Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import os
+import warnings
+from gtts import gTTS
+from deep_translator import GoogleTranslator
+from langdetect import detect
+
+# Load language dataset
+dataset = pd.read_csv(r"C:\Users\thila\Downloads\NARESH IT  PYTHON FSDS NOTES\MY PROJECTS\Language Translation\language.csv")
+dataset.dropna(inplace=True)
+
+# Extract language names and codes
+langlist = tuple(dataset['name'].tolist())
+langcode = dataset['iso'].tolist()
+lang_array = {langlist[i]: langcode[i] for i in range(len(langlist))}
+
+speech_langs = {"en": "English", "es": "Spanish", "fr": "French", "de": "German", "hi": "Hindi", "zh-CN": "Chinese", "te": "Telugu"}
+
+# Function to translate and speak
+def translate_and_speak(text, selected_option):
+    if len(text) > 0:
+        try:
+            detected_lang = detect(text)  # Detect input language
+            source_lang = "en" if detected_lang == "en" else detected_lang  # Handle Tanglish
+
+            translator = GoogleTranslator(source=source_lang, target=lang_array[selected_option])
+            output = translator.translate(text)
+
+            audio_file = None
+            if lang_array[selected_option] in speech_langs:
+                tts = gTTS(text=output, lang=lang_array[selected_option], slow=False)
+                audio_file = "translated_audio.mp3"
+                tts.save(audio_file)
+
+            return output, audio_file
+        except Exception as e:
+            return str(e), None
+    return "", None
+
+# Gradio UI
+demo = gr.Interface(
+    fn=translate_and_speak,
+    inputs=[gr.Textbox(label="Write Your Text"), gr.Dropdown(choices=list(langlist), label="Select Language to Translate")],
+    outputs=[gr.Textbox(label="Translated Text"), gr.Audio(label="Translated Audio")],
+    title="Language Translation App",
+    description="Translate text into different languages, including transliterated Telugu (Tanglish), and listen to the translation."
+)
+
+demo.launch()
+