Update app.py

app.py

@@ -1,7 +1,8 @@
 import os
 from dotenv import load_dotenv
 import streamlit as st
-from groq import Groq
+from groq import Groq  # Ensure this is the correct import based on Groq's SDK
+import tempfile
 
 # Load environment variables
 load_dotenv()
@@ -11,7 +12,7 @@ client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
 # Streamlit UI
 st.title("Voice Cloning Application")
-st.markdown("Clone your voice using Groq's Whisper Model for transcription.")
+st.markdown("Clone your voice using Groq's Whisper Model and generate natural responses.")
 
 # Upload audio file
 uploaded_file = st.file_uploader(
@@ -21,27 +22,45 @@ uploaded_file = st.file_uploader(
 
 if uploaded_file is not None:
     # Display uploaded audio
-    st.audio(uploaded_file, format=f"audio/{uploaded_file.type.split('.')[-1]}")
+    audio_format = uploaded_file.type.split('/')[-1]
+    st.audio(uploaded_file, format=f"audio/{audio_format}")
     st.write("Transcription in progress...")
 
-    # Read file contents as binary
-    audio_bytes = uploaded_file.read()
+    # Save the uploaded file to a temporary location
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.' + audio_format) as temp_audio:
+        temp_audio.write(uploaded_file.read())
+        temp_audio_path = temp_audio.name
 
+    # Transcription Logic
     try:
-        # Transcription request to the Whisper model
-        transcription = client.audio.transcriptions.create(
-            file=audio_bytes,
-            model="whisper-large-v3-turbo",
-            response_format="text",  # Options: "text", "json"
+        # Replace the following with the correct transcription method provided by Groq
+        transcription_response = client.transcriptions.create(
+            file_path=temp_audio_path,
+            model="whisper-large-v3-turbo"
         )
 
-        # Display the transcription
+        # Extract the transcribed text from the response
+        transcribed_text = transcription_response['transcription']  # Adjust based on actual response structure
         st.success("Transcription completed!")
-        st.write("**Transcribed Text:**")
-        st.text_area("Transcription", transcription, height=200)
+        st.write("**Transcribed Text:**", transcribed_text)
+
+        # Placeholder for voice cloning (TTS integration can go here)
+        st.markdown("---")
+        st.subheader("Generate Speech from Transcription")
+        tts_input = st.text_area("Enter text to generate speech:", value=transcribed_text)
+
+        if st.button("Generate Speech"):
+            if tts_input:
+                # Simulate TTS functionality (placeholder for TTS model integration)
+                st.success("Generated speech successfully! (Placeholder)")
+            else:
+                st.warning("Please enter some text.")
 
     except Exception as e:
         st.error(f"Error during transcription: {e}")
+    finally:
+        # Clean up the temporary file
+        os.remove(temp_audio_path)
 
 # Footer
 st.markdown("Developed with ❤️ by Sanam Iftakhar")