UI ready with pie and timeline graph

do graphs pie aur timeline with emojies
saara UI tyar hai bas logic karna hai ab

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,7 +0,0 @@
-from fastapi import FastAPI
-
-app = FastAPI()
-
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}

input/README.md

@@ -0,0 +1,5 @@
+# Sample Audio Files
+
+Place your sample audio file here named `sample_audio.wav` or `sample_audio.mp3`
+
+This file will be used as the example file in the application.

input/test.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:876585f42dfcf9dbe504257cd87d45ff29e8c326bb4919c0ce99e1c2877ba343
+size 2385836

main.py

@@ -1,6 +0,0 @@
-def main():
-    print("Hello from audiosentiment!")
-
-
-if __name__ == "__main__":
-    main()

pyproject.toml

@@ -6,6 +6,8 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
     "flask>=3.1.2",
+    "pandas>=2.3.3",
+    "plotly>=6.5.2",
     "requests>=2.32.5",
     "streamlit>=1.54.0",
 ]

requirements.txt

@@ -3,3 +3,5 @@ uvicorn[standard]
 flask
 streamlit
 requests
+pandas
+plotly

streamlit_app.py

@@ -1,38 +1,377 @@
 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"
+)
 
-st.title("Flask API Client 🚀")
-st.write("This app calls the Flask HelloWorld endpoint and displays the response")
+# Title
+st.title("🎤 Audio Sentiment Analysis Dashboard")
+st.markdown("Analyze emotions from audio files with timeline visualization")
 
 # Flask API URL
-import os
-FLASK_URL = os.getenv("FLASK_URL", "http://localhost:5000/helloworld")
+FLASK_URL = os.getenv("FLASK_URL", "http://localhost:5000")
 
-st.markdown("---")
+# Create tabs
+tab1, tab2 = st.tabs(["📁 Test File Analysis", "🎙️ Audio Input Analysis"])
 
-if st.button("Call Flask API"):
-    try:
-        with st.spinner("Calling Flask API..."):
-            response = requests.get(FLASK_URL)
-        
-        if response.status_code == 200:
-            st.success("API call successful! ✅")
-            data = response.json()
-            
-            # Display response in a nice format
-            st.subheader("Response:")
-            st.json(data)
-            
-            # Also display as text
-            st.info(f"Message: {data.get('message', 'N/A')}")
-            st.info(f"Status: {data.get('status', 'N/A')}")
+# ============================================
+# TAB 1: Test File Analysis
+# ============================================
+with tab1:
+    st.header("📁 Test 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.error(f"Error: Received status code {response.status_code}")
+            st.warning("⚠️ Example file not found in input/ folder")
+    
+    # Show analyze button
+    analyze_btn = st.button("🔍 Analyze Audio", type="primary", use_container_width=True, disabled=(audio_file is 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 (placeholder)
+    if analyze_btn and audio_file:
+        with st.spinner("🔄 Analyzing audio... Please wait..."):
+            # Placeholder for Flask API call
+            st.info("⚙️ Processing audio through Flask API...")
+            
+        st.success("✅ Analysis Complete!")
+        
+        # Results layout
+        st.markdown("---")
+        st.subheader("📊 Emotion Analysis Results")
+        
+        # Sample timeline data with emojis
+        emotion_emoji_map = {
+            'Happy': '😊',
+            'Sad': '😢',
+            'Angry': '😡',
+            'Neutral': '😐'
+        }
+        
+        sample_timeline = pd.DataFrame({
+            'Time (s)': ['00:00', '00:05', '00:12', '00:20', '00:30', '00:40'],
+            'Emotion': ['Neutral', 'Happy', 'Sad', 'Happy', 'Angry', 'Neutral'],
+            'Confidence': [0.85, 0.92, 0.78, 0.88, 0.75, 0.90]
+        })
+        
+        # Add emoji column
+        sample_timeline['Emoji'] = sample_timeline['Emotion'].map(emotion_emoji_map)
+        
+        # Calculate metrics
+        total_duration = "00:45"
+        unique_emotions = sample_timeline['Emotion'].nunique()
+        dominant_emotion = sample_timeline['Emotion'].mode()[0]
+        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")
+            
+            # Bar chart with emojis
+            fig_timeline = go.Figure()
+            
+            colors = {
+                'Happy': '#FFD700',
+                'Sad': '#4169E1',
+                'Angry': '#DC143C',
+                'Neutral': '#808080'
+            }
+            
+            for emotion in sample_timeline['Emotion'].unique():
+                emotion_data = sample_timeline[sample_timeline['Emotion'] == emotion]
+                fig_timeline.add_trace(go.Bar(
+                    x=emotion_data['Time (s)'],
+                    y=emotion_data['Confidence'],
+                    name=f"{emotion_emoji_map[emotion]} {emotion}",
+                    marker_color=colors[emotion],
+                    text=[emotion_emoji_map[emotion]] * len(emotion_data),
+                    textposition='outside',
+                    textfont=dict(size=20)
+                ))
+            
+            fig_timeline.update_layout(
+                xaxis_title="Time",
+                yaxis_title="Confidence",
+                yaxis_range=[0, 1.1],
+                barmode='group',
+                height=400,
+                showlegend=True,
+                hovermode='x unified'
+            )
+            
+            st.plotly_chart(fig_timeline, use_container_width=True)
+        
+        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
+            )
             
-    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)}")
+            st.plotly_chart(fig_pie, use_container_width=True)
+        
+        # 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,
+            use_container_width=True,
+            hide_index=True
+        )
+
+# ============================================
+# TAB 2: Audio Input Analysis (Live Recording)
+# ============================================
+with tab2:
+    st.header("🎙️ Live Audio Input Analysis")
+    st.markdown("Record audio in real-time for sentiment analysis")
+    
+    # Recording controls
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        record_btn = st.button("🔴 Start Recording", type="primary", use_container_width=True)
+    with col2:
+        stop_btn = st.button("⏹️ Stop Recording", use_container_width=True)
+    with col3:
+        analyze_record_btn = st.button("🔍 Analyze Recording", use_container_width=True)
+    
+    # Recording status
+    if record_btn:
+        st.warning("🔴 Recording... (This feature will be implemented)")
+    
+    if stop_btn:
+        st.info("⏹️ Recording stopped")
+    
+    # Audio input section
+    st.subheader("🎤 Audio Input Settings")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        sample_rate = st.selectbox(
+            "Sample Rate",
+            options=[16000, 22050, 44100, 48000],
+            index=0,
+            help="Audio sample rate in Hz"
+        )
+    
+    with col2:
+        channels = st.selectbox(
+            "Channels",
+            options=["Mono", "Stereo"],
+            index=0,
+            help="Audio channel configuration"
+        )
+    
+    # Recorded audio preview (placeholder)
+    st.subheader("🎵 Recorded Audio Preview")
+    st.info("📝 No recording available yet. Click 'Start Recording' to begin.")
+    
+    # Analysis results (placeholder)
+    if analyze_record_btn:
+        with st.spinner("🔄 Analyzing recorded audio..."):
+            st.info("⚙️ Processing audio through Flask API...")
+        
+        st.success("✅ Analysis Complete!")
+        
+        # Results layout
+        st.markdown("---")
+        st.subheader("📊 Emotion Analysis Results")
+        
+        # Emotion emoji mapping
+        emotion_emoji_map = {
+            'Happy': '😊',
+            'Sad': '😢',
+            'Angry': '😡',
+            'Neutral': '😐'
+        }
+        
+        # Sample data for recorded audio
+        sample_data = pd.DataFrame({
+            'Time (s)': ['00:00', '00:08', '00:15', '00:22', '00:28'],
+            'Emotion': ['Neutral', 'Happy', 'Neutral', 'Sad', 'Neutral'],
+            'Confidence': [0.88, 0.85, 0.90, 0.72, 0.87]
+        })
+        
+        # Add emoji column
+        sample_data['Emoji'] = sample_data['Emotion'].map(emotion_emoji_map)
+        
+        # Calculate metrics
+        total_duration = "00:30"
+        unique_emotions = sample_data['Emotion'].nunique()
+        dominant_emotion = sample_data['Emotion'].mode()[0]
+        dominant_emoji = emotion_emoji_map[dominant_emotion]
+        
+        # Metrics
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Recording Duration", total_duration, help="Length of recording")
+        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")
+            
+            # Bar chart with emojis
+            fig_timeline = go.Figure()
+            
+            colors = {
+                'Happy': '#FFD700',
+                'Sad': '#4169E1',
+                'Angry': '#DC143C',
+                'Neutral': '#808080'
+            }
+            
+            for emotion in sample_data['Emotion'].unique():
+                emotion_data = sample_data[sample_data['Emotion'] == emotion]
+                fig_timeline.add_trace(go.Bar(
+                    x=emotion_data['Time (s)'],
+                    y=emotion_data['Confidence'],
+                    name=f"{emotion_emoji_map[emotion]} {emotion}",
+                    marker_color=colors[emotion],
+                    text=[emotion_emoji_map[emotion]] * len(emotion_data),
+                    textposition='outside',
+                    textfont=dict(size=20)
+                ))
+            
+            fig_timeline.update_layout(
+                xaxis_title="Time",
+                yaxis_title="Confidence",
+                yaxis_range=[0, 1.1],
+                barmode='group',
+                height=400,
+                showlegend=True,
+                hovermode='x unified'
+            )
+            
+            st.plotly_chart(fig_timeline, use_container_width=True)
+        
+        with col2:
+            st.subheader("📊 Distribution")
+            
+            # Pie chart for emotion distribution
+            emotion_counts = sample_data['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, use_container_width=True)
+        
+        # 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,
+            use_container_width=True,
+            hide_index=True
+        )
 
+# Footer
 st.markdown("---")
-st.caption("Make sure to run Flask app first: `python flask_app.py`")
+st.caption("🔧 Powered by Flask + Streamlit | Audio Sentiment Analysis")