added streamlit app

app.py

@@ -0,0 +1,138 @@
+
+import streamlit as st
+import streamlit.components.v1 as components
+import pandas as pd
+import torch
+import requests
+import random
+from io import BytesIO
+from PIL import Image
+from torch_geometric.nn import SAGEConv, to_hetero, Linear
+from dotenv import load_dotenv
+import os
+
+from IPython.display import HTML
+
+import viz_utils
+import model_def
+
+load_dotenv() #load environment variables from .env file
+
+##no clue why this is necessary. But won't see subfolders without it. Just on my laptop. 
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+
+API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+
+# --- LOAD DATA AND MODEL ---
+movies_df = pd.read_csv("./sampled_movie_dataset/movies_metadata.csv")  # Load your movie data
+data = torch.load("./PyGdata.pt")
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model_def.Model(hidden_channels=32).to(device) 
+model.load_state_dict(torch.load("PyGTrainedModelState.pt"))
+model.eval()
+
+# --- STREAMLIT APP ---
+st.title("Movie Recommendation App")
+
+
+
+# --- VISUALIZATIONS ---
+#with open("umap_visualization.html", "r", encoding='utf-8') as f:
+#   umap_html = f.read()
+
+#with open("tsne_visualization.html", "r") as f:
+#    tsne_html = f.read()
+
+#with open("pca_visualization.html", "r") as f:
+#    pca_html = f.read()
+
+tab1, tab2 = st.tabs(["Visualizations", "Recommendations"])
+                      
+
+with torch.no_grad():
+    a = model.encoder(data.x_dict,data.edge_index_dict)
+    user = pd.DataFrame(a['user'].detach().cpu())
+    movie = pd.DataFrame(a['movie'].detach().cpu())
+    embedding_df = pd.concat([user, movie], axis=0)
+
+with tab1:
+    umap_expander = st.expander("UMAP Visualization")
+    with umap_expander:
+        st.subheader('UMAP Visualization')
+        umap_fig = viz_utils.visualize_embeddings_umap(embedding_df)
+        st.plotly_chart(umap_fig)
+        #components.html(umap_html, width=800, height=800)
+
+    tsne_expander = st.expander("TSNE Visualization")
+    with tsne_expander:
+        st.subheader('TSNE Visualization')
+        tsne_fig = viz_utils.visualize_embeddings_tsne(embedding_df)
+        st.plotly_chart(tsne_fig)
+        #components.html(tsne_html, width=800, height=800)
+
+    pca_expander = st.expander("PCA Visualization")
+    with pca_expander:
+        st.subheader('PCA Visualization')
+        pca_fig = viz_utils.visualize_embeddings_pca(embedding_df)
+        st.plotly_chart(pca_fig)
+        #components.html(pca_html, width=800, height=800)
+
+
+
+
+def get_movie_recommendations(model, data, user_id, total_movies):
+    user_row = torch.tensor([user_id] * total_movies).to(device)
+    all_movie_ids = torch.arange(total_movies).to(device)
+    edge_label_index = torch.stack([user_row, all_movie_ids], dim=0)
+
+    pred = model(data.x_dict, data.edge_index_dict, edge_label_index).to('cpu')
+    top_five_indices = pred.topk(5).indices
+
+    recommended_movies = movies_df.iloc[top_five_indices]
+    return recommended_movies
+
+def generate_poster(movie_title):
+    headers = {"Authorization": f"Bearer {API_KEY}"}
+
+    #creates random seed so movie poster changes on refresh even if same title. 
+    seed = random.randint(0, 2**32 - 1)
+    payload = {
+        "inputs": movie_title,
+        # "parameters": {
+        #     "seed": seed
+        # }
+    }
+
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()  # Raise an error if the request fails
+
+        # Display the generated image
+        image = Image.open(BytesIO(response.content))
+        st.image(image, caption=movie_title)
+
+    except requests.exceptions.HTTPError as err:
+        st.error(f"Image generation failed: {err}")
+
+with tab2:
+    user_id = st.number_input("Enter the User ID:", min_value=0)
+    if st.button("Get Recommendations"):
+        st.write("Top 5 Recommendations:")
+        try:
+            total_movies = data['movie'].num_nodes  
+            recommended_movies = get_movie_recommendations(model, data, user_id, total_movies)
+            cols = st.columns(3)  
+
+        
+            for i, row in recommended_movies.iterrows():
+                with cols[i % 3]: 
+                    #st.write(f"{i+1}. {row['title']}") 
+                    try:
+                        image = generate_poster(row['title'])
+                    except requests.exceptions.HTTPError as err:
+                        st.error(f"Image generation failed for {row['title']}: {err}")
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")