Update app.py

app.py

@@ -1,71 +1,71 @@
-import os
-import streamlit as st
-from pydub import AudioSegment
-from groq import Groq
-
-# Set ffmpeg path
-ffmpeg_path = r"C:\Users\AMAR\Downloads\ffmpeg-7.0.2-essentials_build\ffmpeg-7.0.2-essentials_build\bin\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}")
+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}")