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3fcdba1683
### What problem does this PR solve? #762 ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
324 lines
12 KiB
Python
324 lines
12 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from openai.lib.azure import AzureOpenAI
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from zhipuai import ZhipuAI
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import io
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from abc import ABC
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from ollama import Client
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from PIL import Image
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from openai import OpenAI
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import os
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import base64
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from io import BytesIO
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import json
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import requests
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from api.utils import get_uuid
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from api.utils.file_utils import get_project_base_directory
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class Base(ABC):
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def __init__(self, key, model_name):
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pass
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def describe(self, image, max_tokens=300):
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raise NotImplementedError("Please implement encode method!")
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def image2base64(self, image):
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if isinstance(image, bytes):
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return base64.b64encode(image).decode("utf-8")
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if isinstance(image, BytesIO):
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return base64.b64encode(image.getvalue()).decode("utf-8")
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buffered = BytesIO()
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try:
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image.save(buffered, format="JPEG")
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except Exception as e:
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image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode("utf-8")
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def prompt(self, b64):
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return [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{b64}"
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},
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},
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{
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"text": "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else
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"Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out.",
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},
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],
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}
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]
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class GptV4(Base):
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def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1"):
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if not base_url: base_url="https://api.openai.com/v1"
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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def describe(self, image, max_tokens=300):
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b64 = self.image2base64(image)
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prompt = self.prompt(b64)
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for i in range(len(prompt)):
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for c in prompt[i]["content"]:
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if "text" in c: c["type"] = "text"
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=prompt,
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip(), res.usage.total_tokens
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class AzureGptV4(Base):
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def __init__(self, key, model_name, lang="Chinese", **kwargs):
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self.client = AzureOpenAI(api_key=key, azure_endpoint=kwargs["base_url"], api_version="2024-02-01")
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self.model_name = model_name
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self.lang = lang
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def describe(self, image, max_tokens=300):
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b64 = self.image2base64(image)
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prompt = self.prompt(b64)
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for i in range(len(prompt)):
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for c in prompt[i]["content"]:
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if "text" in c: c["type"] = "text"
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=prompt,
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip(), res.usage.total_tokens
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class QWenCV(Base):
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def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese", **kwargs):
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import dashscope
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dashscope.api_key = key
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self.model_name = model_name
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self.lang = lang
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def prompt(self, binary):
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# stupid as hell
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tmp_dir = get_project_base_directory("tmp")
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if not os.path.exists(tmp_dir):
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os.mkdir(tmp_dir)
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path = os.path.join(tmp_dir, "%s.jpg" % get_uuid())
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Image.open(io.BytesIO(binary)).save(path)
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return [
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{
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"role": "user",
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"content": [
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{
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"image": f"file://{path}"
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},
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{
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"text": "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else
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"Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out.",
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},
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],
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}
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]
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def describe(self, image, max_tokens=300):
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from http import HTTPStatus
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from dashscope import MultiModalConversation
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response = MultiModalConversation.call(model=self.model_name,
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messages=self.prompt(image))
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if response.status_code == HTTPStatus.OK:
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return response.output.choices[0]['message']['content'][0]["text"], response.usage.output_tokens
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return response.message, 0
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class Zhipu4V(Base):
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def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
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self.client = ZhipuAI(api_key=key)
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self.model_name = model_name
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self.lang = lang
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def describe(self, image, max_tokens=1024):
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b64 = self.image2base64(image)
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=self.prompt(b64),
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip(), res.usage.total_tokens
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class OllamaCV(Base):
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def __init__(self, key, model_name, lang="Chinese", **kwargs):
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self.client = Client(host=kwargs["base_url"])
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self.model_name = model_name
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self.lang = lang
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def describe(self, image, max_tokens=1024):
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prompt = self.prompt("")
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try:
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options = {"num_predict": max_tokens}
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response = self.client.generate(
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model=self.model_name,
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prompt=prompt[0]["content"][1]["text"],
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images=[image],
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options=options
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)
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ans = response["response"].strip()
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return ans, 128
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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class LocalAICV(Base):
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def __init__(self, key, model_name, base_url, lang="Chinese"):
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self.client = OpenAI(api_key="empty", base_url=base_url)
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self.model_name = model_name.split("___")[0]
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self.lang = lang
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def describe(self, image, max_tokens=300):
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if not isinstance(image, bytes) and not isinstance(
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image, BytesIO
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): # if url string
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prompt = self.prompt(image)
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for i in range(len(prompt)):
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prompt[i]["content"]["image_url"]["url"] = image
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else:
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b64 = self.image2base64(image)
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prompt = self.prompt(b64)
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for i in range(len(prompt)):
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for c in prompt[i]["content"]:
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if "text" in c:
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c["type"] = "text"
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=prompt,
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip(), res.usage.total_tokens
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class XinferenceCV(Base):
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def __init__(self, key, model_name="", lang="Chinese", base_url=""):
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self.client = OpenAI(api_key="xxx", base_url=base_url)
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self.model_name = model_name
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self.lang = lang
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def describe(self, image, max_tokens=300):
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b64 = self.image2base64(image)
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=self.prompt(b64),
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip(), res.usage.total_tokens
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class GeminiCV(Base):
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def __init__(self, key, model_name="gemini-1.0-pro-vision-latest", lang="Chinese", **kwargs):
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from google.generativeai import client,GenerativeModel
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client.configure(api_key=key)
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_client = client.get_default_generative_client()
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self.model_name = model_name
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self.model = GenerativeModel(model_name=self.model_name)
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self.model._client = _client
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self.lang = lang
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def describe(self, image, max_tokens=2048):
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from PIL.Image import open
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gen_config = {'max_output_tokens':max_tokens}
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prompt = "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else \
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"Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
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b64 = self.image2base64(image)
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img = open(BytesIO(base64.b64decode(b64)))
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input = [prompt,img]
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res = self.model.generate_content(
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input,
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generation_config=gen_config,
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)
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return res.text,res.usage_metadata.total_token_count
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class OpenRouterCV(Base):
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def __init__(
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self,
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key,
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model_name,
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lang="Chinese",
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base_url="https://openrouter.ai/api/v1/chat/completions",
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):
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self.model_name = model_name
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self.lang = lang
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self.base_url = "https://openrouter.ai/api/v1/chat/completions"
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self.key = key
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def describe(self, image, max_tokens=300):
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b64 = self.image2base64(image)
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response = requests.post(
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url=self.base_url,
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headers={
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"Authorization": f"Bearer {self.key}",
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},
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data=json.dumps(
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{
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"model": self.model_name,
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"messages": self.prompt(b64),
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"max_tokens": max_tokens,
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}
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),
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)
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response = response.json()
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return (
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response["choices"][0]["message"]["content"].strip(),
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response["usage"]["total_tokens"],
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)
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def prompt(self, b64):
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return [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{b64}"},
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},
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{
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"type": "text",
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"text": (
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"请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。"
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if self.lang.lower() == "chinese"
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else "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
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),
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},
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],
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}
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]
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class LocalCV(Base):
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def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
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pass
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def describe(self, image, max_tokens=1024):
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return "", 0
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