Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,165 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+from streamlit_option_menu import option_menu
+
+# Set page config
+st.set_page_config(
+    page_title="Movie Analytics Dashboard",
+    page_icon="🎬",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Load data
+@st.cache_data
+def load_data():
+    df = pd.read_csv('watch_movies.csv')
+    return df
+
+df = load_data()
+
+# Sidebar
+with st.sidebar:
+    st.title("🎬 Movie Analytics")
+    selected = option_menu(
+        menu_title="Navigation",
+        options=["Overview", "3D Analysis", "Genre Analysis", "Actor Analysis", "Recommendations"],
+        icons=["house", "graph-up-3d", "film", "person", "star"],
+        menu_icon="cast",
+        default_index=0,
+    )
+
+# Main content
+if selected == "Overview":
+    st.title("Movie Analytics Dashboard")
+    
+    # Key metrics
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("Total Movies", len(df))
+    with col2:
+        st.metric("Average Budget", f"${df['budget_usd'].mean():,.0f}")
+    with col3:
+        st.metric("Average User Score", f"{df['user_score'].mean():.1f}")
+    with col4:
+        st.metric("Total Genres", df['genres'].nunique())
+
+    # Budget distribution with animation
+    st.subheader("Budget Distribution Over Time")
+    fig = px.histogram(
+        df,
+        x="budget_usd",
+        animation_frame=pd.to_datetime(df['release_date']).dt.year,
+        nbins=50,
+        color_discrete_sequence=['#636EFA']
+    )
+    fig.update_layout(
+        xaxis_title="Budget (USD)",
+        yaxis_title="Number of Movies",
+        showlegend=False
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+elif selected == "3D Analysis":
+    st.title("3D Movie Analysis")
+    
+    # 3D scatter plot
+    fig = go.Figure(data=[go.Scatter3d(
+        x=df['budget_usd'],
+        y=df['vote_count'],
+        z=df['user_score'],
+        mode='markers',
+        marker=dict(
+            size=5,
+            color=df['user_score'],
+            colorscale='Viridis',
+            opacity=0.8
+        ),
+        text=df['title']
+    )])
+    
+    fig.update_layout(
+        scene=dict(
+            xaxis_title="Budget (USD)",
+            yaxis_title="Vote Count",
+            zaxis_title="User Score"
+        ),
+        title="Budget vs Vote Count vs User Score"
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+elif selected == "Genre Analysis":
+    st.title("Genre Analysis")
+    
+    # Genre distribution
+    genre_counts = df['genres'].value_counts().head(10)
+    fig = px.bar(
+        x=genre_counts.values,
+        y=genre_counts.index,
+        orientation='h',
+        title="Top 10 Genres",
+        labels={'x': 'Number of Movies', 'y': 'Genre'},
+        color=genre_counts.values,
+        color_continuous_scale='Viridis'
+    )
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Genre budget analysis
+    st.subheader("Average Budget by Genre")
+    genre_budget = df.groupby('genres')['budget_usd'].mean().sort_values(ascending=False).head(10)
+    fig = px.bar(
+        x=genre_budget.values,
+        y=genre_budget.index,
+        orientation='h',
+        title="Average Budget by Genre",
+        labels={'x': 'Average Budget (USD)', 'y': 'Genre'},
+        color=genre_budget.values,
+        color_continuous_scale='Viridis'
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+elif selected == "Actor Analysis":
+    st.title("Actor Analysis")
+    
+    # Top actors
+    actor_counts = df['top_billed'].value_counts().head(10)
+    fig = px.bar(
+        x=actor_counts.values,
+        y=actor_counts.index,
+        orientation='h',
+        title="Top 10 Actors by Movie Count",
+        labels={'x': 'Number of Movies', 'y': 'Actor'},
+        color=actor_counts.values,
+        color_continuous_scale='Viridis'
+    )
+    st.plotly_chart(fig, use_container_width=True)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+elif selected == "Recommendations":
+    st.title("Movie Recommendations")
+    
+    # Genre selection
+    selected_genre = st.selectbox("Select a genre", df['genres'].unique())
+    
+    # Filter movies by genre
+    genre_movies = df[df['genres'] == selected_genre]
+    
+    # Sort by user score
+    top_movies = genre_movies.sort_values('user_score', ascending=False).head(5)
+    
+    # Display recommendations
+    for _, movie in top_movies.iterrows():
+        with st.container():
+            col1, col2 = st.columns([1, 3])
+            with col1:
+                st.image(movie['poster_path'], width=150)
+            with col2:
+                st.subheader(movie['title'])
+                st.write(f"User Score: {movie['user_score']:.1f}")
+                st.write(f"Release Date: {movie['release_date']}")
+                st.write(f"Director: {movie['director']}")
+                st.write(f"Top Billed: {movie['top_billed']}")
+            st.markdown("---")