Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import spaces
+import torch
+
+model_name = "ai4bharat/indictrans2-en-indic-1B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+LANG_MAP = {
+    "Hindi": "hin_Deva",
+    "Bengali": "ben_Beng",
+    "Marathi": "mar_Deva",
+    "Telugu": "tel_Telu",
+    "Tamil": "tam_Taml",
+    "Gujarati": "guj_Gujr",
+    "Kannada": "kan_Knda",
+    "Malayalam": "mal_Mlym",
+    "Punjabi": "pan_Guru",
+    "Odia": "ory_Orya",
+}
+
+@spaces.GPU
+def translate(target_lang, text):
+    lang_code = LANG_MAP[target_lang]
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        padding=True,
+        truncation=True
+    ).to(device)
+
+    output = model.generate(
+        **inputs,
+        forced_bos_token_id=tokenizer.convert_tokens_to_ids(lang_code),
+        max_length=200
+    )
+
+    return tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Dropdown(list(LANG_MAP.keys()), label="Select Target Language", value="Hindi"),
+        gr.Textbox(label="Enter Text in English")
+    ],
+    outputs=gr.Textbox(label="Translated Output"),
+    title="BASHA BRIDGE 🌍",
+    description="AI Powered Multilingual Translation System"
+)
+
+iface.launch()