Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+try:
+    model_name = "ai4bharat/IndicTrans3-beta"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
+    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+except Exception as e:
+    raise Exception(f"Error loading model: {str(e)}")
+
+def translate(text, src_lang, tgt_lang):
+    if not text:
+        return "Please enter text to translate."
+    try:
+        prompt = f"Translate from {src_lang} to {tgt_lang}: {text}"
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True)
+        outputs = model.generate(**inputs, max_new_tokens=50)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    except Exception as e:
+        return f"Translation error: {str(e)}"
+
+lang_options = ["en", "hi", "ta", "te"]
+
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text", placeholder="Enter text here..."),
+        gr.Dropdown(lang_options, label="Source Language", value="en"),
+        gr.Dropdown(lang_options, label="Target Language", value="hi")
+    ],
+    outputs=gr.Textbox(label="Translation"),
+    title="Indic Translator",
+    description="Translate text using AI4Bharat's IndicTrans3-beta model."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)