Update app.py

app.py

@@ -1,64 +1,53 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from transformers import pipeline
+
+# Create a translation pipeline
+model_name = "facebook/nllb-200-1.3B"
+translator = pipeline("translation", model=model_name)
+
+# Define available languages including Kinyarwanda
+languages = {
+    "English": "eng_Latn",
+    "French": "fra_Latn",
+    "Spanish": "spa_Latn",
+    "German": "deu_Latn",
+    "Chinese": "zho_Hans",
+    "Arabic": "ara_Arab",
+    "Russian": "rus_Cyrl",
+    "Hindi": "hin_Deva",
+    "Japanese": "jpn_Jpan",
+    "Kinyarwanda": "kin_Latn"
+}
+
+def translate(text, source_lang, target_lang):
+    if not text:
+        return ""
+    
+    source_code = languages.get(source_lang)
+    target_code = languages.get(target_lang)
+    
+    # NLLB requires specific format for translation
+    translation = translator(
+        text, 
+        src_lang=source_code, 
+        tgt_lang=target_code,
+        max_length=400
+    )
+    
+    return translation[0]["translation_text"]
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text", lines=5),
+        gr.Dropdown(list(languages.keys()), label="Source Language", value="English"),
+        gr.Dropdown(list(languages.keys()), label="Target Language", value="French")
     ],
+    outputs=gr.Textbox(label="Translated Text", lines=5),
+    title="NLLB-200 Multilingual Translation",
+    description="Translate text between multiple languages using Facebook's NLLB-200 model."
 )
 
-
 if __name__ == "__main__":
     demo.launch()