Create app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+import subprocess
+subprocess.check_call(["pip", "install", "transformers"])
+subprocess.check_call(["pip", "install", "torch"])
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("balaramas/mbart-sahitrans_new_data")
+model = AutoModelForSeq2SeqLM.from_pretrained("balaramas/mbart-sahitrans_new_data")
+
+
+def sanmt(txt):
+    
+    tokenizer.src_lang = "hi_IN"
+    encoded_ar = tokenizer(txt, return_tensors="pt")   
+    generated_tokens = model.generate(
+        **encoded_ar,
+        forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"]
+    )
+    output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]   
+    return output
+
+iface = gr.Interface(
+    fn=sanmt, 
+    inputs=gr.Textbox(label="Enter text in Sanskrit", placeholder="Type here..."), 
+    outputs=gr.Textbox(label="Translated Hindi Text"),
+    title="Sanskrit to Hindi Translator"
+)
+iface.launch()