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| import streamlit as st | |
| import plotly.graph_objects as go | |
| from aira.core import AmbisonicsImpulseResponseAnalyzer | |
| from aira.engine.input import InputMode | |
| import os | |
| import tempfile | |
| def save_temp_file(file): | |
| temp_dir = tempfile.gettempdir() | |
| temp_file_path = os.path.join(temp_dir, file.name) | |
| with open(temp_file_path, "wb") as temp_file: | |
| temp_file.write(file.getvalue()) | |
| return temp_file_path | |
| def run_streamlit_app(): | |
| st.set_page_config( | |
| page_title="AIRA", page_icon="docs/images/aira-icon.png", layout="wide" | |
| ) | |
| # Logo | |
| logo_path = "docs/images/aira-banner.png" | |
| personal_logo_path = "docs/images/nahue-passano.png" | |
| st.columns(11)[10].image(personal_logo_path, use_column_width=True) | |
| st.columns(3)[1].image(logo_path, use_column_width=True) | |
| # Audio loading and settings | |
| audio_files, settings = st.columns(2) | |
| with audio_files: | |
| st.header("🔉 LSS room responses in A-Format") | |
| up_files, down_files = st.columns(2) | |
| with up_files: | |
| audio_file_flu = st.file_uploader("Front-Left-Up", type=["mp3", "wav"]) | |
| audio_file_bru = st.file_uploader("Back-Right-Up", type=["mp3", "wav"]) | |
| with down_files: | |
| audio_file_frd = st.file_uploader("Front-Right-Down", type=["mp3", "wav"]) | |
| audio_file_bld = st.file_uploader("Back-Left-Down", type=["mp3", "wav"]) | |
| audio_file_inverse_filter = st.file_uploader( | |
| "Inverse filter", type=["mp3", "wav"] | |
| ) | |
| with settings: | |
| st.header("⚙️ Settings") | |
| integration_time = st.selectbox("Integration time [ms]", [1, 5, 10]) | |
| analysis_length = st.text_input("Analysis length [ms]", value="500") | |
| intensity_threshold = st.text_input("Intensity threshold [dB]", value=-60) | |
| # "Analyze" button | |
| if st.button("Analyze", use_container_width=True): | |
| data = { | |
| "front_left_up": save_temp_file(audio_file_flu), | |
| "front_right_down": save_temp_file(audio_file_frd), | |
| "back_right_up": save_temp_file(audio_file_bru), | |
| "back_left_down": save_temp_file(audio_file_bld), | |
| "inverse_filter": save_temp_file(audio_file_inverse_filter), | |
| "input_mode": InputMode.LSS, | |
| "channels_per_file": 1, | |
| "frequency_correction": True, | |
| } | |
| analyzer = AmbisonicsImpulseResponseAnalyzer( | |
| int(integration_time), | |
| float(intensity_threshold), | |
| float(analysis_length), | |
| ) | |
| fig = analyzer.analyze(data, show=False) | |
| fig.update_layout(height=1080) | |
| st.plotly_chart(fig, use_container_width=True, height=1080) | |
| # Generar un gráfico genérico con Plotly | |
| fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4], y=[10, 5, 7, 2])) | |
| st.plotly_chart(fig) | |
| if __name__ == "__main__": | |
| run_streamlit_app() | |