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7f95fc6
1
Parent(s):
c0c2e82
Added English audio feature
Browse files- app.py +73 -50
- postBuild +7 -1
- requirements.txt +2 -1
app.py
CHANGED
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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from IndicTransToolkit.processor import IndicProcessor
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import gradio as gr
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import requests
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from datetime import datetime
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# Supabase configuration
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SUPABASE_URL = "https://gptmdbhzblfybdnohqnh.supabase.co"
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SUPABASE_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImdwdG1kYmh6YmxmeWJkbm9ocW5oIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDc0NjY1NDgsImV4cCI6MjA2MzA0MjU0OH0.CfWArts6Kd_x7Wj0a_nAyGJfrFt8F7Wdy_MdYDj9e7U"
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SUPABASE_TABLE = "translations"
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# Device configuration
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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# Load
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model_en_to_indic = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True).to(DEVICE)
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tokenizer_en_to_indic = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True)
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model_indic_to_en = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True).to(DEVICE)
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tokenizer_indic_to_en = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True)
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ip = IndicProcessor(inference=True)
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#
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def save_to_supabase(input_text, output_text, direction):
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if not input_text.strip() or not output_text.strip():
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return "Nothing to save."
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# Choose table name based on direction
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table_name = "translations" if direction == "en_to_ks" else "ks_to_en_translations"
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payload = {
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@@ -47,19 +51,12 @@ def save_to_supabase(input_text, output_text, direction):
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json=payload,
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timeout=10
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)
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if response.status_code == 201:
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return "β
Saved successfully!"
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else:
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print("SAVE ERROR:", response.status_code, response.text)
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return "β Failed to save."
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except Exception as e:
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print("SAVE EXCEPTION:", e)
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return "β Save request error."
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# Function to retrieve recent translation history from Supabase
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def get_translation_history(direction="en_to_ks"):
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table_name = "translations" if direction == "en_to_ks" else "ks_to_en_translations"
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@@ -74,19 +71,16 @@ def get_translation_history(direction="en_to_ks"):
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headers=headers,
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timeout=10
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)
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if response.status_code == 200:
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records = response.json()
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return "\n\n".join(
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[f"Input: {r['input_text']} β Output: {r['output_text']}" for r in records]
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)
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return "Failed to load history."
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except Exception as e:
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print("HISTORY FETCH ERROR:", e)
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return "Error loading history."
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# Translation function
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def translate(text, direction):
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if not text.strip():
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print("Translation Error:", e)
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return "β οΈ Translation failed.", gr.update(), gr.update()
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def switch_direction(direction, input_text_val, output_text_val):
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new_direction = "ks_to_en" if direction == "en_to_ks" else "en_to_ks"
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input_label = "Kashmiri Text" if new_direction == "ks_to_en" else "English Text"
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output_label = "English Translation" if new_direction == "ks_to_en" else "Kashmiri Translation"
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-
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# Swap input/output text too
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return (
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new_direction,
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gr.update(value=output_text_val, label=input_label),
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gr.update(value=input_text_val, label=output_label)
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)
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# Update your Gradio interface block
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with gr.Blocks() as interface:
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gr.HTML("""
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<div style="display: flex; justify-content: space-between; align-items: center; padding: 10px;">
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</div>
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""")
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translation_direction = gr.State(value="en_to_ks")
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with gr.Row():
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input_text = gr.Textbox(lines=2, label="English Text", placeholder="Enter text....")
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with gr.Row():
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translate_button = gr.Button("Translate")
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save_button = gr.Button("Save Translation")
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switch_button = gr.Button("Switch")
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save_status = gr.Textbox(label="Save Status", interactive=False)
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history_box = gr.Textbox(lines=10, label="Translation History", interactive=False)
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translate_button.click(
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fn=translate,
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inputs=[input_text, translation_direction],
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)
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save_button.click(
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).then(
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)
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switch_button.click(
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)
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if __name__ == "__main__":
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interface.launch(
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
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from IndicTransToolkit.processor import IndicProcessor
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import gradio as gr
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import requests
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from datetime import datetime
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import tempfile
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# Supabase configuration
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SUPABASE_URL = "https://gptmdbhzblfybdnohqnh.supabase.co"
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SUPABASE_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImdwdG1kYmh6YmxmeWJkbm9ocW5oIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDc0NjY1NDgsImV4cCI6MjA2MzA0MjU0OH0.CfWArts6Kd_x7Wj0a_nAyGJfrFt8F7Wdy_MdYDj9e7U"
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SUPABASE_TABLE = "translations"
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# Device configuration
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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# Load translation models
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model_en_to_indic = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True).to(DEVICE)
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tokenizer_en_to_indic = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True)
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model_indic_to_en = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True).to(DEVICE)
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tokenizer_indic_to_en = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True)
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ip = IndicProcessor(inference=True)
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# Whisper STT and English TTS pipelines
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asr = pipeline("automatic-speech-recognition", model="openai/whisper-small")
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tts_en = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
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# Save to Supabase
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def save_to_supabase(input_text, output_text, direction):
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if not input_text.strip() or not output_text.strip():
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return "Nothing to save."
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table_name = "translations" if direction == "en_to_ks" else "ks_to_en_translations"
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payload = {
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json=payload,
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timeout=10
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)
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return "β
Saved successfully!" if response.status_code == 201 else "β Failed to save."
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except Exception as e:
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print("SAVE EXCEPTION:", e)
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return "β Save request error."
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# Fetch translation history
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def get_translation_history(direction="en_to_ks"):
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table_name = "translations" if direction == "en_to_ks" else "ks_to_en_translations"
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headers=headers,
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timeout=10
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)
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if response.status_code == 200:
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records = response.json()
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return "\n\n".join(
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[f"Input: {r['input_text']} β Output: {r['output_text']}" for r in records]
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)
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return "Failed to load history."
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except Exception as e:
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print("HISTORY FETCH ERROR:", e)
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return "Error loading history."
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# Translation function
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def translate(text, direction):
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if not text.strip():
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print("Translation Error:", e)
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return "β οΈ Translation failed.", gr.update(), gr.update()
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# Transcribe English audio
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def transcribe_audio(audio_path):
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try:
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result = asr(audio_path)
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return result["text"]
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except Exception as e:
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print("STT Error:", e)
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return "β οΈ Transcription failed."
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# Synthesize English audio if direction is ks_to_en
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def synthesize_tts(text, direction):
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if direction == "ks_to_en":
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try:
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result = tts_en(text)
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return (result["sampling_rate"], result["audio"])
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except Exception as e:
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print("TTS Error:", e)
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return None
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# Direction switch
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def switch_direction(direction, input_text_val, output_text_val):
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new_direction = "ks_to_en" if direction == "en_to_ks" else "en_to_ks"
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input_label = "Kashmiri Text" if new_direction == "ks_to_en" else "English Text"
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output_label = "English Translation" if new_direction == "ks_to_en" else "Kashmiri Translation"
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return (
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new_direction,
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gr.update(value=output_text_val, label=input_label),
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gr.update(value=input_text_val, label=output_label)
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)
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# Gradio interface
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with gr.Blocks() as interface:
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gr.HTML("""
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<div style="display: flex; justify-content: space-between; align-items: center; padding: 10px;">
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<img src="https://raw.githubusercontent.com/BurhaanRasheedZargar/Images/211321a234613a9c3dd944fe9367cf13d1386239/assets/left_logo.png" style="height:150px; width:auto;">
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<h2 style="margin: 0; text-align: center;">English β Kashmiri Translator</h2>
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<img src="https://raw.githubusercontent.com/BurhaanRasheedZargar/Images/77797f7f7cbee328fa0f9d31cf3e290441e04cd3/assets/right_logo.png">
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</div>
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""")
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translation_direction = gr.State(value="en_to_ks")
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with gr.Row():
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input_text = gr.Textbox(lines=2, label="English Text", placeholder="Enter text....")
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with gr.Row():
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translate_button = gr.Button("Translate")
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save_button = gr.Button("Save Translation")
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switch_button = gr.Button("Switch")
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save_status = gr.Textbox(label="Save Status", interactive=False)
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history_box = gr.Textbox(lines=10, label="Translation History", interactive=False)
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with gr.Row():
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audio_input = gr.Audio(source="microphone", type="filepath", label="ποΈ Speak in English")
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audio_output = gr.Audio(label="π English Output Audio")
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stt_translate_button = gr.Button("π€ Transcribe & Translate")
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# Click events
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translate_button.click(
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fn=translate,
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inputs=[input_text, translation_direction],
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)
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save_button.click(
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fn=save_to_supabase,
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inputs=[input_text, output_text, translation_direction],
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outputs=save_status
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).then(
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fn=get_translation_history,
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inputs=translation_direction,
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outputs=history_box
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)
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switch_button.click(
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fn=switch_direction,
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inputs=[translation_direction, input_text, output_text],
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outputs=[translation_direction, input_text, output_text]
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)
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stt_translate_button.click(
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fn=transcribe_audio,
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inputs=audio_input,
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outputs=input_text
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).then(
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fn=translate,
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inputs=[input_text, translation_direction],
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outputs=[output_text, input_text, output_text]
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).then(
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fn=synthesize_tts,
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inputs=[output_text, translation_direction],
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outputs=audio_output
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)
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if __name__ == "__main__":
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interface.queue().launch()
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postBuild
CHANGED
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python -c "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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python -c "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline; \
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AutoModelForSeq2SeqLM.from_pretrained('ai4bharat/indictrans2-en-indic-1B'); \
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AutoTokenizer.from_pretrained('ai4bharat/indictrans2-en-indic-1B'); \
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AutoModelForSeq2SeqLM.from_pretrained('ai4bharat/indictrans2-indic-en-1B'); \
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AutoTokenizer.from_pretrained('ai4bharat/indictrans2-indic-en-1B'); \
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pipeline('automatic-speech-recognition', model='openai/whisper-small'); \
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pipeline('text-to-speech', model='espnet/kan-bayashi_ljspeech_vits')"
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requirements.txt
CHANGED
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torch>=1.12
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transformers>=4.30.0
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gradio
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requests
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sentencepiece # Optional, may still be needed by transformers
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git+https://github.com/VarunGumma/IndicTransToolkit.git
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torch>=1.12
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transformers>=4.30.0
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sentencepiece # Required for tokenizer in IndicTrans2
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torchaudio # Required by Whisper and ESPnet TTS
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gradio
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requests
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git+https://github.com/VarunGumma/IndicTransToolkit.git
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