refactor: code and upgrade gradio

app.py

@@ -1,11 +1,23 @@
 import gradio as gr
-from functions import *
+import onnxruntime as rt
+from functions import run_inference
+
+model_path = 'weights/model.onnx'
+session = rt.InferenceSession(model_path)
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
 
 title = "Water Body Segmentation - Image Segmentation PyTorch"
 examples = ['examples/image1.png', 'examples/image2.png', 'examples/image3.png', 'examples/image4.png', 'examples/image5.png']
 
-interface = gr.Interface(fn=predict, inputs=gr.Image(type= 'numpy').style(height= 256),
-            outputs= gr.Image(type = "numpy").style(height= 256),
-            examples= examples, title= title, css= '.gr-box {background-color: rgb(230 230 230);}')
+def inference_wrapper(image):
+    return run_inference(image, session, input_name, output_name)
+
+
+interface = gr.Interface(fn=inference_wrapper, 
+            inputs=gr.Image(type='numpy', height=400, width=400),
+            outputs=gr.Image(type="numpy", height=400, width=400),
+            examples=examples, 
+            title=title)
 
 interface.launch()

functions.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-import onnxruntime as rt
 
 def resize_preserve_aspect_ratio(image, size):
     h, w = image.shape[:2]
@@ -10,9 +9,7 @@ def resize_preserve_aspect_ratio(image, size):
         image = cv2.resize(image, (size, size * h // w))
     return image
 
-def predict(inp_image):
-
-    model_path = 'weights/model.onnx'
+def run_inference(inp_image, session, input_name, output_name):
     inp_dim = inp_image.shape[:2]
 
     image = cv2.resize(inp_image, (256, 256))
@@ -20,10 +17,6 @@ def predict(inp_image):
     image = np.transpose(image, (2, 0, 1))
     image = np.expand_dims(image, axis=0)
 
-    session = rt.InferenceSession(model_path)
-    input_name = session.get_inputs()[0].name
-    output_name = session.get_outputs()[0].name
-
     pred_onx = session.run([output_name], {input_name: image.astype(np.float32)})[0]
     pred_onx = pred_onx > 0.5
     pred_onx = pred_onx * 255
@@ -32,5 +25,5 @@ def predict(inp_image):
     pred_onx = np.expand_dims(pred_onx, axis=2)
     pred_onx = np.concatenate((pred_onx, pred_onx, pred_onx), axis=2)
 
-    output = resize_preserve_aspect_ratio(pred_onx, 256)
+    output = resize_preserve_aspect_ratio(pred_onx, 400)
     return output

requirements.txt

@@ -1,5 +1,8 @@
+aiofiles==23.2.1
 aiohttp==3.8.3
 aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.7.0
 anyio==3.6.2
 async-timeout==4.0.2
 attrs==22.1.0
@@ -12,22 +15,29 @@ coloredlogs==15.0.1
 contourpy==1.0.6
 cryptography==38.0.3
 cycler==0.11.0
-fastapi==0.87.0
+fastapi==0.115.12
 ffmpy==0.3.0
+filelock==3.18.0
 flatbuffers==22.10.26
 fonttools==4.38.0
 frozenlist==1.3.3
-fsspec==2022.11.0
-gradio==3.50.2
-h11==0.12.0
-httpcore==0.15.0
-httpx==0.23.0
+fsspec==2025.5.1
+gradio==5.32.0
+gradio_client==1.10.2
+groovy==0.1.2
+h11==0.16.0
+hf-xet==1.1.2
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.32.3
 humanfriendly==10.0
 idna==3.4
+importlib_resources==6.5.2
 Jinja2==3.1.2
+jsonschema==4.17.3
 kiwisolver==1.4.4
 linkify-it-py==1.0.3
-markdown-it-py==2.1.0
+markdown-it-py==3.0.0
 MarkupSafe==2.1.1
 matplotlib==3.6.2
 mdit-py-plugins==0.3.1
@@ -45,21 +55,34 @@ Pillow==9.3.0
 protobuf==4.21.9
 pycparser==2.21
 pycryptodome==3.15.0
-pydantic==1.10.2
+pydantic==2.11.5
+pydantic_core==2.33.2
 pydub==0.25.1
+Pygments==2.19.1
 PyNaCl==1.5.0
 pyparsing==3.0.9
+pyrsistent==0.20.0
 python-dateutil==2.8.2
-python-multipart==0.0.5
+python-multipart==0.0.20
 pytz==2022.6
 PyYAML==6.0
 requests==2.28.1
 rfc3986==1.5.0
+rich==14.0.0
+ruff==0.11.12
+safehttpx==0.1.6
+semantic-version==2.10.0
+shellingham==1.5.4
 six==1.16.0
 sniffio==1.3.0
-starlette==0.21.0
+starlette==0.46.2
 sympy==1.11.1
-typing_extensions==4.4.0
+tomlkit==0.13.2
+toolz==1.0.0
+tqdm==4.67.1
+typer==0.16.0
+typing-inspection==0.4.1
+typing_extensions==4.13.2
 uc-micro-py==1.0.1
 urllib3==1.26.12
 uvicorn==0.19.0