Update app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from pyannote.audio import Pipeline
+from transformers import pipeline
+import whisper
+
+# Title
+st.title("Multi-Speaker Audio Analyzer")
+
+# Upload Audio File
+uploaded_file = st.file_uploader("Upload an audio file (MP3/WAV)", type=["mp3", "wav"])
+
+# Process Button
+if uploaded_file:
+    st.audio(uploaded_file, format='audio/wav')
+
+    # Load pre-trained models
+    diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
+    transcription_model = whisper.load_model("base")
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+    # Perform Speaker Diarization
+    st.write("Processing Speaker Diarization...")
+    diarized_output = diarization_pipeline(uploaded_file)
+
+    # Perform Speech-to-Text Transcription
+    st.write("Transcribing Audio...")
+    transcription = transcription_model.transcribe(uploaded_file)
+
+    # Generate Summary
+    st.write("Generating Summary...")
+    summary = summarizer(transcription["text"])
+
+    # Display Outputs
+    st.write("Speaker-Diarized Transcript:")
+    st.text(diarized_output)
+
+    st.write("Full Transcription:")
+    st.text(transcription["text"])
+
+    st.write("Summary:")
+    st.text(summary[0]['summary_text'])