Update app.py

app.py

@@ -5,22 +5,25 @@ import gradio as gr
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
 def translate_text(text, target_language):
-    # Format the prompt to include the translation instruction
-    prompt = f"Translate the following text to {target_language}:\n{text}"
+    # Correctly format the prompt for the translation task
+    prompt = f"Translate the following text to {target_language}: {text}"
     
-    # Call the Mixtral model for translation
-    response = client(text_generation=prompt, parameters={"max_new_tokens": 100}, options={"wait_for_model": True})
+    # Correctly perform the model call for text generation
+    response = client.text_generation(
+        inputs=prompt,
+        parameters={"max_new_tokens": 100},
+        options={"wait_for_model": True}
+    )
     
-    # The Mixtral model response includes the translated text in its output
+    # Extract the translated text from the response
     translated_text = response[0]['generated_text']
     
-    # Clean up the response to extract only the translated part
-    # This step might need adjustment based on the model's output format
-    translated_text = translated_text.replace(prompt, '').strip()
+    # Depending on the model's response, you may need to clean the output
+    # For example, remove the input part from the response if necessary
+    translated_text = translated_text.split(prompt)[-1].strip()
     
     return translated_text
 
-# Define the languages you want to support in your app
 languages = [
     "French",
     "Spanish",
@@ -30,7 +33,6 @@ languages = [
     # Add more languages as needed
 ]
 
-# Create the Gradio interface
 iface = gr.Interface(
     fn=translate_text,
     inputs=[
@@ -42,5 +44,4 @@ iface = gr.Interface(
     description="Translate text to various languages using the Mixtral model from Hugging Face."
 )
 
-# Launch the interface
-iface.launch()
+iface.launch()