只提交1个

llm.py

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+import io
+from PIL import Image
+import os
+
+from openai import OpenAI
+from  conf import *
+from tos import HttpMethodType
+import time
+from openai import OpenAI
+import os
+import base64
+from PIL import Image
+
+def image_to_base64(image):
+    # 将Image对象转换为BytesIO对象
+    image_io = io.BytesIO()
+    image.save(image_io, format='PNG')
+    image_io.seek(0)
+
+    # 使用base64编码
+    image_base64 = base64.b64encode(image_io.read()).decode('utf-8')
+
+    return f"data:image/png;base64,{image_base64}"
+
+def image_reader(image):
+    """图片读取器,输出PIL.Image格式的图片"""
+    if isinstance(image,str):
+        if image.startswith("http"):
+            return image
+        else:
+            image_path = image
+        out_image = Image.open(image_path)
+    elif isinstance(image,np.ndarray):
+        out_image = Image.fromarray(image)
+    else:
+        out_image = image
+    out_image=out_image.convert('RGB')
+    base64_img=image_to_base64(out_image)
+    return base64_img
+def get_lm_text(sys_prompt,user_prompt):
+    completion = LMConfig.lm_client.chat.completions.create(
+        messages = [
+            {"role": "system", "content": sys_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        model=LMConfig.model,
+    )
+
+    return completion.choices[0].message.content
+
+
+
+
+## 多模态的输入
+def compress_image(input_path, output_path):
+    img = Image.open(input_path)
+    current_size = os.path.getsize(input_path)
+    # 粗略的估计压缩质量，也可以从常量开始，逐步减小压缩质量，直到文件大小小于目标大小
+    image_quality = int(float(MMMConfig.target_size / current_size) * 100)
+    img.save(output_path, optimize=True, quality=int(float(MMMConfig.target_size / current_size) * 100))
+    # 如果压缩后文件大小仍然大于目标大小，则继续压缩
+    # 压缩质量递减，直到文件大小小于目标大小
+    while os.path.getsize(output_path) > MMMConfig.target_size:
+        img = Image.open(output_path)
+        image_quality -= 10
+        if image_quality <= 0:
+            break
+        img.save(output_path, optimize=True, quality=image_quality)
+    return image_quality
+
+def upload_tos(filename,  tos_object_key):
+
+    tos_client, inner_tos_client = MMMConfig.tos_client, MMMConfig.inner_tos_client
+    try:
+        # 将本地文件上传到目标桶中, filename为本地压缩后图片的完整路径
+        tos_client.put_object_from_file(MMMConfig.tos_bucket_name, tos_object_key, filename)
+        # 获取上传后预签名的 url
+        return inner_tos_client.pre_signed_url(HttpMethodType.Http_Method_Get, MMMConfig.tos_bucket_name, tos_object_key)
+    except Exception as e:
+        if isinstance(e, tos.exceptions.TosClientError):
+            # 操作失败，捕获客户端异常，一般情况为非法请求参数或网络异常
+            print('fail with client error, message:{}, cause: {}'.format(e.message, e.cause))
+        elif isinstance(e, tos.exceptions.TosServerError):
+            # 操作失败，捕获服务端异常，可从返回信息中获取详细错误信息
+            print('fail with server error, code: {}'.format(e.code))
+            # request id 可定位具体问题，强烈建议日志中保存
+            print('error with request id: {}'.format(e.request_id))
+            print('error with message: {}'.format(e.message))
+            print('error with http code: {}'.format(e.status_code))
+
+        else:
+
+            print('fail with unknown error: {}'.format(e))
+
+        raise e
+
+
+
+def doubao_MMM_request(pre_signed_url_output, prompt):
+
+    client = MMMConfig.client
+    
+
+    response = client.chat.completions.create(
+        model=MMMConfig.model,
+        messages=[{"role": "user","content": [
+                {"type": "text", "text": prompt},
+                {"type": "image_url", "image_url": {"url": pre_signed_url_output.signed_url}}
+            ],
+        }],
+        temperature=0.8,
+        extra_headers={"x-ark-beta-vision": "true"}
+    )
+    result = response.choices[0].message.content
+    return result
+
+
+class llm_request:
+    def __init__(self,api_key,base_url,model) -> None:
+        self.api_key=api_key
+        self.base_url=base_url
+        self.model=model
+
+
+    def llm_mm_request(self,usr_text,img,sys_text="You are a helpful assistant."):
+        client = OpenAI(
+        # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
+        api_key=self.api_key,
+        base_url=self.base_url
+    )
+        completion = client.chat.completions.create(
+            model=self.model,#
+            messages=[
+                {
+                    "role": "system",
+                    "content": [{"type":"text","text": sys_text}]},
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            # 需要注意，传入Base64，图像格式（即image/{format}）需要与支持的图片列表中的Content Type保持一致。"f"是字符串格式化的方法。
+                            # PNG图像：  f"data:image/png;base64,{base64_image}"
+                            # JPEG图像： f"data:image/jpeg;base64,{base64_image}"
+                            # WEBP图像： f"data:image/webp;base64,{base64_image}"
+                            "image_url": {"url": image_reader(img)}, 
+                        },
+                        {"type": "text", "text": usr_text},
+                    ],
+                }
+
+            ],
+            temperature=1.5,
+            top_p=0.85, 
+            presence_penalty=1.5, 
+            frequency_penalty=1.5,
+        )
+        return completion.choices[0].message.content
+    
+    def llm_text_request(self,text,sys_text="You are a helpful assistant."):
+        client = OpenAI(
+        # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
+        api_key=self.api_key,
+        base_url=self.base_url
+    )
+        completion = client.chat.completions.create(
+            model=self.model,#
+            messages=[
+                {
+                    "role": "system",
+                    "content": sys_text},
+                {
+                    "role": "user",
+                    "content":  text,
+                }
+            ],
+            temperature=0.9,
+        )
+        return completion.choices[0].message.content
+    
+
+
+if __name__=="__main__":
+    ##ali
+    ky="sk-04b63960983445f980d85ff185a17876"
+    baseurl="https://dashscope.aliyuncs.com/compatible-mode/v1"
+    model="qwen-vl-max-latest"
+    ##doubao
+    # ky='817dff39-5586-4f9b-acba-55004167c0b1'
+    # baseurl="https://ark.cn-beijing.volces.com/api/v3"
+    # model="doubao-1-5-vision-pro-32k-250115"
+    llm=llm_request(ky,baseurl,model)
+    res1=llm.llm_mm_request("描述一下图片中的衣服","/data/data/Mia/product_env_project/gen_sellpoint/企业微信截图_17372766091671.png")
+    print(res1)
+    res2=llm.llm_text_request("你好！你是谁")
+    print(res2)