Rename untitled.py to app.py

untitled.py → app.py

@@ -1,14 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Untitled
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/12GhPKbBzxei0ZhB0r-m5kvNOaCRyCxiM
-"""
-
-!pip install gradio openai gtts pydub numpy requests groq openai-whisper transformers
-!apt-get install -y ffmpeg
 
 import os
 os.environ["GROQ_API_KEY"] = "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"
@@ -87,57 +76,6 @@ iface.launch()
 
 
 
-# import os
-# import gradio as gr
-# import whisper
-# from gtts import gTTS
-# import io
-# from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-# from groq import Groq
-
-# # Initialize the Groq client
-# client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-# # Load the Whisper model
-# whisper_model = whisper.load_model("base")  # You can choose other models like "small", "medium", "large"
-
-# # Initialize the grammar correction pipeline
-# corrector = pipeline("text2text-generation", model="pszemraj/flan-t5-large-grammar-synthesis")
-
-# def process_audio(file_path):
-#     try:
-#         # Load the audio file
-#         audio = whisper.load_audio(file_path)
-
-#         # Transcribe the audio using Whisper
-#         result = whisper_model.transcribe(audio)
-#         user_text = result["text"]
-
-#         # Display the user input text
-#         corrected_text = corrector(user_text)[0]['generated_text'].strip()
-
-#         # Generate a response using Groq
-#         chat_completion = client.chat.completions.create(
-#             messages=[{"role": "user", "content": corrected_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 original text, corrected text, and the path to the saved audio file
-#         return user_text, corrected_text, "response.mp3"
-
 #     except Exception as e:
 #         return f"An error occurred: {e}", None, None
 
@@ -156,60 +94,3 @@ iface.launch()
 
 
 
-
-# # import os
-# # import gradio as gr
-# # import whisper
-# # from gtts import gTTS
-# # import io
-# # from groq import Groq
-
-# # # Initialize the Groq client
-# # client = Groq(api_key=os.environ.get("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()
-