import os
import dashscope
from dashscope import MultiModalConversation
dashscope.base_http_api_url = 'https://[workspace-id].ap-southeast-1.maas.aliyuncs.com/api/v1'
messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
            {"text": "Solve this problem"}
        ]
    }
]

response = MultiModalConversation.call(
    # If you use a model in the China (Beijing) region, you must use an API key from that region. Get the key at https://bailian.console.alibabacloud.com/?tab=model#/api-key
    # If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model="qvq-max",  # qvq-max is used as an example. You can replace it with another model name as needed
    messages=messages,
    stream=True,
)

# Define full thinking process
reasoning_content = ""
# Define full response
answer_content = ""
# Determine whether the chain-of-thought has ended and the response has begun
is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in response:
    # If both the thinking process and the response is empty, ignore this
    message = chunk.output.choices[0].message
    reasoning_content_chunk = message.get("reasoning_content", None)
    if (chunk.output.choices[0].message.content == [] and
        reasoning_content_chunk == ""):
        pass
    else:
        # It this is thinking process
        if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
            print(chunk.output.choices[0].message.reasoning_content, end="")
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        # It this is response
        elif chunk.output.choices[0].message.content != []:
            if not is_answering:
                print("\n" + "=" * 20 + "Full response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content[0]["text"], end="")
            answer_content += chunk.output.choices[0].message.content[0]["text"]

# To print the full thinking process and response, uncomment and run the following
# print("=" * 20 + "Thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")