streamlit_app.py

import streamlit as st
import requests
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime
import os

# Page config
st.set_page_config(
    page_title="Audio Sentiment Analysis",
    page_icon="🎤",
    layout="wide"
)

# Title
st.title("🎤 Audio Sentiment Analysis Dashboard")
st.markdown("Analyze emotions from audio files with timeline visualization")

# Flask API URL
FLASK_URL = os.getenv("FLASK_URL", "http://localhost:5000")

# Create tabs
tab1, tab2 = st.tabs(["📁 File Analysis", "🎙️ Audio Recording"])

# ============================================
# TAB 1: File Analysis
# ============================================
with tab1:
    st.header("📁 File Analysis")
    st.markdown("Upload a pre-recorded audio file for sentiment analysis")
    
    # File selection option
    file_option = st.radio(
        "Choose audio source:",
        options=["📁 Upload Your File", "🎯 Try Example File"],
        horizontal=True,
        help="Select whether to upload your own file or use the example"
    )
    
    audio_file = None
    file_name = None
    
    # Upload or Example file logic
    if file_option == "📁 Upload Your File":
        uploaded_file = st.file_uploader(
            "Choose an audio file",
            type=["wav", "mp3", "ogg", "flac", "m4a"],
            help="Supported formats: WAV, MP3, OGG, FLAC, M4A"
        )
        if uploaded_file is not None:
            audio_file = uploaded_file
            file_name = uploaded_file.name
            st.success(f"✅ File uploaded: {uploaded_file.name}")
    
    else:  # Example file
        example_path = "input/test.wav"
        if os.path.exists(example_path):
            audio_file = open(example_path, 'rb')
            file_name = "test.wav"
            st.info("📌 Using example audio file: test.wav")
        else:
            st.warning("⚠️ Example file not found in input/ folder")
    
    # Show analyze button
    analyze_btn = st.button("🔍 Analyze Audio", type="primary", width="stretch", disabled=(audio_file is None))
    
    # Initialize session state for results
    if 'analysis_results' not in st.session_state:
        st.session_state.analysis_results = None
    if 'job_id' not in st.session_state:
        st.session_state.job_id = None
    
    # Display audio player and file info if file is selected
    if audio_file is not None:
        # Audio player
        st.subheader("🎵 Audio Preview")
        st.audio(audio_file)
        
        # File info
        with st.expander("📊 File Information"):
            col1, col2, col3 = st.columns(3)
            with col1:
                st.metric("File Name", file_name)
            with col2:
                if hasattr(audio_file, 'size'):
                    st.metric("File Size", f"{audio_file.size / 1024:.2f} KB")
                else:
                    st.metric("File Size", "N/A")
            with col3:
                if hasattr(audio_file, 'type'):
                    st.metric("File Type", audio_file.type)
                else:
                    st.metric("File Type", "WAV")
    
    # Analysis Results Section
    if analyze_btn and audio_file:
        # Upload file to Flask API
        try:
            # Prepare file for upload
            if file_option == "📁 Upload Your File":
                files = {'file': (file_name, audio_file, 'audio/wav')}
            else:
                # For example file, need to reset file pointer
                audio_file.seek(0)
                files = {'file': (file_name, audio_file, 'audio/wav')}
            
            # Upload to Flask
            with st.spinner("📤 Uploading audio file..."):
                upload_response = requests.post(
                    f"{FLASK_URL}/upload",
                    files=files
                )
            
            if upload_response.status_code == 202:
                job_data = upload_response.json()
                job_id = job_data['job_id']
                st.session_state.job_id = job_id
                
                # Poll for status
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                import time
                max_attempts = 60  # 60 attempts = 2 minutes max
                attempt = 0
                
                while attempt < max_attempts:
                    # Check status
                    status_response = requests.get(f"{FLASK_URL}/status/{job_id}")
                    
                    if status_response.status_code == 200:
                        status_data = status_response.json()
                        progress = status_data['progress']
                        message = status_data['message']
                        status = status_data['status']
                        
                        # Update progress
                        progress_bar.progress(progress / 100)
                        status_text.text(f"⚙️ {message} ({progress}%)")
                        
                        # Check if completed
                        if status == "completed":
                            st.session_state.analysis_results = status_data['results']
                            progress_bar.progress(100)
                            status_text.empty()
                            st.success("✅ Analysis Complete!")
                            break
                        
                        elif status == "failed":
                            error_msg = status_data.get('error', 'Unknown error')
                            st.error(f"❌ Processing failed: {error_msg}")
                            progress_bar.empty()
                            status_text.empty()
                            break
                    
                    # Wait before next poll
                    time.sleep(5)
                    attempt += 1
                
                if attempt >= max_attempts:
                    st.error("⏱️ Processing timeout. Please try again.")
                    
            else:
                st.error(f"❌ Upload failed: {upload_response.json().get('error', 'Unknown error')}")
                
        except requests.exceptions.ConnectionError:
            st.error("❌ Could not connect to Flask server. Make sure it's running on port 5000!")
        except Exception as e:
            st.error(f"❌ An error occurred: {str(e)}")
    
    # Display results if available
    if st.session_state.analysis_results:
        
        # Results layout
        st.markdown("---")
        st.subheader("📊 Emotion Analysis Results")
        
        # Get results from session state
        results = st.session_state.analysis_results
        
        # Emotion emoji mapping (supports all emotions)
        emotion_emoji_map = {
            'Happy': '😊',
            'Sad': '😢',
            'Angry': '😡',
            'Neutral': '😐',
            'Fear': '😨',
            'Surprise': '😲',
            'Disgust': '🤢',
            'Calm': '😌'
        }
        
        # Convert timeline to DataFrame
        timeline_data = results['timeline']
        sample_timeline = pd.DataFrame(timeline_data)
        sample_timeline.rename(columns={'time': 'Time (s)'}, inplace=True)
        sample_timeline.rename(columns={'emotion': 'Emotion'}, inplace=True)
        sample_timeline.rename(columns={'confidence': 'Confidence'}, inplace=True)
        
        # Add emoji column
        sample_timeline['Emoji'] = sample_timeline['Emotion'].map(emotion_emoji_map)
        
        # Calculate metrics
        total_duration = results['duration']
        unique_emotions = results['emotions_detected']
        dominant_emotion = results['dominant_emotion']
        dominant_emoji = emotion_emoji_map[dominant_emotion]
        
        # Metrics
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Total Duration", total_duration, help="Audio length")
        with col2:
            st.metric("Emotions Detected", unique_emotions, help="Number of unique emotions")
        with col3:
            st.metric("Dominant Emotion", f"{dominant_emoji} {dominant_emotion}", help="Most frequent emotion")
        
        st.markdown("---")
        
        # Layout: Timeline and Pie Chart
        col1, col2 = st.columns([2, 1])
        
        with col1:
            st.subheader("⏱️ Emotion Timeline")
            
            # Color mapping (supports all emotions)
            colors = {
                'Happy': '#FFD700',
                'Sad': '#4169E1',
                'Angry': '#DC143C',
                'Neutral': '#808080',
                'Fear': '#9370DB',
                'Surprise': '#FF8C00',
                'Disgust': '#32CD32',
                'Calm': '#87CEEB'
            }
            
            # Create bar chart with individual bars (not grouped)
            fig_timeline = go.Figure()
            
            # Add all bars in sequence
            bar_colors = [colors[emotion] for emotion in sample_timeline['Emotion']]
            bar_text = [emotion_emoji_map[emotion] for emotion in sample_timeline['Emotion']]
            
            fig_timeline.add_trace(go.Bar(
                x=sample_timeline['Time (s)'],
                y=sample_timeline['Confidence'],
                marker_color=bar_colors,
                text=bar_text,
                textposition='outside',
                textfont=dict(size=20),
                hovertemplate='<b>%{x}</b><br>Confidence: %{y:.2%}<br><extra></extra>',
                showlegend=False
            ))
            
            fig_timeline.update_layout(
                xaxis_title="Time",
                yaxis_title="Confidence",
                yaxis_range=[0, 1.1],
                height=400,
                hovermode='x'
            )
            
            st.plotly_chart(fig_timeline, width="stretch")
        
        with col2:
            st.subheader("📊 Distribution")
            
            # Pie chart for emotion distribution
            emotion_counts = sample_timeline['Emotion'].value_counts()
            
            fig_pie = go.Figure(data=[go.Pie(
                labels=[f"{emotion_emoji_map[e]} {e}" for e in emotion_counts.index],
                values=emotion_counts.values,
                marker=dict(colors=[colors[e] for e in emotion_counts.index]),
                textinfo='percent+label',
                textfont=dict(size=12),
                hole=0.3
            )])
            
            fig_pie.update_layout(
                height=400,
                showlegend=False
            )
            
            st.plotly_chart(fig_pie, width="stretch")
        
        # Detailed Timeline Table
        st.subheader("📋 Detailed Timeline")
        display_df = sample_timeline[['Time (s)', 'Emoji', 'Emotion', 'Confidence']].copy()
        display_df['Confidence'] = display_df['Confidence'].apply(lambda x: f"{x:.2%}")
        st.dataframe(
            display_df,
            width="stretch",
            hide_index=True
        )

# ============================================
# TAB 2: Audio Input Analysis (Live Recording)
# ============================================
with tab2:
    st.header("🎙️ Audio Recording Analysis")
    st.markdown("Record audio from your microphone for real-time sentiment analysis")
    
    # Initialize session state for Tab 2
    if 'tab2_results' not in st.session_state:
        st.session_state.tab2_results = None
    
    # Audio recorder widget
    audio_data = st.audio_input("Record your audio")
    
    audio_filename = "recorded_audio.wav"
    
    if audio_data:
        st.success("✅ Recording complete! You can now analyze it.")
    
    # Show audio player if available
    if audio_data:
        st.subheader("🎵 Audio Preview")
        st.audio(audio_data)
    
    # Analyze button
    analyze_btn_tab2 = st.button(
        "🔍 Analyze Audio",
        type="primary",
        width="stretch",
        disabled=(audio_data is None),
        key="analyze_tab2"
    )
    
    # Analysis process
    if analyze_btn_tab2 and audio_data:
        try:
            # Prepare file for upload
            if hasattr(audio_data, 'seek'):
                audio_data.seek(0)
            
            files = {'file': (audio_filename, audio_data, 'audio/wav')}
            
            # Upload to Flask
            with st.spinner("📤 Uploading audio..."):
                upload_response = requests.post(
                    f"{FLASK_URL}/upload",
                    files=files
                )
            
            if upload_response.status_code == 202:
                job_data = upload_response.json()
                job_id = job_data['job_id']
                
                # Poll for status
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                import time
                max_attempts = 60
                attempt = 0
                
                while attempt < max_attempts:
                    status_response = requests.get(f"{FLASK_URL}/status/{job_id}")
                    
                    if status_response.status_code == 200:
                        status_data = status_response.json()
                        progress = status_data['progress']
                        message = status_data['message']
                        status = status_data['status']
                        
                        progress_bar.progress(progress / 100)
                        status_text.text(f"⚙️ {message} ({progress}%)")
                        
                        if status == "completed":
                            st.session_state.tab2_results = status_data['results']
                            progress_bar.progress(100)
                            status_text.empty()
                            st.success("✅ Analysis Complete!")
                            break
                        
                        elif status == "failed":
                            error_msg = status_data.get('error', 'Unknown error')
                            st.error(f"❌ Processing failed: {error_msg}")
                            progress_bar.empty()
                            status_text.empty()
                            break
                    
                    time.sleep(5)
                    attempt += 1
                
                if attempt >= max_attempts:
                    st.error("⏱️ Processing timeout. Please try again.")
            else:
                st.error(f"❌ Upload failed: {upload_response.json().get('error', 'Unknown error')}")
                
        except requests.exceptions.ConnectionError:
            st.error("❌ Could not connect to Flask server. Make sure it's running on port 5000!")
        except Exception as e:
            st.error(f"❌ An error occurred: {str(e)}")
    
    # Display results if available
    if st.session_state.tab2_results:
        results = st.session_state.tab2_results
        
        st.markdown("---")
        st.subheader("📊 Emotion Analysis Results")
        
        # Emotion emoji mapping
        emotion_emoji_map = {
            'Happy': '😊',
            'Sad': '😢',
            'Angry': '😡',
            'Neutral': '😐',
            'Fear': '😨',
            'Surprise': '😲',
            'Disgust': '🤢',
            'Calm': '😌'
        }
        
        # Convert timeline to DataFrame
        timeline_data = results['timeline']
        sample_data = pd.DataFrame(timeline_data)
        sample_data.rename(columns={'time': 'Time (s)', 'emotion': 'Emotion', 'confidence': 'Confidence'}, inplace=True)
        
        # Add emoji column
        sample_data['Emoji'] = sample_data['Emotion'].map(emotion_emoji_map)
        
        # Metrics
        total_duration = results['duration']
        unique_emotions = results['emotions_detected']
        dominant_emotion = results['dominant_emotion']
        dominant_emoji = emotion_emoji_map.get(dominant_emotion, '❓')
        
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Audio Duration", total_duration, help="Length of audio")
        with col2:
            st.metric("Emotions Detected", unique_emotions, help="Number of unique emotions")
        with col3:
            st.metric("Dominant Emotion", f"{dominant_emoji} {dominant_emotion}", help="Most frequent emotion")
        
        st.markdown("---")
        
        # Layout: Timeline and Pie Chart
        col1, col2 = st.columns([2, 1])
        
        with col1:
            st.subheader("⏱️ Emotion Timeline")
            
            # Color mapping
            colors = {
                'Happy': '#FFD700',
                'Sad': '#4169E1',
                'Angry': '#DC143C',
                'Neutral': '#808080',
                'Fear': '#9370DB',
                'Surprise': '#FF8C00',
                'Disgust': '#32CD32',
                'Calm': '#87CEEB'
            }
            
            # Create bar chart
            bar_colors = [colors.get(emotion, '#808080') for emotion in sample_data['Emotion']]
            bar_text = [emotion_emoji_map.get(emotion, '❓') for emotion in sample_data['Emotion']]
            
            fig_timeline = go.Figure()
            fig_timeline.add_trace(go.Bar(
                x=sample_data['Time (s)'],
                y=sample_data['Confidence'],
                marker_color=bar_colors,
                text=bar_text,
                textposition='outside',
                textfont=dict(size=20),
                hovertemplate='<b>%{x}</b><br>Confidence: %{y:.2%}<br><extra></extra>',
                showlegend=False
            ))
            
            fig_timeline.update_layout(
                xaxis_title="Time",
                yaxis_title="Confidence",
                yaxis_range=[0, 1.1],
                height=400,
                hovermode='x'
            )
            
            st.plotly_chart(fig_timeline, width="stretch")
        
        with col2:
            st.subheader("📊 Distribution")
            
            # Pie chart
            emotion_counts = sample_data['Emotion'].value_counts()
            
            fig_pie = go.Figure(data=[go.Pie(
                labels=[f"{emotion_emoji_map.get(e, '❓')} {e}" for e in emotion_counts.index],
                values=emotion_counts.values,
                marker=dict(colors=[colors.get(e, '#808080') for e in emotion_counts.index]),
                textinfo='percent+label',
                textfont=dict(size=12),
                hole=0.3
            )])
            
            fig_pie.update_layout(
                height=400,
                showlegend=False
            )
            
            st.plotly_chart(fig_pie, width="stretch")
        
        # Detailed Timeline Table
        st.subheader("📋 Detailed Timeline")
        display_df = sample_data[['Time (s)', 'Emoji', 'Emotion', 'Confidence']].copy()
        display_df['Confidence'] = display_df['Confidence'].apply(lambda x: f"{x:.2%}")
        st.dataframe(
            display_df,
            width="stretch",
            hide_index=True
        )

# Footer
st.markdown("---")
st.caption("🔧 Powered by Flask + Streamlit | Audio Sentiment Analysis")