[New] Add predict code

app.py

@@ -1,7 +1,67 @@
 import gradio as gr
+import yolov7
+from yolov7.models.common import autoShape
+from yolov7.models.experimental import attempt_load
+from yolov7.utils.google_utils import attempt_download_from_hub, attempt_download
+from yolov7.utils.torch_utils import TracedModel
+YOLO_MODEL_FILE_NAME="kadirnar/yolov7-v0.1"
 
-def greet(name):
-    return "Hello " + name + "!!"
+def load_local_model(model_file, autoshape=True, device='cpu', trace=False, size=640, half=False, hf_model=False):
+    """
+    Creates a specified YOLOv7 model
+    Arguments:
+        model_path (str): path of the model
+        device (str): select device that model will be loaded (cpu, cuda)
+        trace (bool): if True, model will be traced
+        size (int): size of the input image
+        half (bool): if True, model will be in half precision
+        hf_model (bool): if True, model will be loaded from huggingface hub    
+    Returns:
+        pytorch model
+    (Adapted from yolov7.hubconf.create)
+    """
+    
+    model = attempt_load(model_file, map_location=device)
+    if trace:
+        model = TracedModel(model, device, size)
+    if autoshape:
+        model = autoShape(model)
+    if half:
+        model.half()
+    return model
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# YOLO_MODEL_FILE_NAME="kadirnar/yolov7-tiny-v0.1"
+def yolov7_inference(
+    image: gr.inputs.Image = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45,        
+):
+    model = yolov7.load_model(YOLO_MODEL_FILE_NAME, device="cpu", hf_model=False, trace=False)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model([image], size=image_size)
+    return results.render()[0]
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "Yolov7: evaluation yolov7.pt"
+
+examples = [['car.jpeg', 640, 0.5, 0.75], 
+            ['horse.jpeg', 640, 0.5, 0.75]]
+demo_app = gr.Interface(
+    fn=yolov7_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

@@ -0,0 +1,2 @@
+torch
+yolov7detect