Modify

app.py

@@ -3,36 +3,40 @@ import gradio as gr
 import models as MOD
 import process_data as PD
 
-model_master= {
-    "SSL-AASIST (Trained on ASV-Spoof5)": {"eer_threshold":3.3330237865448,
-                                           "data_process_func": "process_ssl_assist_input" ,
+model_master = {
+    "SSL-AASIST (Trained on ASV-Spoof5)": {"eer_threshold": 3.3330237865448,
+                                           "data_process_func": "process_ssl_assist_input",
                                            "note": "This model is trained on ASVSpoof 2024 training data.",
                                            "model_class": "Model",
-                                           "model_checkpoint":"ssl_aasist_epoch_7.pth"},
-    "AASIST": {"eer_threshold":1.8018419742584229,
-                                           "data_process_func": "process_assist_input" ,
-                                           "note": "This model is trained on ASVSpoof 2024 training data."}
+                                           "model_checkpoint": "ssl_aasist_epoch_7.pth"},
+    "AASIST": {"eer_threshold": 1.8018419742584229,
+               "data_process_func": "process_assist_input",
+               "note": "This model is trained on ASVSpoof 2024 training data."}
 }
 
-
-model = MOD.Model(None,"cpu")
-model.load_state_dict(torch.load("ssl_aasist_epoch_7.pth",map_location="cpu"))
+model = MOD.Model(None, "cpu")
+model.load_state_dict(torch.load("ssl_aasist_epoch_7.pth", map_location="cpu"))
 loaded_model = "SSL-AASIST (Trained on ASV-Spoof5)"
 print("model loaded")
 
+
 def process(file, type):
+    if type == "AASIST":
+        return "Model AASIST is not yet implemented."
     global model
     global loaded_model
     inp = getattr(PD, model_master[type]["data_process_func"])(file)
-    print(inp)
     if not loaded_model == type:
         model = getattr(MOD, model_master[type]["model_class"])(None, "cpu")
         model.load_state_dict(torch.load(model_master[type]["model_checkpoint"], map_location="cpu"))
         loaded_model = type
 
-    op = model(inp).detach().squeeze()
-    print("processed")
-    return str(file)+str(type)+str(model_master[type]["eer_threshold"]) + str(op)
+    op = model(inp).detach().squeeze()[1].item()
+    
+    response_text = "Decision score: {} \n Decision threshold: {} \n Note: 1. Any score below threshold is considered fake. \n 2. {} ".format(
+        str(op), str(model_master[type]["eer_threshold"]), model_master[type]["note"])
+    return response_text
+
 
 demo = gr.Blocks()
 file_proc = gr.Interface(
@@ -40,7 +44,7 @@ file_proc = gr.Interface(
     inputs=[
         gr.Audio(sources=["upload"], label="Audio file", type="filepath"),
         gr.Radio(["SSL-AASIST (Trained on ASV-Spoof5)", "AASIST"], label="Select Model", type="value"),
-        ],
+    ],
     outputs="text",
     title="Find the Fake: Analyze 'Real' or 'Fake'.",
     description=(
@@ -53,4 +57,4 @@ file_proc = gr.Interface(
 with demo:
     gr.TabbedInterface([file_proc], ["Analyze Audio File"])
 demo.queue(max_size=10)
-demo.launch(share=True)
+demo.launch(share=True)