Update app.py

app.py

@@ -1,48 +1,22 @@
+from gradio_client import Client
 import gradio as gr
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
-import time
-import os
 
-HF_TOKEN = os.environ['HF_TOKEN']
-headers = {"Authorization": "Bearer "+ HF_TOKEN}
+client = Client("https://15fdee77fc28978c9b.gradio.live/")
 
-# Define the model and tokenizer loading
-def load_model_and_tokenizer():
-    try:
-        tokenizer = AutoTokenizer.from_pretrained("atlasia/Terjman-Large")
-        model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Large")
-        translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
-        return translation_pipeline
-    except Exception as e:
-        print(f"Error loading model and tokenizer: {e}")
-        return None
-
-# Load the model and tokenizer once at startup
-model_pipeline = load_model_and_tokenizer()
-
-# Define the response function
 def respond(english_text):
-    if model_pipeline is None:
-        return "Model failed to load, please refresh the webpage."
-    
-    while True:
-        try:
-            result = model_pipeline(english_text)
-            return result[0]['translation_text']
-        except Exception as e:
-            if "estimated_time" in str(e):
-                time.sleep(5)  # Wait for 5 seconds before retrying
-            else:
-                return f"An error occurred: {e}"
+    darija_translated_text = client.predict(
+    		text=english_text,
+    		api_name="/predict"
+    )
+    return darija_translated_text
 
 # Create the Gradio interface
-app = gr.Interface(
-    fn=respond,
-    inputs="text",
-    outputs="text",
-    title="Terjman-Large 👨‍💻🥰",
-    description="Translate English text to Moroccan Darija using our Large model (240M) 🤗"
-)
+app = gr.Interface(fn=respond, 
+                   inputs="text", 
+                   outputs="text", 
+                   title="Terjman-Supreme 👨‍💻🤯", 
+                   description="Translate English text to Moroccan Darija using our top and biggest model (3.3B) 🤗")
+
 
 if __name__ == "__main__":
     app.launch()