Create app.py

app.py

@@ -0,0 +1,126 @@
+Hugging Face's logo
+Hugging Face
+Models
+Datasets
+Spaces
+Posts
+Docs
+Enterprise
+Pricing
+
+
+
+Hugging Face is way more fun with friends and colleagues! 🤗 Join an organization
+Spaces:
+
+Qwen
+/
+Qwen2.5-Turbo-1M-Demo
+
+
+like
+359
+App
+Files
+Community
+6
+Qwen2.5-Turbo-1M-Demo
+/
+app.py
+
+feihu.hf
+update file types
+602373e
+4 months ago
+raw
+
+Copy download link
+history
+blame
+contribute
+delete
+
+3.4 kB
+import os
+os.system("pip install 'https://modelscope-studios.oss-cn-zhangjiakou.aliyuncs.com/SDK/gradio/gradio-4.44.0-py3-none-any.whl?OSSAccessKeyId=LTAI5tCGZWFdkWKivGKCtvTD&Expires=361727611665&Signature=iynlOFVFiaF3OmxatNMHUBPfb3o%3D'")
+os.system("pip install starlette==0.38.6 fastapi==0.112.4")
+
+from typing import List, Tuple, Union
+from web_ui import WebUI
+import math
+
+from qwen_agent.agents import Assistant
+from qwen_agent.tools.base import register_tool
+from qwen_agent.tools.doc_parser import Record
+from qwen_agent.tools.search_tools.base_search import RefMaterialOutput, BaseSearch
+from qwen_agent.log import logger
+from qwen_agent.gui.gradio import gr
+
+POSITIVE_INFINITY = math.inf
+
+@register_tool('no_search')
+class NoSearch(BaseSearch):
+    def call(self, params: Union[str, dict], docs: List[Union[Record, str, List[str]]] = None, **kwargs) -> list:
+        """The basic search algorithm
+        Args:
+            params: The dict parameters.
+            docs: The list of parsed doc, each doc has unique url.
+        Returns:
+            The list of retrieved chunks from each doc.
+        """
+        params = self._verify_json_format_args(params)
+        # Compatible with the parameter passing of the qwen-agent version <= 0.0.3
+        max_ref_token = kwargs.get('max_ref_token', self.max_ref_token)
+
+        # The query is a string that may contain only the original question,
+        # or it may be a json string containing the generated keywords and the original question
+        if not docs:
+            return []
+        return self._get_the_front_part(docs, max_ref_token)
+
+    @staticmethod
+    def _get_the_front_part(docs: List[Record], max_ref_token: int) -> list:
+        all_tokens = 0
+        _ref_list = []
+        for doc in docs:
+            text = []
+            for page in doc.raw:
+                text.append(page.content)
+                all_tokens += page.token
+            now_ref_list = RefMaterialOutput(url=doc.url, text=text).to_dict()
+            _ref_list.append(now_ref_list)
+
+        logger.info(f'Using tokens: {all_tokens}')
+        if all_tokens > max_ref_token:
+            raise gr.Error(f"Your document files (around {all_tokens} tokens) exceed the maximum context length ({max_ref_token} tokens).")
+        return _ref_list
+
+    def sort_by_scores(self,
+                       query: str,
+                       docs: List[Record],
+                       max_ref_token: int,
+                       **kwargs) -> List[Tuple[str, int, float]]:
+        raise NotImplementedError
+
+def app_gui():
+    # Define the agent
+    bot = Assistant(llm={
+                    'model': 'qwen-turbo-1101',
+                    'generate_cfg': {
+                        'max_input_tokens': 1000000,
+                        'max_retries': 10,
+                    }},
+                    name='Qwen-Turbo-1M',
+                    description='Qwen-Turbo natively supports input length of up to 1M tokens. You can upload documents for Q&A (eg., pdf/docx/pptx/txt/html).',
+                    rag_cfg={'max_ref_token': 1000000, 'rag_searchers': ['no_search']},
+                )
+    chatbot_config = {
+        'input.placeholder': "Type \"/clear\" to clear the history",
+        'verbose': True,
+    }
+    WebUI(bot, chatbot_config=chatbot_config).run()
+
+if __name__ == '__main__':
+    import patching # patch qwen-agent to accelerate 1M processing
+    app_gui()
+