update some gradio API calls

app.py

@@ -5,7 +5,7 @@ import json
 import re
 import random
 import numpy as np
-from gradio_client import Client
+from gradio_client import Client, handle_file
 hf_token = os.environ.get("HF_TOKEN")
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -13,7 +13,7 @@ MAX_SEED = np.iinfo(np.int32).max
 def check_api(model_name):
     if model_name == "MAGNet":
         try :
-            client = Client("https://fffiloni-magnet.hf.space/")
+            client = Client("fffiloni/MAGNet")
             return "api ready"
         except : 
             return "api not ready yet"
@@ -25,7 +25,7 @@ def check_api(model_name):
             return "api not ready yet"
     elif model_name == "Riffusion":
         try :
-            client = Client("https://fffiloni-spectrogram-to-music.hf.space/")
+            client = Client("fffiloni/spectrogram-to-music")
             return "api ready"
         except : 
             return "api not ready yet"
@@ -69,13 +69,12 @@ def extract_audio(video_in):
 
 
 def get_caption(image_in):
-    kosmos2_client = Client("https://ydshieh-kosmos-2.hf.space/")
+    kosmos2_client = Client("fffiloni/Kosmos-2-API", hf_token=hf_token)
     kosmos2_result = kosmos2_client.predict(
-        image_in,	# str (filepath or URL to image) in 'Test Image' Image component
-        "Detailed",	# str in 'Description Type' Radio component
-        fn_index=4
+		image_input=handle_file(image_in),
+		text_input="Detailed",
+		api_name="/generate_predictions"
     )
-
     print(f"KOSMOS2 RETURNS: {kosmos2_result}")
 
     with open(kosmos2_result[1], 'r') as f:
@@ -121,20 +120,20 @@ def get_caption_from_MD(image_in):
 
 def get_magnet(prompt):
 
-    client = Client("https://fffiloni-magnet.hf.space/")
+    client = Client("fffiloni/MAGNet")
     result = client.predict(
-        "facebook/magnet-small-10secs",	# Literal['facebook/magnet-small-10secs', 'facebook/magnet-medium-10secs', 'facebook/magnet-small-30secs', 'facebook/magnet-medium-30secs', 'facebook/audio-magnet-small', 'facebook/audio-magnet-medium']  in 'Model' Radio component
-        "",	# str  in 'Model Path (custom models)' Textbox component
-        prompt,	# str  in 'Input Text' Textbox component
-        3,	# float  in 'Temperature' Number component
-        0.9,	# float  in 'Top-p' Number component
-        10,	# float  in 'Max CFG coefficient' Number component
-        1,	# float  in 'Min CFG coefficient' Number component
-        20,	# float  in 'Decoding Steps (stage 1)' Number component
-        10,	# float  in 'Decoding Steps (stage 2)' Number component
-        10,	# float  in 'Decoding Steps (stage 3)' Number component
-        10,	# float  in 'Decoding Steps (stage 4)' Number component
-        "prod-stride1 (new!)",	# Literal['max-nonoverlap', 'prod-stride1 (new!)']  in 'Span Scoring' Radio component
+        model="facebook/magnet-small-10secs",	# Literal['facebook/magnet-small-10secs', 'facebook/magnet-medium-10secs', 'facebook/magnet-small-30secs', 'facebook/magnet-medium-30secs', 'facebook/audio-magnet-small', 'facebook/audio-magnet-medium']  in 'Model' Radio component
+        model_path="",	# str  in 'Model Path (custom models)' Textbox component
+        text=prompt,	# str  in 'Input Text' Textbox component
+        temperature=3,	# float  in 'Temperature' Number component
+        topp=0.9,	# float  in 'Top-p' Number component
+        max_cfg_coef=10,	# float  in 'Max CFG coefficient' Number component
+        min_cfg_coef=1,	# float  in 'Min CFG coefficient' Number component
+        decoding_steps1=20,	# float  in 'Decoding Steps (stage 1)' Number component
+        decoding_steps2=10,	# float  in 'Decoding Steps (stage 2)' Number component
+        decoding_steps3=10,	# float  in 'Decoding Steps (stage 3)' Number component
+        decoding_steps4=10,	# float  in 'Decoding Steps (stage 4)' Number component
+        span_score="prod-stride1 (new!)",	# Literal['max-nonoverlap', 'prod-stride1 (new!)']  in 'Span Scoring' Radio component
         api_name="/predict_full"
     )
     print(result)
@@ -157,12 +156,12 @@ def get_audioldm(prompt):
     return audio_result
 
 def get_riffusion(prompt):
-    client = Client("https://fffiloni-spectrogram-to-music.hf.space/")
+    client = Client("fffiloni/spectrogram-to-music")
     result = client.predict(
-		prompt,	# str  in 'Musical prompt' Textbox component
-		"",	# str  in 'Negative prompt' Textbox component
-		None,	# filepath  in 'parameter_4' Audio component
-		10,	# float (numeric value between 5 and 10) in 'Duration in seconds' Slider component
+		prompt=prompt,	# str  in 'Musical prompt' Textbox component
+		negative_prompt="",	# str  in 'Negative prompt' Textbox component
+		audio_input=None,	# filepath  in 'parameter_4' Audio component
+		duration=10,	# float (numeric value between 5 and 10) in 'Duration in seconds' Slider component
 		api_name="/predict"
     )
     print(result)