Delete app.py

app.py

@@ -1,140 +0,0 @@
-import gradio as gr
-import whisper
-from gtts import gTTS
-from groq import Groq
-import os
-import numpy as np
-import soundfile as sf
-import logging
-
-# Initialize the Groq API key
-GROQ_API_KEY = "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"
-
-# Configure logging
-logging.basicConfig(level=logging.DEBUG)
-
-# Initialize Whisper model (No API key required)
-try:
-    whisper_model = whisper.load_model("base")
-    logging.info("Whisper model loaded successfully.")
-except Exception as e:
-    raise RuntimeError(f"Error loading Whisper model: {e}")
-
-# Initialize Groq client (API key required for Groq API)
-try:
-    client = Groq(
-        api_key=GROQ_API_KEY  # Directly use the API key from the variable
-    )
-    logging.info("Groq client initialized successfully.")
-except Exception as e:
-    raise RuntimeError(f"Error initializing Groq client: {e}")
-
-# Function to transcribe audio using Whisper
-def transcribe_audio(audio):
-    try:
-        # Load audio file with soundfile
-        logging.debug(f"Loading audio file: {audio}")
-        audio_data, sample_rate = sf.read(audio, dtype='float32')  # Ensure dtype is float32
-        logging.debug(f"Audio loaded with sample rate: {sample_rate}, data shape: {audio_data.shape}")
-
-        # Whisper expects a specific sample rate
-        if sample_rate != 16000:
-            logging.debug(f"Resampling audio from {sample_rate} to 16000 Hz")
-            # Resample audio data to 16000 Hz
-            num_samples = int(len(audio_data) * (16000 / sample_rate))
-            audio_data_resampled = np.interp(np.linspace(0, len(audio_data), num_samples),
-                                             np.arange(len(audio_data)),
-                                             audio_data)
-            audio_data = audio_data_resampled.astype(np.float32)  # Ensure dtype is float32
-            sample_rate = 16000
-
-        # Perform the transcription
-        result = whisper_model.transcribe(audio_data)
-        logging.debug(f"Transcription result: {result['text']}")
-        return result['text']
-    except Exception as e:
-        logging.error(f"Error during transcription: {e}")
-        return f"Error during transcription: {e}"
-
-# Function to get response from LLaMA model using Groq API
-def get_response(text):
-    try:
-        logging.debug(f"Sending request to Groq API with text: {text}")
-        chat_completion = client.chat.completions.create(
-            messages=[
-                {
-                    "role": "user",
-                    "content": text,  # Using the transcribed text as input
-                }
-            ],
-            model="llama3-8b-8192",  # Ensure the correct model is used
-        )
-
-        # Extract and return the model's response
-        response_text = chat_completion.choices[0].message.content
-        logging.debug(f"Received response from Groq API: {response_text}")
-        return response_text
-    except Exception as e:
-        logging.error(f"Error during model response generation: {e}")
-        return f"Error during model response generation: {e}"
-
-# Function to convert text to speech using gTTS
-def text_to_speech(text):
-    try:
-        tts = gTTS(text)
-        tts.save("response.mp3")
-        logging.debug("Text-to-speech conversion completed successfully.")
-        return "response.mp3"
-    except Exception as e:
-        logging.error(f"Error during text-to-speech conversion: {e}")
-        return f"Error during text-to-speech conversion: {e}"
-
-# Combined function for Gradio
-def chatbot(audio):
-    try:
-        # Step 1: Transcribe the audio input using Whisper
-        user_input = transcribe_audio(audio)
-
-        # Check if transcription returned an error
-        if "Error" in user_input:
-            return user_input, None
-
-        logging.debug(f"Transcribed text: {user_input}")
-
-        # Step 2: Get response from the LLaMA model using Groq API
-        response_text = get_response(user_input)
-
-        # Check if the response generation returned an error
-        if "Error" in response_text:
-            return response_text, None
-
-        logging.debug(f"Response text: {response_text}")
-
-        # Step 3: Convert the response text to speech using gTTS
-        response_audio = text_to_speech(response_text)
-
-        # Check if the text-to-speech conversion returned an error
-        if "Error" in response_audio:
-            return response_audio, None
-
-        # Step 4: Return the response text and response audio file
-        return response_text, response_audio
-
-    except Exception as e:
-        logging.error(f"Unexpected error occurred: {e}")
-        return f"Unexpected error occurred: {e}", None
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=chatbot,
-    inputs=gr.Audio(type="filepath"),
-    outputs=[gr.Textbox(label="Response Text"), gr.Audio(label="Response Audio")],
-    live=True,
-    title="Voice-to-Voice Chatbot",
-    description="Speak to the bot, and it will respond with voice.",
-)
-
-try:
-    iface.launch()
-except Exception as e:
-    logging.error(f"Error launching Gradio interface: {e}")