS(text=text, lang=detected_language)
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tts.write_to_fp(bytes_obj)
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bytes_obj.seek(0)
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return bytes_obj |