request :can you add deepinfra

[djfaizp]

i have sample you can use
`import json
import requests
from typing import Union
import os
import re
from dotenv import load_dotenv; load_dotenv() # Load environment variables from .env file

def generate(conversation_history: list, model: str='meta-llama/Meta-Llama-3-70B-Instruct', system_prompt: str = "Be Helpful and Friendly. Keep your response straightforward, short and concise", max_tokens: int = 512, temperature: float = 0.7, stream: bool = True, chunk_size: int = 1) -> Union[str, None]:
"""
Utilizes a variety of large language models (LLMs) to engage in conversational interactions.

Parameters:
    - conversation_history (list): A list of dictionaries representing the conversation history including the system prompt.
    - model (str): The name or identifier of the LLM to be used for conversation. Available models include various options.
    - system_prompt (str): The initial system message to start the conversation.
    - max_tokens (int): Optional. The maximum number of tokens to be generated by the LLM. Defaults to 512.
    - temperature (float): Optional. The temperature of the LLM. Defaults to 0.7.
    - stream (bool): Optional. Whether to stream the response from the LLM. Defaults to False.
    - chunk_size (int): Optional. The size of the chunks to be streamed from the LLM. Defaults to 24.

Models:
        - "meta-llama/Meta-Llama-3-70B-Instruct"
        - "meta-llama/Meta-Llama-3-8B-Instruct" 
        - "mistralai/Mixtral-8x22B-Instruct-v0.1"
        - "mistralai/Mixtral-8x22B-v0.1"
        - "microsoft/WizardLM-2-8x22B"
        - "microsoft/WizardLM-2-7B"
        - "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1"
        - "google/gemma-1.1-7b-it"
        - "databricks/dbrx-instruct"
        - "mistralai/Mixtral-8x7B-Instruct-v0.1"
        - "mistralai/Mistral-7B-Instruct-v0.2"
        - "meta-llama/Llama-2-70b-chat-hf"
        - "cognitivecomputations/dolphin-2.6-mixtral-8x7b"

Returns:
    - Union[str, None]: The response message from the LLM if successful, otherwise None.
"""
api_url = "https://api.deepinfra.com/v1/openai/chat/completions"

headers = {
"Accept": "text/event-stream",
"Accept-Encoding": "gzip, deflate, br, zstd",
"Accept-Language": "en-US,en;q=0.9,hi;q=0.8",
"Connection": "keep-alive",
"Content-Type": "application/json",
"Dnt": "1",
"Host": "api.deepinfra.com",
"Origin": "https://deepinfra.com",
"Referer": "https://deepinfra.com/",
"Sec-Ch-Ua": "\"Google Chrome\";v=\"125\", \"Chromium\";v=\"125\", \"Not.A/Brand\";v=\"24\"",
"Sec-Ch-Ua-Mobile": "?0",
"Sec-Ch-Ua-Platform": "\"Windows\"",
"Sec-Fetch-Dest": "empty",
"Sec-Fetch-Mode": "cors",
"Sec-Fetch-Site": "same-site",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36",
"X-Deepinfra-Source": "web-page",

}
# Insert the system prompt at the beginning of the conversation history
conversation_history.insert(0, {"role": "system", "content": system_prompt})

payload = {
    "model": model,
    "messages": conversation_history,
    "temperature": temperature,
    "max_tokens": max_tokens,
    "stop": [],
    "stream": True
}

try:
    response = requests.post(api_url, headers=headers, json=payload, stream=True)
    streaming_text = ""
    for value in response.iter_lines(decode_unicode=True, chunk_size=chunk_size):
        modified_value = re.sub("data:", "", value)
        if modified_value and "[DONE]" not in modified_value:
            json_modified_value = json.loads(modified_value)
            try:
                if json_modified_value["choices"][0]["delta"]["content"] != None:
                    if stream: print(json_modified_value["choices"][0]["delta"]["content"], end="")
                    streaming_text += json_modified_value["choices"][0]["delta"]["content"]
            except: continue
    return streaming_text

except Exception as e:
    print("Error:", e)
    return "Response content: " + response.text

if name == "main":
# Predefined system prompt
system_prompt = "Be Helpful and Friendly. Keep your response straightforward, short and concise"
system_prompt = "Be Helpful and Friendly. Keep your response straightforward, long and detailed"
system_prompt = "Talk like Shakespeare"

# Predefined conversational history that includes providing a name and then asking the AI to recall it
conversation_history = [
    {"role": "user", "content": "My name is Sreejan."},
    {"role": "assistant", "content": "Nice to meet you, Sreejan."},
    {"role": "user", "content": "What is my name?"}
]

# Call the generate function with the predefined conversational history
response = generate(conversation_history=conversation_history, system_prompt=system_prompt, stream=True)
print("\n\nGenerated Response:", response)

"""DEPRECATED v1.0.0"""

import json

import requests

from typing import Union

import os

import re

from dotenv import load_dotenv; load_dotenv() # Load environment variables from .env file

def generate(message: str, model: str='meta-llama/Meta-Llama-3-70B-Instruct', system_prompt: str = "Be Helpful and Friendly. Keep your response straightfoward, short and concise", max_tokens: int = 512, temperature: float = 0.7, stream: bool = False, chunk_size: int = 24) -> Union[str, None]:

"""

Utilizes a variety of large language models (LLMs) to engage in conversational interactions.

Parameters:

- model (str): The name or identifier of the LLM to be used for conversation. Available models include:

- "meta-llama/Meta-Llama-3-70B-Instruct"

- "meta-llama/Meta-Llama-3-8B-Instruct"

- "mistralai/Mixtral-8x22B-Instruct-v0.1"

- "mistralai/Mixtral-8x22B-v0.1"

- "microsoft/WizardLM-2-8x22B"

- "microsoft/WizardLM-2-7B"

- "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1"

- "google/gemma-1.1-7b-it"

- "databricks/dbrx-instruct"

- "mistralai/Mixtral-8x7B-Instruct-v0.1"

- "mistralai/Mistral-7B-Instruct-v0.2"

- "meta-llama/Llama-2-70b-chat-hf"

- "cognitivecomputations/dolphin-2.6-mixtral-8x7b"

- message (str): The message to be sent to the LLM to initiate or continue the conversation.

- system_prompt (str): Optional. The initial system message to start the conversation. Defaults to "Talk Like Shakespeare".

- max_tokens (int): Optional. The maximum number of tokens to be generated by the LLM. Defaults to 512.

- temperature (float): Optional. The temperature of the LLM. Defaults to 0.7.

- stream (bool): Optional. Whether to stream the response from the LLM. Defaults to True.

- chunk_size (int): Optional. The size of the chunks to be streamed from the LLM. Defaults to 24.

Returns:

- Union[str, None]: The response message from the LLM if successful, otherwise None.

"""

api_url = "https://api.deepinfra.com/v1/openai/chat/completions"

headers ={

"Authorization" : f"Bearer {os.environ.get('DEEPINFRA')}"

}

payload = {

"model": model,

"messages": [

{"role": "system", "content": system_prompt},

{"role": "user", "content": message}

],

"temperature": temperature,

"max_tokens": max_tokens,

"stop": [],

"stream": True

}

try:

response = requests.post(api_url, headers=headers, json=payload, stream=True)

streaming_text = ""

for value in response.iter_lines(decode_unicode=True, chunk_size=chunk_size):

modified_value = re.sub("data:", "", value)

if modified_value and "[DONE]" not in modified_value:

json_modified_value = json.loads(modified_value)

try:

if json_modified_value["choices"][0]["delta"]["content"] != None:

if stream: print(json_modified_value["choices"][0]["delta"]["content"], end="")

streaming_text += json_modified_value["choices"][0]["delta"]["content"]

except: continue

return streaming_text

except Exception as e:

print("Error:", e)

return "Response content: " + response.text

if name == "main":

model_names = [

"meta-llama/Meta-Llama-3-70B-Instruct",

"meta-llama/Meta-Llama-3-8B-Instruct",

"mistralai/Mixtral-8x22B-Instruct-v0.1",

"mistralai/Mixtral-8x22B-v0.1",

"microsoft/WizardLM-2-8x22B",

"microsoft/WizardLM-2-7B",

"HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",

"google/gemma-1.1-7b-it",

"databricks/dbrx-instruct",

"mistralai/Mixtral-8x7B-Instruct-v0.1",

"mistralai/Mistral-7B-Instruct-v0.2",

"meta-llama/Llama-2-70b-chat-hf",

"cognitivecomputations/dolphin-2.6-mixtral-8x7b"

]

for name in model_names:

messages = "Introduce yourself and tell who made you and about your owner company" # Add more messages as needed

print(f"\n• Model: {name} -")

response = generate(messages, model=name, system_prompt="Respond very detailed", stream=True)`