Update app.py

app.py

@@ -1,120 +1,13 @@
 #!/usr/bin/env python
 
-import functools
-import os
-import pathlib
-
-import cv2
-import dlib
 import gradio as gr
-import huggingface_hub
-import numpy as np
-import pretrainedmodels
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-DESCRIPTION = "# [Age Estimation](https://github.com/yu4u/age-estimation-pytorch)"
-
-
-def get_model(model_name="se_resnext50_32x4d", num_classes=101, pretrained="imagenet"):
-    model = pretrainedmodels.__dict__[model_name](pretrained=pretrained)
-    dim_feats = model.last_linear.in_features
-    model.last_linear = nn.Linear(dim_feats, num_classes)
-    model.avg_pool = nn.AdaptiveAvgPool2d(1)
-    return model
-
-
-def load_model(device):
-    model = get_model(model_name="se_resnext50_32x4d", pretrained=None)
-    path = huggingface_hub.hf_hub_download("public-data/yu4u-age-estimation-pytorch", "pretrained.pth")
-    model.load_state_dict(torch.load(path))
-    model = model.to(device)
-    model.eval()
-    return model
-
-
-def load_image(path):
-    image = cv2.imread(path)
-    h_orig, w_orig = image.shape[:2]
-    size = max(h_orig, w_orig)
-    scale = 640 / size
-    w, h = int(w_orig * scale), int(h_orig * scale)
-    image = cv2.resize(image, (w, h))
-    return image
-
-
-def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=0.8, thickness=1):
-    size = cv2.getTextSize(label, font, font_scale, thickness)[0]
-    x, y = point
-    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
-    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)
-
-
-@torch.inference_mode()
-def predict(image, model, face_detector, device, margin=0.4, input_size=224):
-    image = cv2.imread(image, cv2.IMREAD_COLOR)[:, :, ::-1].copy()
-    image_h, image_w = image.shape[:2]
-
-    # detect faces using dlib detector
-    detected = face_detector(image, 1)
-    faces = np.empty((len(detected), input_size, input_size, 3))
-
-    if len(detected) > 0:
-        for i, d in enumerate(detected):
-            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
-            xw1 = max(int(x1 - margin * w), 0)
-            yw1 = max(int(y1 - margin * h), 0)
-            xw2 = min(int(x2 + margin * w), image_w - 1)
-            yw2 = min(int(y2 + margin * h), image_h - 1)
-            faces[i] = cv2.resize(image[yw1 : yw2 + 1, xw1 : xw2 + 1], (input_size, input_size))
-
-            cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 255), 2)
-            cv2.rectangle(image, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
-
-        # predict ages
-        inputs = torch.from_numpy(np.transpose(faces.astype(np.float32), (0, 3, 1, 2))).to(device)
-        outputs = F.softmax(model(inputs), dim=-1).cpu().numpy()
-        ages = np.arange(0, 101)
-        predicted_ages = (outputs * ages).sum(axis=-1)
-
-        # draw results
-        for age, d in zip(predicted_ages, detected):
-            draw_label(image, (d.left(), d.top()), f"{int(age)}")
-    return image, str(predicted_ages)
-
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model = load_model(device)
-face_detector = dlib.get_frontal_face_detector()
-fn = functools.partial(predict, model=model, face_detector=face_detector, device=device)
-
-image_dir = pathlib.Path("sample_images")
-examples = [path.as_posix() for path in sorted(image_dir.glob("*.jpg"))]
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label="Input", type="filepath")
-            run_button = gr.Button("Run")
-        with gr.Column():
-            result = gr.Image(label="Result")
-            digits = gr.Textbox()
 
-    gr.Examples(
-        examples=examples,
-        inputs=image,
-        outputs=result,
-        fn=fn,
-        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
-    )
-    run_button.click(
-        fn=fn,
-        inputs=image,
-        outputs=result,
-        api_name="predict",
-    )
+def greet(text):
+  return text
+   
+with gr.Blocks() as demo:
+  input = gr.TextArea()
+  output = gr.TextArea()
+  gr.Button().click(fn=greet, inputs=input, outputs=output)
 
-if __name__ == "__main__":
-    demo.queue(max_size=15).launch()
+demo.launch(debug=True)