import streamlit as st import pandas as pd import numpy as np import os import time import matplotlib.pyplot as plt from datetime import datetime import tempfile import io import json from model.transcriber import transcribe_audio from predict import predict_emotion # You'll need to install this package: # pip install streamlit-audiorec from st_audiorec import st_audiorec AUDIO_WAV = 'audio/wav' MAX_FILE_SIZE_MB = 10 # Page configuration st.set_page_config( page_title="Emotional Report Analyzer", page_icon="🎤", layout="wide" ) # Initialize session state variables if they don't exist if 'audio_data' not in st.session_state: st.session_state.audio_data = [] if 'current_audio_index' not in st.session_state: st.session_state.current_audio_index = -1 if 'audio_history_csv' not in st.session_state: # Define columns for our CSV storage st.session_state.audio_history_csv = pd.DataFrame( columns=['timestamp', 'file_path', 'transcription', 'emotion', 'probabilities'] ) if 'needs_rerun' not in st.session_state: st.session_state.needs_rerun = False # Function to ensure we keep only the last 10 entries def update_audio_history(new_entry): # Add the new entry st.session_state.audio_history_csv = pd.concat([st.session_state.audio_history_csv, pd.DataFrame([new_entry])], ignore_index=True) # Keep only the last 10 entries if len(st.session_state.audio_history_csv) > 10: st.session_state.audio_history_csv = st.session_state.audio_history_csv.iloc[-10:] # Save to CSV st.session_state.audio_history_csv.to_csv('audio_history.csv', index=False) # Function to process audio and get results def process_audio(audio_path): try: # Get transcription transcription = transcribe_audio(audio_path) # Get emotion prediction predicted_emotion, probabilities = predict_emotion(audio_path) # Update audio history new_entry = { 'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'file_path': audio_path, 'transcription': transcription, 'emotion': predicted_emotion, 'probabilities': str(probabilities) # Convert dict to string for storage } update_audio_history(new_entry) # Update current index st.session_state.current_audio_index = len(st.session_state.audio_history_csv) - 1 return transcription, predicted_emotion, probabilities except Exception as e: st.error(f"Error processing audio: {str(e)}") return None, None, None # Function to split audio into 10-second segments def split_audio(audio_file, segment_length=10): # This is a placeholder - in a real implementation, you'd use a library like pydub # to split the audio file into segments st.warning("Audio splitting functionality is a placeholder. Implement with pydub or similar library.") # For now, we'll just return the whole file as a single segment return [audio_file] # Function to display emotion visualization def display_emotion_chart(probabilities): emotions = list(probabilities.keys()) values = list(probabilities.values()) fig, ax = plt.subplots(figsize=(10, 5)) bars = ax.bar(emotions, values, color=['red', 'gray', 'green']) # Add data labels on top of bars for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height + 0.02, f'{height:.2f}', ha='center', va='bottom') ax.set_ylim(0, 1.1) ax.set_ylabel('Probability') ax.set_title('Emotion Prediction Results') st.pyplot(fig) # Trigger rerun if needed (replaces experimental_rerun) if st.session_state.needs_rerun: st.session_state.needs_rerun = False st.rerun() # Using st.rerun() instead of experimental_rerun col_logo, col_name = st.columns([3, 1]) col_logo.image("./img/logo_01.png", width=400) col_name.title("Emotional Report") # Create two columns for the main layout col1, col2 = st.columns([1, 1]) with col1: st.header("Audio Input") # Method selection tab1, tab2 = st.tabs(["Record Audio", "Upload Audio"]) with tab1: st.write("Record your audio (max 10 seconds):") # Using streamlit-audiorec for better recording functionality wav_audio_data = st_audiorec() if wav_audio_data is not None: # Save the recorded audio to a temporary file with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file: tmp_file.write(wav_audio_data) tmp_file_path = tmp_file.name st.success("Audio recorded successfully!") # Process button if st.button("Process Recorded Audio"): # Process the audio with st.spinner("Processing audio..."): transcription, emotion, probs = process_audio(tmp_file_path) # Set flag for rerun instead of calling experimental_rerun if transcription is not None: st.success("Audio processed successfully!") st.session_state.needs_rerun = True with tab2: uploaded_file = st.file_uploader("Upload an audio file (WAV format)", type=['wav']) if uploaded_file is not None and uploaded_file.type == AUDIO_WAV and uploaded_file.size < MAX_FILE_SIZE_MB * 1_000_000: try: # Save the uploaded file to a temporary location with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file: tmp_file.write(uploaded_file.getbuffer()) tmp_file_path = tmp_file.name except Exception as e: st.error(f"Error saving uploaded file: {str(e)}") st.error(f"Try to record your voice directly, maybe your storage is locked.") st.audio(uploaded_file, format="audio/wav") # Process button if st.button("Process Uploaded Audio"): # Split audio into 10-second segments with st.spinner("Processing audio..."): segments = split_audio(tmp_file_path) # Process each segment for i, segment_path in enumerate(segments): st.write(f"Processing segment {i+1}...") transcription, emotion, probs = process_audio(segment_path) # Set flag for rerun instead of calling experimental_rerun st.success("Audio processed successfully!") st.session_state.needs_rerun = True # Audio History and Analytics Section st.header("Audio History and Analytics") if len(st.session_state.audio_history_csv) > 0: # Display a select box to choose from audio history timestamps = st.session_state.audio_history_csv['timestamp'].tolist() selected_timestamp = st.selectbox( "Select audio from history:", options=timestamps, index=len(timestamps) - 1 # Default to most recent ) # Update current index when selection changes selected_index = st.session_state.audio_history_csv[ st.session_state.audio_history_csv['timestamp'] == selected_timestamp ].index[0] # Only update if different if st.session_state.current_audio_index != selected_index: st.session_state.current_audio_index = selected_index st.session_state.needs_rerun = True # Analytics button if st.button("Run Analytics on Selected Audio"): st.subheader("Analytics Results") # Get the selected audio data selected_data = st.session_state.audio_history_csv.iloc[selected_index] # Display analytics (this is where you would add more sophisticated analytics) st.write(f"Selected Audio: {selected_data['timestamp']}") st.write(f"Emotion: {selected_data['emotion']}") st.write(f"File Path: {selected_data['file_path']}") # Add any additional analytics you want here # Try to play the selected audio try: if os.path.exists(selected_data['file_path']): st.audio(selected_data['file_path'], format="audio/wav") else: st.warning("Audio file not found - it may have been deleted or moved.") except Exception as e: st.error(f"Error playing audio: {str(e)}") else: st.info("No audio history available. Record or upload audio to create history.") with col2: st.header("Results") # Display results if available if st.session_state.current_audio_index >= 0 and len(st.session_state.audio_history_csv) > 0: current_data = st.session_state.audio_history_csv.iloc[st.session_state.current_audio_index] # Transcription st.subheader("Transcription") st.text_area("", value=current_data['transcription'], height=100, key="transcription_area") # Emotion st.subheader("Detected Emotion") st.info(f"🎭 Predicted emotion: **{current_data['emotion']}**") # Convert string representation of dict back to actual dict try: import ast probs = ast.literal_eval(current_data['probabilities']) display_emotion_chart(probs) except Exception as e: st.error(f"Error parsing probabilities: {str(e)}") st.write(f"Raw probabilities: {current_data['probabilities']}") else: st.info("Record or upload audio to see results") # Footer st.markdown("---") st.caption("Emotional Report Analyzer - Processes audio in 10-second segments and predicts emotions")