converted app to multipage to improve performance

app.py

@@ -1,42 +1,23 @@
-import streamlit as st
+import base64
+import io
+import json
 import os
-import re
+import random
 import tempfile
-from audio_recorder_streamlit import audio_recorder
-import numpy as np
 import time
-import requests
-import io
-import base64
-import random
-import librosa
+
+import boto3
 import fsspec
+import librosa
+import numpy as np
 import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import boto3
-import json
-from plotly.subplots import make_subplots
-from logger import logger
+import requests
+import streamlit as st
+from audio_recorder_streamlit import audio_recorder
 
-fs = fsspec.filesystem(
-        's3',
-        key=os.getenv("AWS_ACCESS_KEY"),
-        secret=os.getenv("AWS_SECRET_KEY")
-    )
-
-s3_client = boto3.client(
-    's3',
-    aws_access_key_id=os.getenv("AWS_ACCESS_KEY"),
-    aws_secret_access_key=os.getenv("AWS_SECRET_KEY")
-    )
-
-SAVE_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('RESULTS_KEY')}"
-ELO_JSON_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('ELO_JSON_PATH')}"
-ELO_CSV_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('ELO_CSV_KEY')}"
-EMAIL_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('EMAILS_KEY')}"
-TEMP_DIR = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('AUDIOS_KEY')}"
-CREATE_TASK_URL = os.getenv("CREATE_TASK_URL")
+from logger import logger
+from utils import fs,s3_client
+from enums import SAVE_PATH, ELO_JSON_PATH, ELO_CSV_PATH, EMAIL_PATH, TEMP_DIR, CREATE_TASK_URL
 
 def create_files():
     if not fs.exists(SAVE_PATH):
@@ -314,324 +295,6 @@ def on_random_click():
 
 result_writer = ResultWriter(SAVE_PATH)
 
-def validate_email(email):
-    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
-    return re.match(pattern, email) is not None
-
-def get_model_abbreviation(model_name):
-    abbrev_map = {
-        'Ori Apex': 'Ori Apex',
-        'Ori Apex XT': 'Ori Apex XT',
-        'deepgram': 'DG',
-        'Ori Swift': 'Ori Swift',
-        'Ori Prime': 'Ori Prime',
-        'azure' : 'Azure'
-    }
-    return abbrev_map.get(model_name, model_name)
-
-
-def calculate_metrics(df):
-    models = ['Ori Apex', 'Ori Apex XT', 'deepgram', 'Ori Swift', 'Ori Prime', 'azure']
-    metrics = {}
-
-    for model in models:
-        appearances = df[f'{model}_appearance'].sum()
-        wins = df[f'{model}_score'].sum()
-        durations = df[df[f'{model}_appearance'] == 1][f'{model}_duration']
-
-        if appearances > 0:
-            win_rate = (wins / appearances) * 100
-            avg_duration = durations.mean()
-            duration_std = durations.std()
-        else:
-            win_rate = 0
-            avg_duration = 0
-            duration_std = 0
-
-        metrics[model] = {
-            'appearances': appearances,
-            'wins': wins,
-            'win_rate': win_rate,
-            'avg_response_time': avg_duration,
-            'response_time_std': duration_std
-        }
-
-    return metrics
-
-def create_win_rate_chart(metrics):
-    models = list(metrics.keys())
-    win_rates = [metrics[model]['win_rate'] for model in models]
-
-    fig = go.Figure(data=[
-        go.Bar(
-            x=[get_model_abbreviation(model) for model in models],
-            y=win_rates,
-            text=[f'{rate:.1f}%' for rate in win_rates],
-            textposition='auto',
-            hovertext=models
-        )
-    ])
-
-    fig.update_layout(
-        title='Win Rate by Model',
-        xaxis_title='Model',
-        yaxis_title='Win Rate (%)',
-        yaxis_range=[0, 100]
-    )
-
-    return fig
-
-def create_appearance_chart(metrics):
-    models = list(metrics.keys())
-    appearances = [metrics[model]['appearances'] for model in models]
-
-    fig = px.pie(
-        values=appearances,
-        names=[get_model_abbreviation(model) for model in models],
-        title='Model Appearances Distribution',
-        hover_data=[models]
-    )
-
-    return fig
-
-def create_head_to_head_matrix(df):
-    models = ['Ori Apex', 'Ori Apex XT', 'deepgram', 'Ori Swift', 'Ori Prime', 'azure']
-    matrix = np.zeros((len(models), len(models)))
-
-    for i, model1 in enumerate(models):
-        for j, model2 in enumerate(models):
-            if i != j:
-                matches = df[
-                    (df[f'{model1}_appearance'] == 1) &
-                    (df[f'{model2}_appearance'] == 1)
-                ]
-                if len(matches) > 0:
-                    win_rate = (matches[f'{model1}_score'].sum() / len(matches)) * 100
-                    matrix[i][j] = win_rate
-
-    fig = go.Figure(data=go.Heatmap(
-        z=matrix,
-        x=[get_model_abbreviation(model) for model in models],
-        y=[get_model_abbreviation(model) for model in models],
-        text=[[f'{val:.1f}%' if val > 0 else '' for val in row] for row in matrix],
-        texttemplate='%{text}',
-        colorscale='RdYlBu',
-        zmin=0,
-        zmax=100
-    ))
-
-    fig.update_layout(
-        title='Head-to-Head Win Rates',
-        xaxis_title='Opponent Model',
-        yaxis_title='Model'
-    )
-
-    return fig
-
-def create_elo_chart(df):
-    fig = make_subplots(rows=1, cols=1,
-                    subplot_titles=('ELO Rating Progression'),
-                    row_heights=[0.7])
-
-    for column in df.columns:
-        fig.add_trace(
-            go.Scatter(
-                x=list(range(len(df))),
-                y=df[column],
-                name=column,
-                mode='lines+markers'
-            ),
-            row=1, col=1
-        )
-
-    fig.update_layout(
-        title='Model ELO Ratings Analysis',
-        showlegend=True,
-        hovermode='x unified'
-    )
-
-    fig.update_xaxes(title_text='Match Number', row=1, col=1)
-    fig.update_xaxes(title_text='Models', row=2, col=1)
-
-    return fig
-
-def create_metric_container(label, value, full_name=None):
-    container = st.container()
-    with container:
-        st.markdown(f"**{label}**")
-        if full_name:
-            st.markdown(f"<h3 style='margin-top: 0;'>{value}</h3>", unsafe_allow_html=True)
-            st.caption(f"Full name: {full_name}")
-        else:
-            st.markdown(f"<h3 style='margin-top: 0;'>{value}</h3>", unsafe_allow_html=True)
-
-def on_refresh_click():
-    with st.spinner("Refreshing data... please wait"):
-        with fs.open(SAVE_PATH, 'rb') as f:
-            st.session_state.df = pd.read_csv(f)
-
-        try:
-            with fs.open(ELO_JSON_PATH,'r') as f:
-                st.session_state.elo_json = json.load(f)
-        except Exception as e:
-            logger.error("Error while reading elo json file %s",e)
-            st.session_state.elo_json = None
-
-        try:
-            with fs.open(ELO_CSV_PATH,'rb') as f:
-                st.session_state.elo_df = pd.read_csv(f)
-        except Exception as e:
-            logger.error("Error while reading elo csv file %s",e)
-            st.session_state.elo_df = None
-
-def dashboard():
-    st.title('Model Arena Scoreboard')
-
-    if "df" not in st.session_state:
-        with fs.open(SAVE_PATH, 'rb') as f:
-            st.session_state.df = pd.read_csv(f)
-    if "elo_json" not in st.session_state:
-        with fs.open(ELO_JSON_PATH,'r') as f:
-            elo_json = json.load(f)
-            st.session_state.elo_json = elo_json
-    if "elo_df" not in st.session_state:
-        with fs.open(ELO_CSV_PATH,'rb') as f:
-            elo_df = pd.read_csv(f)
-            st.session_state.elo_df = elo_df
-
-    st.button("Refresh",on_click=on_refresh_click)
-
-    if len(st.session_state.df) != 0:
-        metrics = calculate_metrics(st.session_state.df)
-
-        MODEL_DESCRIPTIONS = {
-            "Ori Prime": "Foundational, large, and stable.",
-            "Ori Swift": "Lighter and faster than Ori Prime.",
-            "Ori Apex": "The top-performing model, fast and stable.",
-            "Ori Apex XT": "Enhanced with more training, though slightly less stable than Ori Apex.",
-            "DG" : "Deepgram Nova-2 API",
-            "Azure" : "Azure Speech Services API"
-        }
-
-        st.header('Model Descriptions')
-
-        cols = st.columns(2)
-        for idx, (model, description) in enumerate(MODEL_DESCRIPTIONS.items()):
-            with cols[idx % 2]:
-                st.markdown(f"""
-                    <div style='padding: 1rem; border: 1px solid #e1e4e8; border-radius: 6px; margin-bottom: 1rem;'>
-                        <h3 style='margin: 0; margin-bottom: 0.5rem;'>{model}</h3>
-                        <p style='margin: 0; color: #6e7681;'>{description}</p>
-                    </div>
-                    """, unsafe_allow_html=True)
-
-        st.header('Overall Performance')
-
-        col1, col2, col3= st.columns(3)
-
-        with col1:
-            create_metric_container("Total Matches", len(st.session_state.df))
-
-        # best_model = max(metrics.items(), key=lambda x: x[1]['win_rate'])[0]
-        best_model = max(st.session_state.elo_json.items(), key=lambda x: x[1])[0] if st.session_state.elo_json else max(metrics.items(), key=lambda x: x[1]['win_rate'])[0]
-        with col2:
-            create_metric_container(
-                "Best Model",
-                get_model_abbreviation(best_model),
-                full_name=best_model
-            )
-
-        most_appearances = max(metrics.items(), key=lambda x: x[1]['appearances'])[0]
-        with col3:
-            create_metric_container(
-                "Most Used",
-                get_model_abbreviation(most_appearances),
-                full_name=most_appearances
-            )
-
-        metrics_df = pd.DataFrame.from_dict(metrics, orient='index')
-        metrics_df['win_rate'] = metrics_df['win_rate'].round(2)
-        metrics_df.drop(["avg_response_time","response_time_std"],axis=1,inplace=True)
-        metrics_df.index = [get_model_abbreviation(model) for model in metrics_df.index]
-        st.dataframe(metrics_df,use_container_width=True)
-
-        st.header('Win Rates')
-        win_rate_chart = create_win_rate_chart(metrics)
-        st.plotly_chart(win_rate_chart, use_container_width=True)
-
-        st.header('Appearance Distribution')
-        appearance_chart = create_appearance_chart(metrics)
-        st.plotly_chart(appearance_chart, use_container_width=True)
-
-        if st.session_state.elo_json is not None and st.session_state.elo_df is not None:
-            st.header('Elo Ratings')
-            st.dataframe(pd.DataFrame(st.session_state.elo_json,index=[0]),use_container_width=True)
-            elo_progression_chart = create_elo_chart(st.session_state.elo_df)
-            st.plotly_chart(elo_progression_chart, use_container_width=True)
-
-        st.header('Head-to-Head Analysis')
-        matrix_chart = create_head_to_head_matrix(st.session_state.df)
-        st.plotly_chart(matrix_chart, use_container_width=True)
-
-        st.header('Full Dataframe')
-        st.dataframe(st.session_state.df.drop(['path','Ori Apex_duration', 'Ori Apex XT_duration', 'deepgram_duration', 'Ori Swift_duration', 'Ori Prime_duration','azure_duration','email'],axis=1),use_container_width=True)
-    else:
-        st.write("No Data to show")
-
-def about():
-    st.title("About")
-
-    st.markdown(
-    """
-    # Ori Speech-To-Text Arena
-    """
-    )
-
-    st.markdown(
-        """## Arena
-        """
-        )
-
-    st.markdown(
-    """
-    * The Arena allows a user to record their audios, in which speech will be recognized by two randomly selected models. After listening to the audio, and evaluating the output from both the models, the user can vote on which transcription they prefer. Due to the risks of human bias and abuse, model names are revealed only after a vote is submitted."""
-    )
-
-    st.markdown(
-        "## Scoreboard"
-    )
-
-    st.markdown(
-        """ * The Scoreboard shows the performance of the models in the Arena. The user can see the overall performance of the models, the model with the highest win rate, and the model with the most appearances. The user can also see the win rates of each model, as well as the appearance distribution of each model."""
-    )
-
-    st.markdown(
-        "## Contact Us"
-    )
-
-    st.markdown(
-        "To inquire about our speech-to-text models and APIs, you can submit your email using the form below."
-    )
-
-    with st.form("login_form"):
-        st.subheader("Please Enter you Email")
-
-        email = st.text_input("Email")
-
-        submit_button = st.form_submit_button("Submit")
-
-        if submit_button:
-            if not email:
-                st.error("Please fill in all fields")
-            else:
-                if not validate_email(email):
-                    st.error("Please enter a valid email address")
-                else:
-                    st.session_state.logged_in = True
-                    st.session_state.user_email = email
-                    write_email(st.session_state.user_email)
-                    st.success("Thanks for submitting your email, our team will be in touch with you shortly!")
-
 def main():
 
     st.title("⚔️ Ori Speech-To-Text Arena ⚔️")
@@ -659,123 +322,89 @@ def main():
     if "user_email" not in st.session_state:
         st.session_state.user_email = ""
 
-    if 'logged_in' not in st.session_state:
-        st.session_state.logged_in = False
-
-    arena, scoreboard,about_tab = st.tabs(["Arena", "Scoreboard","About"])
-
-    with arena:
-        INSTR = """
-        ## Instructions:
-        * Record audio to recognise speech (or press 🎲 for random Audio).
-        * Click on transcribe audio button to commence the transcription process.
-        * Read the two options one after the other while listening to the audio.
-        * Vote on which transcript you prefer.
-        * Note:
-            * Model names are revealed after the vote is cast.
-            * Currently only Indian Hindi language is supported, and
-                the results will be in Hinglish (Hindi in Latin script)
-            * Random audios are only in hindi
-            * It may take up to 30 seconds for speech recognition in some cases.
-        """.strip()
-
-        st.markdown(INSTR)
-
-        col1, col2 = st.columns([1, 1])
-
-        with col1:
-            st.markdown("### Record Audio")
-            with st.container():
-                audio_bytes = audio_recorder(
-                    text="🎙️ Click to Record",
-                    pause_threshold=3,
-                    icon_size="2x",
-                    key="audio_recorder",
-                    sample_rate=16_000
-                )
-            if audio_bytes and audio_bytes != st.session_state.get('last_recorded_audio'):
-                reset_state()
-                st.session_state.last_recorded_audio = audio_bytes
-                st.session_state.audio = {"data":audio_bytes,"format":"audio/wav"}
-                st.session_state.current_audio_type = "recorded"
-                st.session_state.has_audio = True
-                with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
-                    tmp_file.write(audio_bytes)
-                    os.makedirs(TEMP_DIR, exist_ok=True)
-                    s3_client.put_object(Bucket=os.getenv('AWS_BUCKET_NAME'), Key=f"{os.getenv('AUDIOS_KEY')}/{tmp_file.name.split('/')[-1]}", Body=audio_bytes)
-                st.session_state.audio_path = tmp_file.name
-                st.session_state.option_selected = None
-
-        with col2:
-            st.markdown("### Random Audio Example")
-            with st.container():
-                st.button("🎲 Random Audio",on_click=on_random_click)
-
-        if st.session_state.has_audio:
-            st.audio(**st.session_state.audio)
+    INSTR = """
+    ## Instructions:
+    * Record audio to recognise speech (or press 🎲 for random Audio).
+    * Click on transcribe audio button to commence the transcription process.
+    * Read the two options one after the other while listening to the audio.
+    * Vote on which transcript you prefer.
+    * Note:
+        * Model names are revealed after the vote is cast.
+        * Currently only Indian Hindi language is supported, and
+            the results will be in Hinglish (Hindi in Latin script)
+        * Random audios are only in hindi
+        * It may take up to 30 seconds for speech recognition in some cases.
+    """.strip()
+
+    st.markdown(INSTR)
+
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.markdown("### Record Audio")
+        with st.container():
+            audio_bytes = audio_recorder(
+                text="🎙️ Click to Record",
+                pause_threshold=3,
+                icon_size="2x",
+                key="audio_recorder",
+                sample_rate=16_000
+            )
+        if audio_bytes and audio_bytes != st.session_state.get('last_recorded_audio'):
+            reset_state()
+            st.session_state.last_recorded_audio = audio_bytes
+            st.session_state.audio = {"data":audio_bytes,"format":"audio/wav"}
+            st.session_state.current_audio_type = "recorded"
+            st.session_state.has_audio = True
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+                tmp_file.write(audio_bytes)
+                os.makedirs(TEMP_DIR, exist_ok=True)
+                # s3_client.put_object(Bucket=os.getenv('AWS_BUCKET_NAME'), Key=f"{os.getenv('AUDIOS_KEY')}/{tmp_file.name.split('/')[-1]}", Body=audio_bytes)
+            st.session_state.audio_path = tmp_file.name
+            st.session_state.option_selected = None
+
+    with col2:
+        st.markdown("### Random Audio Example")
+        with st.container():
+            st.button("🎲 Random Audio",on_click=on_random_click)
 
+    if st.session_state.has_audio:
+        st.audio(**st.session_state.audio)
 
-        with st.container():
-            st.button("📝 Transcribe Audio",on_click=on_click_transcribe,use_container_width=True)
 
-        text_containers = st.columns([1, 1])
-        name_containers = st.columns([1, 1])
+    with st.container():
+        st.button("📝 Transcribe Audio",on_click=on_click_transcribe,use_container_width=True)
 
-        with text_containers[0]:
-            st.text_area("Option 1", value=st.session_state.option_1, height=300)
+    text_containers = st.columns([1, 1])
+    name_containers = st.columns([1, 1])
 
-        with text_containers[1]:
-            st.text_area("Option 2", value=st.session_state.option_2, height=300)
+    with text_containers[0]:
+        st.text_area("Option 1", value=st.session_state.option_1, height=300)
 
-        with name_containers[0]:
-            if st.session_state.option_1_model_name_state:
-                st.markdown(f"<div style='text-align: center'>{st.session_state.option_1_model_name_state}</div>", unsafe_allow_html=True)
+    with text_containers[1]:
+        st.text_area("Option 2", value=st.session_state.option_2, height=300)
 
-        with name_containers[1]:
-            if st.session_state.option_2_model_name_state:
-                st.markdown(f"<div style='text-align: center'>{st.session_state.option_2_model_name_state}</div>", unsafe_allow_html=True)
+    with name_containers[0]:
+        if st.session_state.option_1_model_name_state:
+            st.markdown(f"<div style='text-align: center'>{st.session_state.option_1_model_name_state}</div>", unsafe_allow_html=True)
 
-        c1, c2, c3, c4 = st.columns(4)
+    with name_containers[1]:
+        if st.session_state.option_2_model_name_state:
+            st.markdown(f"<div style='text-align: center'>{st.session_state.option_2_model_name_state}</div>", unsafe_allow_html=True)
 
-        with c1:
-            st.button("Prefer Option 1",on_click=on_option_1_click)
+    c1, c2, c3, c4 = st.columns(4)
 
-        with c2:
-            st.button("Prefer Option 2",on_click=on_option_2_click)
+    with c1:
+        st.button("Prefer Option 1",on_click=on_option_1_click)
 
-        with c3:
-            st.button("Prefer Both",on_click=on_option_both_click)
+    with c2:
+        st.button("Prefer Option 2",on_click=on_option_2_click)
 
-        with c4:
-            st.button("Prefer None",on_click=on_option_none_click)
+    with c3:
+        st.button("Prefer Both",on_click=on_option_both_click)
 
-    with scoreboard:
-        if st.session_state.logged_in or os.getenv("IS_TEST"):
-            dashboard()
-        else:
-            with st.form("contact_us_form"):
-                st.subheader("Please Enter you Email")
-
-                email = st.text_input("Email")
-
-                submit_button = st.form_submit_button("Submit")
-
-                if submit_button:
-                    if not email:
-                        st.error("Please fill in all fields")
-                    else:
-                        if not validate_email(email):
-                            st.error("Please enter a valid email address")
-                        else:
-                            st.session_state.logged_in = True
-                            st.session_state.user_email = email
-                            write_email(st.session_state.user_email)
-                            st.success("Thanks for submitting your email")
-            if st.session_state.logged_in:
-                dashboard()
-
-    with about_tab:
-        about()
+    with c4:
+        st.button("Prefer None",on_click=on_option_none_click)
 
 create_files()
 main()

enums.py

@@ -0,0 +1,8 @@
+import os
+
+SAVE_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('RESULTS_KEY')}"
+ELO_JSON_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('ELO_JSON_PATH')}"
+ELO_CSV_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('ELO_CSV_KEY')}"
+EMAIL_PATH = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('EMAILS_KEY')}"
+TEMP_DIR = f"s3://{os.getenv('AWS_BUCKET_NAME')}/{os.getenv('AUDIOS_KEY')}"
+CREATE_TASK_URL = os.getenv("CREATE_TASK_URL")

pages/about.py

@@ -0,0 +1,57 @@
+import streamlit as st
+from pages.scoreboard import validate_email
+from pages.scoreboard import write_email
+
+
+st.title("About")
+
+st.markdown(
+"""
+# Ori Speech-To-Text Arena
+"""
+)
+
+st.markdown(
+    """## Arena
+    """
+    )
+
+st.markdown(
+"""
+* The Arena allows a user to record their audios, in which speech will be recognized by two randomly selected models. After listening to the audio, and evaluating the output from both the models, the user can vote on which transcription they prefer. Due to the risks of human bias and abuse, model names are revealed only after a vote is submitted."""
+)
+
+st.markdown(
+    "## Scoreboard"
+)
+
+st.markdown(
+    """ * The Scoreboard shows the performance of the models in the Arena. The user can see the overall performance of the models, the model with the highest win rate, and the model with the most appearances. The user can also see the win rates of each model, as well as the appearance distribution of each model."""
+)
+
+st.markdown(
+    "## Contact Us"
+)
+
+st.markdown(
+    "To inquire about our speech-to-text models and APIs, you can submit your email using the form below."
+)
+
+with st.form("login_form"):
+    st.subheader("Please Enter you Email")
+
+    email = st.text_input("Email")
+
+    submit_button = st.form_submit_button("Submit")
+
+    if submit_button:
+        if not email:
+            st.error("Please fill in all fields")
+        else:
+            if not validate_email(email):
+                st.error("Please enter a valid email address")
+            else:
+                st.session_state.logged_in = True
+                st.session_state.user_email = email
+                write_email(st.session_state.user_email)
+                st.success("Thanks for submitting your email, our team will be in touch with you shortly!")

pages/scoreboard.py

@@ -0,0 +1,312 @@
+import streamlit as st
+import os
+import numpy as np
+import pandas as pd
+from logger import logger
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import json
+from utils import fs,validate_email
+from enums import SAVE_PATH, ELO_JSON_PATH, ELO_CSV_PATH, EMAIL_PATH
+
+
+def write_email(email):
+    if fs.exists(EMAIL_PATH):
+        with fs.open(EMAIL_PATH, 'rb') as f:
+            existing_content = f.read().decode('utf-8')
+    else:
+        existing_content = ''
+
+    new_content = existing_content + email + '\n'
+
+    with fs.open(EMAIL_PATH, 'wb') as f:
+        f.write(new_content.encode('utf-8'))
+
+def get_model_abbreviation(model_name):
+    abbrev_map = {
+        'Ori Apex': 'Ori Apex',
+        'Ori Apex XT': 'Ori Apex XT',
+        'deepgram': 'DG',
+        'Ori Swift': 'Ori Swift',
+        'Ori Prime': 'Ori Prime',
+        'azure' : 'Azure'
+    }
+    return abbrev_map.get(model_name, model_name)
+
+
+def calculate_metrics(df):
+    models = ['Ori Apex', 'Ori Apex XT', 'deepgram', 'Ori Swift', 'Ori Prime', 'azure']
+    metrics = {}
+
+    for model in models:
+        appearances = df[f'{model}_appearance'].sum()
+        wins = df[f'{model}_score'].sum()
+        durations = df[df[f'{model}_appearance'] == 1][f'{model}_duration']
+
+        if appearances > 0:
+            win_rate = (wins / appearances) * 100
+            avg_duration = durations.mean()
+            duration_std = durations.std()
+        else:
+            win_rate = 0
+            avg_duration = 0
+            duration_std = 0
+
+        metrics[model] = {
+            'appearances': appearances,
+            'wins': wins,
+            'win_rate': win_rate,
+            'avg_response_time': avg_duration,
+            'response_time_std': duration_std
+        }
+
+    return metrics
+
+def create_win_rate_chart(metrics):
+    models = list(metrics.keys())
+    win_rates = [metrics[model]['win_rate'] for model in models]
+
+    fig = go.Figure(data=[
+        go.Bar(
+            x=[get_model_abbreviation(model) for model in models],
+            y=win_rates,
+            text=[f'{rate:.1f}%' for rate in win_rates],
+            textposition='auto',
+            hovertext=models
+        )
+    ])
+
+    fig.update_layout(
+        title='Win Rate by Model',
+        xaxis_title='Model',
+        yaxis_title='Win Rate (%)',
+        yaxis_range=[0, 100]
+    )
+
+    return fig
+
+def create_appearance_chart(metrics):
+    models = list(metrics.keys())
+    appearances = [metrics[model]['appearances'] for model in models]
+
+    fig = px.pie(
+        values=appearances,
+        names=[get_model_abbreviation(model) for model in models],
+        title='Model Appearances Distribution',
+        hover_data=[models]
+    )
+
+    return fig
+
+def create_head_to_head_matrix(df):
+    models = ['Ori Apex', 'Ori Apex XT', 'deepgram', 'Ori Swift', 'Ori Prime', 'azure']
+    matrix = np.zeros((len(models), len(models)))
+
+    for i, model1 in enumerate(models):
+        for j, model2 in enumerate(models):
+            if i != j:
+                matches = df[
+                    (df[f'{model1}_appearance'] == 1) &
+                    (df[f'{model2}_appearance'] == 1)
+                ]
+                if len(matches) > 0:
+                    win_rate = (matches[f'{model1}_score'].sum() / len(matches)) * 100
+                    matrix[i][j] = win_rate
+
+    fig = go.Figure(data=go.Heatmap(
+        z=matrix,
+        x=[get_model_abbreviation(model) for model in models],
+        y=[get_model_abbreviation(model) for model in models],
+        text=[[f'{val:.1f}%' if val > 0 else '' for val in row] for row in matrix],
+        texttemplate='%{text}',
+        colorscale='RdYlBu',
+        zmin=0,
+        zmax=100
+    ))
+
+    fig.update_layout(
+        title='Head-to-Head Win Rates',
+        xaxis_title='Opponent Model',
+        yaxis_title='Model'
+    )
+
+    return fig
+
+def create_elo_chart(df):
+    fig = make_subplots(rows=1, cols=1,
+                    subplot_titles=('ELO Rating Progression'),
+                    row_heights=[0.7])
+
+    for column in df.columns:
+        fig.add_trace(
+            go.Scatter(
+                x=list(range(len(df))),
+                y=df[column],
+                name=column,
+                mode='lines+markers'
+            ),
+            row=1, col=1
+        )
+
+    fig.update_layout(
+        title='Model ELO Ratings Analysis',
+        showlegend=True,
+        hovermode='x unified'
+    )
+
+    fig.update_xaxes(title_text='Match Number', row=1, col=1)
+    fig.update_xaxes(title_text='Models', row=2, col=1)
+
+    return fig
+
+def create_metric_container(label, value, full_name=None):
+    container = st.container()
+    with container:
+        st.markdown(f"**{label}**")
+        if full_name:
+            st.markdown(f"<h3 style='margin-top: 0;'>{value}</h3>", unsafe_allow_html=True)
+            st.caption(f"Full name: {full_name}")
+        else:
+            st.markdown(f"<h3 style='margin-top: 0;'>{value}</h3>", unsafe_allow_html=True)
+
+def on_refresh_click():
+    with st.spinner("Refreshing data... please wait"):
+        with fs.open(SAVE_PATH, 'rb') as f:
+            st.session_state.df = pd.read_csv(f)
+
+        try:
+            with fs.open(ELO_JSON_PATH,'r') as f:
+                st.session_state.elo_json = json.load(f)
+        except Exception as e:
+            logger.error("Error while reading elo json file %s",e)
+            st.session_state.elo_json = None
+
+        try:
+            with fs.open(ELO_CSV_PATH,'rb') as f:
+                st.session_state.elo_df = pd.read_csv(f)
+        except Exception as e:
+            logger.error("Error while reading elo csv file %s",e)
+            st.session_state.elo_df = None
+
+def dashboard():
+    st.title('Model Arena Scoreboard')
+
+    if "df" not in st.session_state:
+        with fs.open(SAVE_PATH, 'rb') as f:
+            st.session_state.df = pd.read_csv(f)
+    if "elo_json" not in st.session_state:
+        with fs.open(ELO_JSON_PATH,'r') as f:
+            elo_json = json.load(f)
+            st.session_state.elo_json = elo_json
+    if "elo_df" not in st.session_state:
+        with fs.open(ELO_CSV_PATH,'rb') as f:
+            elo_df = pd.read_csv(f)
+            st.session_state.elo_df = elo_df
+
+    st.button("Refresh",on_click=on_refresh_click)
+
+    if len(st.session_state.df) != 0:
+        metrics = calculate_metrics(st.session_state.df)
+
+        MODEL_DESCRIPTIONS = {
+            "Ori Prime": "Foundational, large, and stable.",
+            "Ori Swift": "Lighter and faster than Ori Prime.",
+            "Ori Apex": "The top-performing model, fast and stable.",
+            "Ori Apex XT": "Enhanced with more training, though slightly less stable than Ori Apex.",
+            "DG" : "Deepgram Nova-2 API",
+            "Azure" : "Azure Speech Services API"
+        }
+
+        st.header('Model Descriptions')
+
+        cols = st.columns(2)
+        for idx, (model, description) in enumerate(MODEL_DESCRIPTIONS.items()):
+            with cols[idx % 2]:
+                st.markdown(f"""
+                    <div style='padding: 1rem; border: 1px solid #e1e4e8; border-radius: 6px; margin-bottom: 1rem;'>
+                        <h3 style='margin: 0; margin-bottom: 0.5rem;'>{model}</h3>
+                        <p style='margin: 0; color: #6e7681;'>{description}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+        st.header('Overall Performance')
+
+        col1, col2, col3= st.columns(3)
+
+        with col1:
+            create_metric_container("Total Matches", len(st.session_state.df))
+
+        # best_model = max(metrics.items(), key=lambda x: x[1]['win_rate'])[0]
+        best_model = max(st.session_state.elo_json.items(), key=lambda x: x[1])[0] if st.session_state.elo_json else max(metrics.items(), key=lambda x: x[1]['win_rate'])[0]
+        with col2:
+            create_metric_container(
+                "Best Model",
+                get_model_abbreviation(best_model),
+                full_name=best_model
+            )
+
+        most_appearances = max(metrics.items(), key=lambda x: x[1]['appearances'])[0]
+        with col3:
+            create_metric_container(
+                "Most Used",
+                get_model_abbreviation(most_appearances),
+                full_name=most_appearances
+            )
+
+        metrics_df = pd.DataFrame.from_dict(metrics, orient='index')
+        metrics_df['win_rate'] = metrics_df['win_rate'].round(2)
+        metrics_df.drop(["avg_response_time","response_time_std"],axis=1,inplace=True)
+        metrics_df.index = [get_model_abbreviation(model) for model in metrics_df.index]
+        st.dataframe(metrics_df,use_container_width=True)
+
+        st.header('Win Rates')
+        win_rate_chart = create_win_rate_chart(metrics)
+        st.plotly_chart(win_rate_chart, use_container_width=True)
+
+        st.header('Appearance Distribution')
+        appearance_chart = create_appearance_chart(metrics)
+        st.plotly_chart(appearance_chart, use_container_width=True)
+
+        if st.session_state.elo_json is not None and st.session_state.elo_df is not None:
+            st.header('Elo Ratings')
+            st.dataframe(pd.DataFrame(st.session_state.elo_json,index=[0]),use_container_width=True)
+            elo_progression_chart = create_elo_chart(st.session_state.elo_df)
+            st.plotly_chart(elo_progression_chart, use_container_width=True)
+
+        st.header('Head-to-Head Analysis')
+        matrix_chart = create_head_to_head_matrix(st.session_state.df)
+        st.plotly_chart(matrix_chart, use_container_width=True)
+
+        st.header('Full Dataframe')
+        st.dataframe(st.session_state.df.drop(['path','Ori Apex_duration', 'Ori Apex XT_duration', 'deepgram_duration', 'Ori Swift_duration', 'Ori Prime_duration','azure_duration','email'],axis=1),use_container_width=True)
+    else:
+        st.write("No Data to show")
+
+if __name__ == "__main__":
+    if 'logged_in' not in st.session_state:
+        st.session_state.logged_in = False
+
+    if st.session_state.logged_in or os.getenv("IS_TEST"):
+        dashboard()
+    else:
+        with st.form("contact_us_form"):
+            st.subheader("Please Enter you Email")
+
+            email = st.text_input("Email")
+
+            submit_button = st.form_submit_button("Submit")
+
+            if submit_button:
+                if not email:
+                    st.error("Please fill in all fields")
+                else:
+                    if not validate_email(email):
+                        st.error("Please enter a valid email address")
+                    else:
+                        st.session_state.logged_in = True
+                        st.session_state.user_email = email
+                        write_email(st.session_state.user_email)
+                        st.success("Thanks for submitting your email")
+    if st.session_state.logged_in:
+        dashboard()

utils.py

@@ -0,0 +1,20 @@
+import fsspec
+import boto3
+import os
+import re
+
+fs = fsspec.filesystem(
+        's3',
+        key=os.getenv("AWS_ACCESS_KEY"),
+        secret=os.getenv("AWS_SECRET_KEY")
+    )
+
+s3_client = boto3.client(
+    's3',
+    aws_access_key_id=os.getenv("AWS_ACCESS_KEY"),
+    aws_secret_access_key=os.getenv("AWS_SECRET_KEY")
+    )
+
+def validate_email(email):
+    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
+    return re.match(pattern, email) is not None