From 0b168057cabc3df17828449c56ecb8bfc88730fb Mon Sep 17 00:00:00 2001 From: Tordor <3262978839@qq.com> Date: Fri, 30 Jan 2026 15:50:52 +0800 Subject: [PATCH] Refactor inference and main modules for improved functionality and add uvicorn dependency --- feature_extraction.py | 3 ++- inference.py | 43 ++++++++++++++++++++++++++++++++++- main.py | 41 +++++++++++++++++++++++++++++---- pyproject.toml | 2 ++ uv.lock | 53 +++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 136 insertions(+), 6 deletions(-) diff --git a/feature_extraction.py b/feature_extraction.py index f550dea..f453f81 100644 --- a/feature_extraction.py +++ b/feature_extraction.py @@ -23,7 +23,8 @@ OUTPUT_DIR = os.getenv("OUTPUT_DIR", "time_12_1/data_ch_1") client = OpenAI( api_key=API_KEY, # base_url="https://dashscope.aliyuncs.com/compatible-mode/v1" - base_url="https://sg1.proxy.yinlihupo.cc/proxy/https://openrouter.ai/api/v1" + # https://sg1.proxy.yinlihupo.cc/proxy/ + base_url="https://openrouter.ai/api/v1" ) diff --git a/inference.py b/inference.py index f8347d3..b347644 100644 --- a/inference.py +++ b/inference.py @@ -60,7 +60,8 @@ class InferenceEngine: 请注意Json文件中的词条数必须大于等于10. """ assert len(json_list) <= 8, "单次输入json文件数量不可超过8。" - id2feature = extract_json_data(json_list) # id2feature + id2feature = extract_json_data(json_list) + # print(id2feature) # id2feature message_list = [] for id, feature in id2feature.items(): @@ -97,6 +98,46 @@ class InferenceEngine: # 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表 return {"labels": preds, "probs": probs} + def inference( + self, + featurs : dict[str ,dict] + ): + assert len(featurs) <= 8, "单次输入json文件数量不可超过8。" + message_list = [] + for id, feature in featurs.items(): + messages = self.formatter.get_llm_prompt(feature) + message_list.append(messages) + + inputs = self.tokenizer.apply_chat_template( + message_list, + tokenize=False, + add_generation_prompt=True, + enable_thinking=False + ) + model_inputs = self.tokenizer( + inputs, + padding=True, + truncation=True, + max_length=2048, + return_tensors="pt" + ).to(self.device) + + with torch.inference_mode(): + with torch.amp.autocast(device_type=self.device, dtype=torch.bfloat16): + outputs = self.model(model_inputs) + + # 1. 计算分类标签（argmax） + preds = torch.argmax(outputs, dim=1).cpu().numpy().tolist() + + # 2. 计算softmax概率（核心修正：转CPU、转numpy、转列表，解决Tensor序列化问题） + outputs_float = outputs.float() # 转换为 float32 避免精度问题 + probs = torch.softmax(outputs_float, dim=1) # probs: [B, 2] + # 转换为CPU的numpy数组，再转列表（每个样本对应2个类别的概率） + probs = probs.cpu().numpy().tolist() + + # 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表 + return {"labels": preds, "probs": probs} + def inference_sample(self, json_path: str) -> dict: """ 单样本推理函数，输入为 JSON 字符串路径，输出为包含转换概率的字典。 diff --git a/main.py b/main.py index b54a1f5..fed4a9d 100644 --- a/main.py +++ b/main.py @@ -1,7 +1,9 @@ from feature_extraction import process_single from inference import engine from services.mongo import voice_collection - +import json,uuid +from fastapi import FastAPI +from pydantic import BaseModel async def get_customer_record(): cursor = voice_collection.find({ @@ -12,12 +14,43 @@ async def get_customer_record(): } } }).sort([('_id', -1)]).limit(24) - return await cursor.to_list(length=24) + return await cursor.to_list(length=4) + async def main(): - records = await get_customer_record() + records = await get_customer_record() for record in records: - print(record) + # print(len(record["text_content"])) + data = await process_single(record["text_content"][:10000]) + # print(json.dumps(data, indent=2 , ensure_ascii=False)) + temp = {} + res = {} + for key ,value in data.items(): + temp[key] = value.get("value") or "" + res[uuid.uuid4().hex] = temp + print(engine.inference(res)) + + + +app = FastAPI() + +class Predictbody(BaseModel): + content : str + +@app.post("/predict") +async def endpoint(body : Predictbody): + data = await process_single(body.content[:10000]) + temp = {} + res = {} + for key ,value in data.items(): + temp[key] = value.get("value") or "" + res[uuid.uuid4().hex] = temp + return { + "feature" : data, + "predict" : engine.inference(res) + } + + if __name__ == "__main__": import asyncio diff --git a/pyproject.toml b/pyproject.toml index f48caca..c029ba2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,6 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ + "fastapi>=0.128.0", "matplotlib>=3.10.8", "motor>=3.7.1", "openai>=2.16.0", @@ -12,4 +13,5 @@ dependencies = [ "python-dotenv>=1.2.1", "torch>=2.10.0", "transformers>=5.0.0", + "uvicorn>=0.40.0", ] diff --git a/uv.lock b/uv.lock index dbd1670..ecb567a 100644 --- a/uv.lock +++ b/uv.lock @@ -10,6 +10,15 @@ resolution-markers = [ "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] +[[package]] +name = "annotated-doc" +version = "0.0.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = 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