Reinitialize project and track PNG via LFS

.gitattributes

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+*.csv filter=lfs diff=lfs merge=lfs -text
+*.h5ad filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

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+# app.py (主入口文件)
+import os
+from fastapi import FastAPI, File, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+import gradio as gr
+import uvicorn
+import aiofiles
+from typing import List
+import service
+
+# ---------- 原 controller.py 的核心逻辑 ----------
+uploadPath = "./data/latents/"
+
+# 初始化 FastAPP
+app = FastAPI()
+
+# CORS 配置（保持与 controller.py 一致）
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173"],  # 前端开发地址
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ---------- 原 controller.py 的路由 ----------
+@app.get("/dataList/{datatype}")
+async def read_data(datatype):
+    return service.getALlData() # 示例数据，替换为实际 service 调用
+
+# 定义带路径参数的 GET 请求处理函数
+@app.get("/details/{name}")
+async def read_item(name: str = None):
+    data = service.getListByName("integration_accuracy.csv", name)
+    return data
+
+@app.post("/upload")
+async def upload_files(files: List[UploadFile] = File(...)):
+    for file in files:
+        save_path = os.path.join(uploadPath, file.filename)
+        try:
+            async with aiofiles.open(save_path, 'wb') as out_file:
+                content = await file.read()
+                await out_file.write(content)
+        except Exception as e:
+            return {"message": f"Error: {e}"}
+        finally:
+            await file.close()
+    return {"success": True, "message": "Files uploaded"}
+
+
+# 挂载 Vue3 前端静态文件（部署时需要）
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+
+# ---------- 集成 Gradio 接口 ----------
+def gradio_predict(text):
+    return f"Processed: {text}"
+
+gradio_ui = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Textbox(label="输入文本"),
+    outputs=gr.Textbox(label="处理结果"),
+    title="Gradio 交互界面"
+)
+
+# 将 Gradio 挂载到 /gradio 路径
+app = gr.mount_gradio_app(app, gradio_ui, path="/gradio")
+
+# ---------- 启动配置 ----------
+if __name__ == "__main__":
+    # Hugging Face Spaces 使用 PORT 环境变量
+    port = int(os.getenv("PORT", 8000))
+    uvicorn.run(app, host="0.0.0.0", port=port)

controller.py

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+import os
+import uvicorn
+import aiofiles
+from fastapi import FastAPI, HTTPException,  File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+from typing import List
+
+import service
+
+uploadPath = "./data/latents/"
+
+
+app = FastAPI()
+# 配置 CORS 中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173"],  # 允许的前端地址
+    allow_credentials=True,                  # 是否允许发送 Cookie
+    allow_methods=["*"],                     # 允许的 HTTP 方法（GET、POST、PUT 等）
+    allow_headers=["*"],                     # 允许的请求头
+)
+
+# 定义根路径的 GET 请求处理函数
+@app.get("/")
+async def read_root():
+    return {"message": "Hello, World!"}
+
+@app.get("/dataList/{datatype}")
+async def read_data(datatype):
+    return service.getALlData()
+        
+
+
+    
+
+# 定义带路径参数的 GET 请求处理函数
+@app.get("/details/{name}")
+async def read_item(name: str = None):
+    data = service.getListByName("integration_accuracy.csv", name)
+    return data
+
+# 定义 POST 请求处理函数
+@app.post("/upload")
+async def upload_files(files:List[UploadFile] = File(...)):
+    for file in files:
+        save_path = os.path.join(uploadPath, file.filename)
+        try:
+            async with aiofiles.open(save_path, 'wb') as out_file:
+                content = await file.read()
+                await out_file.write(content)
+        except Exception as e:
+            return {"message": f"出现错误: {e}"}
+        finally:
+            # 关闭文件
+            await file.close()
+    return {"success":True, "message": f"文件 {file.filename} 已成功保存"}
+
+if __name__ == "__main__":
+    # 从环境变量中读取端口，默认为 8000
+    port = int(os.getenv("APP_PORT", 8000))
+    uvicorn.run(app, host="127.0.0.1", port=port)

data/latents/scGPT/scGPT_atac.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fcf709485bf9250cf6c448474187f8f565fd813c568802f4265ffb34d9fe453f
+size 61607066

data/latents/scGPT/scGPT_rna.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fcf709485bf9250cf6c448474187f8f565fd813c568802f4265ffb34d9fe453f
+size 61607066

data/output/data/batch.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc7cfc5042e7edb1e308222d6f782f30d9aacb87fb554eb90962791300cc177f
+size 2151

data/output/data/biomarker.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de188bb4cb0af2fdee272d0a9c2bd726362cc37d7bb989660c6d764474964385
+size 8246

data/output/data/integration_accuracy.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ad3a43c2e040c02eafe97bb2d7dfcb36e611cb603f66c53bc823b507161476c
+size 4664

data/output/output_metrics.yaml

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+adjusted_rand_index: 0.1653
+avg_silhouette_width: 0.4242
+avg_silhouette_width_batch: 0.9889
+foscttm: 0.0
+graph_connectivity: 0.2809
+mean_average_precision: 0.4612
+neighbor_conservation: 0.0431
+normalized_mutual_info: 0.3243
+seurat_alignment_score: 1.0

data/output/raw_combine_traj.h5ad

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e77aed4ced6a3cb4e86cb0a1cd44611fb5c55501003743cc9bd20eacca14580
+size 11801924

data/shell/run_integration.sh

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+method=scGPT
+echo '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
+echo '+++++         Integration_accuracy real: '$method'         +++++'
+echo '++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
+
+for data_name in 10x-Multiome-Pbmc10k
+do
+  echo "Evaluation with $data_name"
+  python integration_accuracy.py \
+      -d ../../data/download/$data_name/$data_name-small-RNA_uni.h5ad ../../data/download/$data_name/$data_name-small-ATAC_uni.h5ad \
+      -l ../../results/$method-output/$data_name/$data_name-small-rna.csv ../../results/$method-output/$data_name/$data_name-small-atac.csv \
+      -o ../../results/$method-output/$data_name/$data_name-cell_integration_info.yaml 
+  echo "=================="
+done

requirements.text

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+fastapi>=0.68.0
+uvicorn>=0.15.0
+gradio>=3.0
+aiofiles>=0.7.0
+python-multipart>=0.0.5  # 文件上传需要
+pandas>=2.2.3

service.py

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+import os
+import pandas as pd
+
+basePath = "./data/output/data/"
+uploadPath = "./data/latents/"
+
+# def toJson(data):
+#     return {
+#         "success": True,
+#         "data": data
+#     }
+def getALlData():
+    files = os.listdir(basePath)
+
+    merged_df = None
+    for each in files:
+        df = pd.read_csv(os.path.join(basePath, each))
+
+        if merged_df is None: merged_df = df
+        else: merged_df = pd.concat([merged_df, df], ignore_index=True)
+    
+    grouped_df = merged_df.groupby(['methods', 'datasets']).mean().reset_index()
+    grouped_df = grouped_df.fillna(0)
+    for col in grouped_df.select_dtypes(include=['float']).columns:
+        grouped_df[col] = grouped_df[col].round(4)
+
+    data =  grouped_df.to_dict(orient='records')
+    return data
+
+def getList(datatype):
+    if datatype == "Integration Accuracy":
+        file = "integration_accuracy.csv"
+    elif datatype == "Batch Correction":
+        file = "batch.csv"
+    elif datatype == "Bio Conservation":
+        file = "biomarker.csv"
+    
+
+    path = os.path.join(basePath, file)
+    df = pd.read_csv(path)
+    df["object_type"] = datatype
+    data =  df.to_dict(orient='records')
+    return data
+
+def getListByName(file, name):
+    path = os.path.join(basePath, file)
+    df = pd.read_csv(path)
+    filtered_records = df[df['methods'] == name]
+    data =  filtered_records.to_dict(orient='records')
+    return data
+
+def uploadFile(uploadFiles):
+    for file in uploadFiles:
+        save_path = os.path.join(uploadPath, file.filename)
+        file.save(save_path)
+    

static/Image/cuhk.pngZone.Identifier

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+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet

static/assets/index-C9Enc7k_.css

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static/index.html

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+<!DOCTYPE html>
+<html lang="">
+  <head>
+    <meta charset="UTF-8">
+    <link rel="icon" href="/favicon.ico">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Vite App</title>
+    <script type="module" crossorigin src="/assets/index-FSNbkXt5.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-C9Enc7k_.css">
+  </head>
+  <body>
+    <div id="app"></div>
+  </body>
+</html>