Update app.py

app.py

@@ -1,64 +1,40 @@
 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 MarianMTModel, MarianTokenizer
+
+# Load models for both directions
+en_to_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
+ur_to_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
+
+en_to_ur_tokenizer = MarianTokenizer.from_pretrained(en_to_ur_model_name)
+en_to_ur_model = MarianMTModel.from_pretrained(en_to_ur_model_name)
+
+ur_to_en_tokenizer = MarianTokenizer.from_pretrained(ur_to_en_model_name)
+ur_to_en_model = MarianMTModel.from_pretrained(ur_to_en_model_name)
+
+# Translation function
+def translate(text, direction):
+    if direction == "English to Urdu":
+        tokenizer = en_to_ur_tokenizer
+        model = en_to_ur_model
+    else:
+        tokenizer = ur_to_en_tokenizer
+        model = ur_to_en_model
+
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    translated = model.generate(**inputs)
+    output = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return output
+
+# Gradio interface
+demo = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Enter Text"),
+        gr.Radio(["English to Urdu", "Urdu to English"], label="Translation Direction")
     ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="English ↔ Urdu Translator",
+    description="Translate between English and Urdu using Hugging Face Transformers."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()