Create app.py

app.py

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+import torch
+import gradio as gr
+import json
+from transformers import pipeline
+
+# Load the translation pipeline
+text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M", torch_dtype=torch.bfloat16)
+
+# Load the JSON data for language codes
+with open('language.json', 'r') as file:
+    language_data = json.load(file)
+
+# Get all available languages (excluding duplicates with different scripts)
+available_languages = []
+seen_languages = set()
+for entry in language_data:
+    base_language = entry['Language'].split('(')[0].strip()
+    if base_language not in seen_languages:
+        available_languages.append(base_language)
+        seen_languages.add(base_language)
+
+# Sort languages alphabetically
+available_languages.sort()
+
+# Function to retrieve FLORES-200 code for a given language
+def get_FLORES_code_from_language(language):
+    # Try exact match first
+    for entry in language_data:
+        if entry['Language'].lower() == language.lower():
+            return entry['FLORES-200 code']
+    # Fallback to matching base language name
+    for entry in language_data:
+        if entry['Language'].lower().startswith(language.lower()):
+            return entry['FLORES-200 code']
+    return None
+
+# Translation function
+def translate_text(text, destination_language):
+    dest_code = get_FLORES_code_from_language(destination_language)
+    if dest_code is None:
+        return f"Error: Could not find FLORES code for language {destination_language}"
+    
+    translation = text_translator(text, src_lang="eng_Latn", tgt_lang=dest_code)
+    return translation[0]["translation_text"]
+
+# Speech-to-text pipeline
+speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+
+# Function to transcribe audio to text
+def transcribe_audio(audio_file, destination_language):
+    transcription = speech_to_text(audio_file)["text"]
+    return translate_text(transcription, destination_language)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI-Powered Language Translation Bot")
+    gr.Markdown(
+        "This bot allows you to translate English text to various languages or convert audio input into transcribed text and translate it into your desired language."
+    )
+
+    with gr.Tabs():
+        with gr.Tab("Text Translation"):
+            text_input = gr.Textbox(label="Input text to translate", lines=6)
+            language_dropdown = gr.Dropdown(
+                choices=available_languages, label="Select Destination Language"
+            )
+            translated_text_output = gr.Textbox(label="Translated Text", lines=4)
+            translate_button = gr.Button("Translate")
+
+            translate_button.click(
+                translate_text, inputs=[text_input, language_dropdown], outputs=[translated_text_output]
+            )
+
+        with gr.Tab("Audio Translation"):
+            audio_input = gr.Audio(label="Upload Audio File", type="filepath")
+            audio_language_dropdown = gr.Dropdown(
+                choices=available_languages, label="Select Destination Language"
+            )
+            audio_translated_text_output = gr.Textbox(label="Transcribed and Translated Text", lines=4)
+            audio_translate_button = gr.Button("Transcribe and Translate")
+
+            audio_translate_button.click(
+                transcribe_audio,
+                inputs=[audio_input, audio_language_dropdown],
+                outputs=[audio_translated_text_output],
+            )
+
+if __name__ == "__main__":
+    demo.launch()