Update app.py

app.py

@@ -1,173 +1,3 @@
-# import os
-# import gradio as gr
-# import whisper
-# from gtts import gTTS
-# import io
-# from groq import Groq
-
-# # Initialize the Groq client
-# groq_api_key = os.getenv('GROQ_API_KEY')
-# client = Groq(api_key=groq_api_key) 
-
-# # Load the Whisper model
-# model = whisper.load_model("base")  # You can choose other models like "small", "medium", "large"
-
-# def process_audio(file_path):
-#     try:
-#         # Load the audio file
-#         audio = whisper.load_audio(file_path)
-
-#         # Transcribe the audio using Whisper
-#         result = model.transcribe(audio)
-#         text = result["text"]
-
-#         # Generate a response using Groq
-#         chat_completion = client.chat.completions.create(
-#             messages=[{"role": "user", "content": text}],
-#             model="llama3-8b-8192",  # Replace with the correct model if necessary
-#         )
-
-#         # Access the response using dot notation
-#         response_message = chat_completion.choices[0].message.content.strip()
-
-#         # Convert the response text to speech
-#         tts = gTTS(response_message)
-#         response_audio_io = io.BytesIO()
-#         tts.write_to_fp(response_audio_io)  # Save the audio to the BytesIO object
-#         response_audio_io.seek(0)
-
-#         # Save audio to a file to ensure it's generated correctly
-#         with open("response.mp3", "wb") as audio_file:
-#             audio_file.write(response_audio_io.getvalue())
-
-#         # Return the response text and the path to the saved audio file
-#         return response_message, "response.mp3"
-
-#     except Exception as e:
-#         return f"An error occurred: {e}", None
-
-# iface = gr.Interface(
-#     fn=process_audio,
-#     inputs=gr.Audio(type="filepath"),  # Use type="filepath"
-#     outputs=[gr.Textbox(label="Response Text"), gr.Audio(label="Response Audio")],
-#     live=True
-# )
-
-# # iface.launch()
-# import os
-# import gradio as gr
-# import whisper
-# from gtts import gTTS
-# from anthropic import Anthropic  # Import the Anthropic client
-# import io  # Import io for BytesIO
-
-# # Get the Anthropic API key from environment variables
-# ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
-# if not ANTHROPIC_API_KEY:
-#     raise ValueError("ANTHROPIC_API_KEY environment variable is not set.")
-# client = Anthropic(api_key=ANTHROPIC_API_KEY)  # Initialize the Anthropic client
-
-# # Load Whisper model
-# model = whisper.load_model("base")  # You can also use "small", "medium", "large"
-
-# def chatbot(audio=None):
-#     try:
-#         if audio is None:
-#             return "No input detected. Please provide an audio input.", None
-
-#         # Transcribe the audio input using Whisper
-#         transcription = model.transcribe(audio)
-#         user_input = transcription.get("text", "")
-
-#         # Generate a response using Anthropic API
-#         chat_completion = client.completions.create(
-#             model="claude-v1",  # Specify the model
-#             prompt=user_input,   # Provide the user input as the prompt
-#             max_tokens_to_sample=100,  # Specify the maximum tokens to sample
-#         )
-#         response_text = chat_completion['completion']
-
-#         # Convert the response text to speech using gTTS
-#         tts = gTTS(text=response_text, lang='en')
-#         response_audio_io = io.BytesIO()  # Create a BytesIO object
-#         tts.save(response_audio_io)  # Save the audio to the BytesIO object
-#         response_audio_io.seek(0)  # Rewind the BytesIO object
-
-#         return response_text, response_audio_io
-
-#     except Exception as e:
-#         return f"An error occurred: {e}", None
-
-# def clear_inputs():
-#     return None, None, None
-
-# # Create a custom interface
-# def build_interface():
-#     with gr.Blocks(css="""
-#         .block-title {
-#             text-align: center; 
-#             color: white;
-#             background-color: #4CAF50; 
-#             padding: 10px;
-#             border-radius: 8px;
-#         }
-#         .gradio-row {
-#             background-color: #f9f9f9;
-#             border-radius: 8px;
-#             padding: 20px;
-#             margin: 10px;
-#             box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.1);
-#         }
-#         .gradio-column {
-#             padding: 10px;
-#         }
-#         .gradio-button {
-#             background-color: #ff6347 !important;
-#             color: white !important;
-#             border-radius: 8px !important;
-#             padding: 10px 20px !important;
-#             font-size: 16px !important;
-#             border: none !important;
-#             cursor: pointer !important;
-#             box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.2) !important;
-#             transition: background-color 0.3s ease !important;
-#         }
-#         .gradio-button:hover {
-#             background-color: #e5533d !important;
-#         }
-#     """) as demo:
-#         gr.Markdown(
-#             """
-#             <h1 class="block-title">Voice-to-Voice AI Chatbot</h1>
-#             """
-#         )
-#         with gr.Row(elem_classes="gradio-row"):
-#             with gr.Column(elem_classes="gradio-column", scale=1):
-#                 audio_input = gr.Audio(type="filepath", label="Record Your Voice")
-#             with gr.Column(elem_classes="gradio-column", scale=2):
-#                 chatbot_output_text = gr.Textbox(label="Chatbot Response")
-#                 chatbot_output_audio = gr.Audio(label="Audio Response")
-
-#         clear_button = gr.Button("Clear", elem_classes="gradio-button")
-
-#         clear_button.click(
-#             fn=clear_inputs,
-#             outputs=[audio_input, chatbot_output_text, chatbot_output_audio]
-#         )
-
-#         audio_input.change(
-#             fn=chatbot,
-#             inputs=[audio_input],
-#             outputs=[chatbot_output_text, chatbot_output_audio]
-#         )
-
-#     return demo
-
-# # Launch the interface
-# if __name__ == "__main__":
-#     interface = build_interface()
-#     interface.launch()
-
 import gradio as gr
 import whisper
 from gtts import gTTS