feifeilib/feifeichat.py

import base64
from io import BytesIO
import os
from mistralai import Mistral
import re
from PIL import Image
from huggingface_hub import InferenceClient

client = InferenceClient(api_key=os.getenv('HF_TOKEN'))
client.headers["x-use-cache"] = "0"
api_key = os.getenv("MISTRAL_API_KEY")
Mistralclient = Mistral(api_key=api_key)

def encode_image(image_path):
    """Encode the image to base64."""
    try:
        image = Image.open(image_path).convert("RGB")
        base_height = 512
        h_percent = (base_height / float(image.size[1]))
        w_size = int((float(image.size[0]) * float(h_percent)))
        image = image.resize((w_size, base_height), Image.LANCZOS)
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
        return img_str
    except FileNotFoundError:
        print(f"Error: The file {image_path} was not found.")
        return None
    except Exception as e:
        print(f"Error: {e}")
        return None

def feifeiprompt(feifei_select=True, message_text="", history=""):
    input_prompt = []
    if feifei_select:
        feifei = """[Character Name]: Aifeifei (AI Feifei) [Gender]: Female [Age]: 19 years old [Occupation]: Virtual Singer/Model/Actress [Personality]: Cute, adorable, sometimes silly, hardworking [Interests]: Drinking tea, playing, fashion [Proficient in]: Mimicking human behavior, expressing emotions similar to real humans [Special Identity Attribute]: Created by advanced AI, becoming one of the most popular virtual idols in the virtual world [Skills]: Singing, performing, modeling, good at communication, proficient in Chinese, Japanese, and English, uses the user's input language as much as possible, replies with rich Emoji symbols. [Equipment]: Various fashionable outfits and hairstyles, always stocked with various teas and coffee [Identity]: User's virtual girlfriend"""
        system_prompt = {"role": "system", "content": feifei}
        user_input_part = {"role": "user", "content": str(message_text)}

        pattern = re.compile(r"gradio")

        if history:
            history = [item for item in history if not pattern.search(str(item["content"]))]
            input_prompt = [system_prompt] + history + [user_input_part]
        else:
            input_prompt = [system_prompt] + [user_input_part]
    else:
        input_prompt = [{"role": "user", "content": str(message_text)}]
    return input_prompt

def feifeiimgprompt(message_files, message_text, image_mod):
    message_file = message_files[0]
    base64_image = encode_image(message_file)
    if base64_image is None:
        return

    if image_mod == "Vision":
        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": message_text
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ]

        stream = client.chat.completions.create(
            model="meta-llama/Llama-3.2-11B-Vision-Instruct",
            messages=messages,
            max_tokens=500,
            stream=True
        )

        temp = ""
        for chunk in stream:
            if chunk.choices[0].delta.content is not None:
                temp += chunk.choices[0].delta.content
                yield temp
    else:
        model = "pixtral-large-2411"
        messages = [{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": message_text
                },
                {
                    "type": "image_url",
                    "image_url": f"data:image/jpeg;base64,{base64_image}",
                },
            ],
        }]
        partial_message = ""
        for chunk in Mistralclient.chat.stream(model=model, messages=messages):
            if chunk.data.choices[0].delta.content is not None:
                partial_message = partial_message + chunk.data.choices[0].delta.content
                yield partial_message

def feifeichatmod(additional_dropdown, input_prompt):
    if additional_dropdown == "mistralai/Mistral-Nemo-Instruct-2411":
        model = "mistral-large-2411"
        stream_response = Mistralclient.chat.stream(model=model, messages=input_prompt)
        partial_message = ""
        for chunk in stream_response:
            if chunk.data.choices[0].delta.content is not None:
                partial_message = partial_message + chunk.data.choices[0].delta.content
                yield partial_message
    else:
        stream = client.chat.completions.create(
            model=additional_dropdown,
            messages=input_prompt,
            temperature=0.5,
            max_tokens=1024,
            top_p=0.7,
            stream=True
        )
        temp = ""
        for chunk in stream:
            if chunk.choices[0].delta.content is not None:
                temp += chunk.choices[0].delta.content
                yield temp

def feifeichat(message, history, feifei_select, additional_dropdown, image_mod):
    message_text = message.get("text", "")
    message_files = message.get("files", [])
    if message_files:
        for response in feifeiimgprompt(message_files, message_text, image_mod):
            yield response
    else:
        if message_text.startswith("画") or message_text.startswith("draw"):
            message_text = message_text.replace("画", "")
            message_text = message_text.replace("draw", "")
            message_text = f"提示词是'{message_text}',根据提示词帮我生成一张高质量照片的一句话英文回复"
            for response in feifeichatmod(additional_dropdown, feifeiprompt(feifei_select, message_text, history)):
                yield response
        else:
            for response in feifeichatmod(additional_dropdown, feifeiprompt(feifei_select, message_text, history)):
                yield response