Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = "t5-small"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to translate text
+def translate_text(text, source_lang, target_lang):
+    input_text = f"translate {source_lang} to {target_lang}: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(**inputs)
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translation
+
+# List of Indian languages
+indian_languages = [
+    "as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te", "ur"
+]
+
+# Supported languages
+languages = ["en", "fr", "de", "es", "it"] + indian_languages
+
+# Create Gradio interface
+def translate_interface(text, source_lang, target_lang):
+    return translate_text(text, source_lang, target_lang)
+
+iface = gr.Interface(
+    fn=translate_interface,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter text to translate"),
+        gr.Dropdown(choices=languages, label="Source Language"),
+        gr.Dropdown(choices=languages, label="Target Language")
+    ],
+    outputs="text",
+    title="Hugging Face Translation App",
+    description="Translate text from one language to another using a T5 model."
+)
+
+if __name__ == "__main__":
+    iface.launch()