Create app.py

app.py

@@ -0,0 +1,87 @@
+import os
+import subprocess
+
+subprocess.run(["git", "lfs", "install"])
+subprocess.run(["git", "clone", "https://huggingface.co/K024/ChatGLM-6b-onnx-u8s8"])
+os.chdir("ChatGLM-6b-onnx-u8s8")
+subprocess.run(["pip", "install", "-r", "requirements.txt"])
+
+from model import ChatGLMModel#, chat_template
+
+model = ChatGLMModel()
+# history = []
+
+max_tokens = 512
+temperature = 1.0
+top_p = 0.7
+top_k = 50
+
+from typing import Any, List, Mapping, Optional
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+
+class CustomLLM(LLM):
+    model: ChatGLMModel
+    # history: List
+
+    @property
+    def _llm_type(self) -> str:
+        return "custom"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    ) -> str:
+        if stop is not None:
+            raise ValueError("stop kwargs are not permitted.")
+        # prompt = chat_template(self.history, prompt)
+        for answer in self.model.generate_iterate(prompt,
+                            max_generated_tokens=max_tokens,
+                            top_k=top_k,
+                            top_p=top_p,
+                            temperature=temperature):
+          pass
+
+        # self.history = self.history + [(question, answer)]
+        return answer
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {"model": "ChatGLMModel"}
+
+
+llm = CustomLLM(model=model)
+
+import gradio as gr
+from langchain.docstore.document import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.question_answering import load_qa_chain
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import Chroma
+
+# embeddings = HuggingFaceEmbeddings()
+query = "總結並以點列形式舉出重點"
+chain = load_qa_chain(llm, chain_type="map_reduce")
+# chain = load_qa_chain(llm, chain_type="refine")
+
+def greet(text):
+    docs = [Document(page_content=text)]
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1024, # 分割最大尺寸
+        chunk_overlap=64, # 重复字数
+        length_function=len
+    )
+    texts = text_splitter.split_documents(docs)
+    # docsearch = Chroma.from_texts(texts, embeddings).as_retriever()
+    # docs = docsearch.get_relevant_documents(query)
+    return chain.run(input_documents=texts, question=query)
+
+iface = gr.Interface(fn=greet,
+                     inputs=gr.Textbox(lines=20,
+                                       placeholder="Text Here..."),
+                     outputs="text")
+iface.launch()