Create app.py

app.py

@@ -0,0 +1,82 @@
+import gradio as gr
+from urllib.parse import urlparse
+import requests
+import time
+import os
+
+from utils.gradio_helpers import parse_outputs, process_outputs
+
+inputs = []
+inputs.append(gr.Image(
+    label="Target Image", type="filepath"
+))
+
+inputs.append(gr.Image(
+    label="Swap Image", type="filepath"
+))
+
+names = ['target_image', 'swap_image']
+
+outputs = []
+outputs.append(gr.Image())
+
+expected_outputs = len(outputs)
+def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
+    headers = {'Content-Type': 'application/json'}
+
+    payload = {"input": {}}
+    
+    
+    base_url = "http://0.0.0.0:7860"
+    for i, key in enumerate(names):
+        value = args[i]
+        if value and (os.path.exists(str(value))):
+            value = f"{base_url}/file=" + value
+        if value is not None and value != "":
+            payload["input"][key] = value
+
+    response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
+
+    
+    if response.status_code == 201:
+        follow_up_url = response.json()["urls"]["get"]
+        response = requests.get(follow_up_url, headers=headers)
+        while response.json()["status"] != "succeeded":
+            if response.json()["status"] == "failed":
+                raise gr.Error("The submission failed!")
+            response = requests.get(follow_up_url, headers=headers)
+            time.sleep(1)
+    if response.status_code == 200:
+        json_response = response.json()
+        #If the output component is JSON return the entire output response 
+        if(outputs[0].get_config()["name"] == "json"):
+            return json_response["output"]
+        predict_outputs = parse_outputs(json_response["output"])
+        processed_outputs = process_outputs(predict_outputs)
+        difference_outputs = expected_outputs - len(processed_outputs)
+        # If less outputs than expected, hide the extra ones
+        if difference_outputs > 0:
+            extra_outputs = [gr.update(visible=False)] * difference_outputs
+            processed_outputs.extend(extra_outputs)
+        # If more outputs than expected, cap the outputs to the expected number
+        elif difference_outputs < 0:
+            processed_outputs = processed_outputs[:difference_outputs]
+        
+        return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]
+    else:
+        if(response.status_code == 409):
+            raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")
+        raise gr.Error(f"The submission failed! Error: {response.status_code}")
+
+title = "Demo for face-swap cog image by omniedgeio"
+model_description = "Face Swap"
+
+app = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=model_description,
+    allow_flagging="never",
+)
+app.launch(share=True)