Create app.py

app.py

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+import os
+import gradio as gr
+from google.cloud import speech, texttospeech
+from nlp import process_query
+from utils.language_support import detect_language, translate_text
+
+# Set Google credentials
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "credentials/google_credentials.json"
+
+# Initialize Google Speech-to-Text client
+speech_client = speech.SpeechClient()
+
+# Initialize Google Text-to-Speech client
+tts_client = texttospeech.TextToSpeechClient()
+
+# Function to transcribe speech to text
+def transcribe_speech(audio_file):
+    with open(audio_file, "rb") as f:
+        audio_data = f.read()
+
+    audio = speech.RecognitionAudio(content=audio_data)
+    config = speech.RecognitionConfig(
+        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
+        language_code="ta-IN",  # Default to Tamil; can detect dynamically
+        enable_automatic_punctuation=True
+    )
+
+    response = speech_client.recognize(config=config, audio=audio)
+    if response.results:
+        return response.results[0].alternatives[0].transcript
+    else:
+        return "Could not transcribe the audio."
+
+# Function to convert text to speech
+def generate_speech(text, language_code="ta-IN"):
+    input_text = texttospeech.SynthesisInput(text=text)
+    voice = texttospeech.VoiceSelectionParams(
+        language_code=language_code, 
+        ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL
+    )
+    audio_config = texttospeech.AudioConfig(audio_encoding=texttospeech.AudioEncoding.MP3)
+
+    response = tts_client.synthesize_speech(
+        input=input_text, voice=voice, audio_config=audio_config
+    )
+    return response.audio_content
+
+# Main function to handle user interaction
+def voice_assistant(audio_file):
+    # Step 1: Transcribe audio to text
+    user_input = transcribe_speech(audio_file)
+    if not user_input:
+        return "Could not process your query. Please try again.", None
+
+    # Step 2: Detect language
+    language_code = detect_language(user_input)
+
+    # Step 3: Process the query using NLP
+    response_text = process_query(user_input, language_code)
+
+    # Step 4: Generate speech response
+    speech_output = generate_speech(response_text, language_code)
+
+    return response_text, speech_output
+
+# Gradio interface
+def app_interface(audio_file):
+    text_response, audio_response = voice_assistant(audio_file)
+    return text_response, (audio_response, "output.mp3")
+
+# Gradio UI
+app = gr.Interface(
+    fn=app_interface,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=[
+        gr.Textbox(label="Bot Response"),
+        gr.Audio(label="Bot Voice Response")
+    ],
+    title="Multilingual AI Voice Assistant",
+    description="Speak to the bot in your preferred regional language (Tamil, Hindi, Malayalam, Telugu, Kannada)."
+)
+
+if __name__ == "__main__":
+    app.launch()
+