Create app.py

app.py

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+from huggingface_hub import InferenceClient
+import gradio as gr
+
+# Initialize the inference client with the Mixtral model
+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}"
+    
+    # Call the Mixtral model for translation
+    response = client(text_generation=prompt, parameters={"max_new_tokens": 100}, options={"wait_for_model": True})
+    
+    # The Mixtral model response includes the translated text in its output
+    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()
+    
+    return translated_text
+
+# Define the languages you want to support in your app
+languages = [
+    "French",
+    "Spanish",
+    "German",
+    "Italian",
+    "Portuguese",
+    # Add more languages as needed
+]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=[
+        gr.Textbox(label="Text to Translate", placeholder="Enter text here..."),
+        gr.Dropdown(label="Target Language", choices=languages)
+    ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="Simple Translator with Mixtral",
+    description="Translate text to various languages using the Mixtral model from Hugging Face."
+)
+
+# Launch the interface
+iface.launch()