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Create app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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# --- CONFIGURATION ---
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MODEL_K2H = "ankitklakra/kurukh-to-hindi"
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MODEL_H2K = "ankitklakra/hindi-to-kurukh"
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# --- LOAD MODELS ---
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print("Loading Kurukh -> Hindi Model...")
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pipe_k2h = pipeline("text2text-generation", model=MODEL_K2H)
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print("Loading Hindi -> Kurukh Model...")
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pipe_h2k = pipeline("text2text-generation", model=MODEL_H2K)
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# --- TRANSLATION FUNCTION ---
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def translate_text(text, direction):
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if not text:
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return ""
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# Select the correct brain based on user choice
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if direction == "Kurukh -> Hindi":
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target_pipeline = pipe_k2h
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else:
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target_pipeline = pipe_h2k
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# Translate
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# max_length=128 allow for longer sentences
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results = target_pipeline(text, max_length=128)
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return results[0]['generated_text']
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# --- THE USER INTERFACE ---
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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# Header
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gr.Markdown(
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"""
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# 🇮🇳 AI Kurukh (Kurux) Translator
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### Preserving Tribal Languages with Artificial Intelligence
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*Powered by Custom Fine-Tuned Google mT5 Models*
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"""
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)
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# Input Section
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with gr.Row():
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direction = gr.Radio(
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["Kurukh -> Hindi", "Hindi -> Kurukh"],
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label="Translation Mode",
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value="Kurukh -> Hindi"
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(
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label="Input Text",
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placeholder="Type your sentence here...",
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lines=5
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)
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translate_btn = gr.Button("Translate 🚀", variant="primary")
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with gr.Column():
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output_text = gr.Textbox(
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label="Translation Result",
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lines=5,
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show_copy_button=True # Allows users to copy result
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)
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# Examples to help new users
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gr.Examples(
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examples=[
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["निघै नामे इन्द्रा हिकै?", "Kurukh -> Hindi"],
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["इन्गे अम्मो चि'आ।", "Kurukh -> Hindi"],
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["तुम्हारा नाम क्या है?", "Hindi -> Kurukh"],
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["मुझे पानी दो।", "Hindi -> Kurukh"]
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],
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inputs=[input_text, direction],
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label="Click on an example to test:"
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)
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# Logic Connection
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translate_btn.click(fn=translate_text, inputs=[input_text, direction], outputs=output_text)
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# Launch
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demo.launch()
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