Update app.py

app.py

@@ -1,12 +1,80 @@
-# import os
-# import streamlit as st
+# # import os
+# # import streamlit as st
+# # from pydub import AudioSegment
+# # from groq import Groq
+
+# # # Set ffmpeg path
+# # ffmpeg_path = r"ffmpeg.exe"
+# # os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
+# # AudioSegment.converter = ffmpeg_path
+
+# # # Groq API configuration
+# # groq_api_key = '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'  # Replace with your actual API key
+# # client = Groq(api_key=groq_api_key)
+# # model = 'whisper-large-v3'
+
+# # # Function to ensure the file is in a suitable format
+# # def ensure_suitable_format(file_path):
+# #     allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
+# #     file_extension = file_path.split('.')[-1].lower()
+# #     if file_extension not in allowed_formats:
+# #         new_file_path = f"{os.path.splitext(file_path)[0]}.wav"
+# #         os.rename(file_path, new_file_path)
+# #         return new_file_path
+# #     return file_path
+
+# # # Function to convert audio to WAV
+# # def convert_audio_to_wav(input_path, output_path):
+# #     audio = AudioSegment.from_file(input_path)
+# #     audio.export(output_path, format="wav")
+# #     return output_path
+
+# # # Function to transcribe audio using Groq
+# # def audio_to_text(filepath):
+# #     with open(filepath, "rb") as file:
+# #         translation = client.audio.translations.create(
+# #             file=(filepath, file.read()),
+# #             model=model,
+# #         )
+# #     return translation.text
+
+# # # Streamlit App UI
+# # st.title("Audio-to-Text Transcription")
+# # st.write("Upload an audio file to get the transcribed text.")
+
+# # # File upload
+# # uploaded_file = st.file_uploader("Upload your audio file", type=["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"])
+
+# # if uploaded_file:
+# #     # Save the uploaded file locally
+# #     file_path = os.path.join("uploaded_audio", uploaded_file.name)
+# #     os.makedirs("uploaded_audio", exist_ok=True)
+# #     with open(file_path, "wb") as f:
+# #         f.write(uploaded_file.getbuffer())
+    
+# #     st.write(f"File uploaded: {uploaded_file.name}")
+
+# #     # Ensure file format is suitable
+# #     suitable_audio_path = ensure_suitable_format(file_path)
+
+# #     # Convert audio to WAV
+# #     wav_path = f"{os.path.splitext(suitable_audio_path)[0]}.wav"
+# #     converted_audio = convert_audio_to_wav(suitable_audio_path, wav_path)
+
+# #     # Transcribe audio
+# #     st.write("Processing transcription...")
+# #     try:
+# #         transcription = audio_to_text(converted_audio)
+# #         st.success("Transcription complete!")
+# #         st.text_area("Transcribed Text", transcription, height=200)
+# #     except Exception as e:
+# #         st.error(f"Error during transcription: {e}")
+
+
 # from pydub import AudioSegment
 # from groq import Groq
-
-# # Set ffmpeg path
-# ffmpeg_path = r"ffmpeg.exe"
-# os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
-# AudioSegment.converter = ffmpeg_path
+# import os
+# import streamlit as st
 
 # # Groq API configuration
 # groq_api_key = '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'  # Replace with your actual API key
@@ -70,16 +138,10 @@
 #     except Exception as e:
 #         st.error(f"Error during transcription: {e}")
 
-
-from pydub import AudioSegment
-from groq import Groq
 import os
 import streamlit as st
-
-# Groq API configuration
-groq_api_key = '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'  # Replace with your actual API key
-client = Groq(api_key=groq_api_key)
-model = 'whisper-large-v3'
+from pydub import AudioSegment
+from groq import Groq
 
 # Function to ensure the file is in a suitable format
 def ensure_suitable_format(file_path):
@@ -98,7 +160,9 @@ def convert_audio_to_wav(input_path, output_path):
     return output_path
 
 # Function to transcribe audio using Groq
-def audio_to_text(filepath):
+def audio_to_text(filepath, groq_api_key):
+    client = Groq(api_key=groq_api_key)
+    model = 'whisper-large-v3'
     with open(filepath, "rb") as file:
         translation = client.audio.translations.create(
             file=(filepath, file.read()),
@@ -110,10 +174,13 @@ def audio_to_text(filepath):
 st.title("Audio-to-Text Transcription")
 st.write("Upload an audio file to get the transcribed text.")
 
+# Input for API key
+groq_api_key = st.text_input("Enter your Groq API Key", type="password")
+
 # File upload
 uploaded_file = st.file_uploader("Upload your audio file", type=["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"])
 
-if uploaded_file:
+if groq_api_key and uploaded_file:
     # Save the uploaded file locally
     file_path = os.path.join("uploaded_audio", uploaded_file.name)
     os.makedirs("uploaded_audio", exist_ok=True)
@@ -132,9 +199,10 @@ if uploaded_file:
     # Transcribe audio
     st.write("Processing transcription...")
     try:
-        transcription = audio_to_text(converted_audio)
+        transcription = audio_to_text(converted_audio, groq_api_key)
         st.success("Transcription complete!")
         st.text_area("Transcribed Text", transcription, height=200)
     except Exception as e:
         st.error(f"Error during transcription: {e}")
-
+elif not groq_api_key:
+    st.warning("Please enter your Groq API Key to proceed.")