Update app.py

app.py

@@ -5,34 +5,29 @@ import gradio as gr
 from ultralytics import YOLO
 from PIL import Image
 
-# 预加载两个模型
-model_doc = YOLO("moldet_yolo11l_960_doc.pt")        # PDF 专用
-model_img = YOLO("moldet_yolo11l_640_general.pt")    # 图片通用
+# preload detection models
+model_doc = YOLO("moldet_yolo11l_960_doc.pt")        # for PDF
+model_img = YOLO("moldet_yolo11l_640_general.pt")    # general
 
 def process_file(file):
-    # 创建临时目录
     with tempfile.TemporaryDirectory() as tmpdir:
         input_path = os.path.join(tmpdir, "input.png")
 
-        # 判断文件类型
+        # pdf or image
         if file.name.lower().endswith(".pdf"):
-            # PDF -> 转第一页为图片
             pdf = fitz.open(file.name)
             page = pdf[0]
-            pix = page.get_pixmap(dpi=300)  # 高分辨率渲染
+            pix = page.get_pixmap(dpi=300)
             pix.save(input_path)
 
-            # 运行 PDF 模型
             results = model_doc.predict(input_path, save=True, imgsz=960, conf=0.5)
 
         else:
-            # 图片直接保存
             Image.open(file.name).save(input_path)
 
-            # 运行图片模型
             results = model_img.predict(input_path, save=True, imgsz=640, conf=0.5)
 
-        # YOLO 会保存预测结果到 runs/predict/xxx 目录
+        # results is a list, our results is in results[0] 
         # save_dir = results[0].save_dir
         # pred_path = os.path.join(save_dir, os.path.basename(input_path))
         result_img = results[0].plot()
@@ -45,13 +40,12 @@ def process_file(file):
 # Gradio UI
 demo = gr.Interface(
     fn=process_file,
-    inputs=gr.File(label="上传图片或 PDF（单页）", file_types=[".png", ".jpg", ".jpeg", ".pdf"]),
-    # outputs=gr.Image(type="filepath", label="检测结果"),
-    outputs=gr.Image(type="pil", label="检测结果"),
-    title="PDF / 图片目标检测",
+    inputs=gr.File(label="Upload an image or a single PDF file", file_types=[".png", ".jpg", ".jpeg", ".pdf"]),
+    outputs=gr.Image(type="pil", label="Detection results"),
+    title="Molecule detection",
     description=(
-        "上传一张图片或单页 PDF，系统会自动选择对应的 YOLO 模型进行检测并输出结果。\n"
-        "PDF 使用 moldet_yolo11l_960_doc.pt，图片使用 moldet_yolo11l_640_general.pt"
+            "Upload an image or a single PDF file to detect molecules using a YOLO-based deep learning model.\n
+            The system processes the input and returns an annotated image with bounding boxes and labels around the detected molecular structures."
     )
 )