changed file system to be more streamlined

app.py

@@ -0,0 +1,39 @@
+import streamlit as st
+import faiss
+import pandas as pd
+from functions.loader import data, index
+from functions.logic import load_image, find_similar_shows
+
+
+
+st.title('TV Show Recommender')
+
+# User input for the show description
+user_description = st.text_area("Describe the TV show you're looking for:")
+
+# Slider for the number of recommendations
+num_recommendations = st.slider('Number of recommendations:', min_value=1, max_value=10, value=5)
+
+# Button to get recommendations
+if st.button('Recommend') and user_description:
+    try:
+        recommended_shows = find_similar_shows(user_description, index, num_recommendations)
+
+        for idx in recommended_shows.index:
+            with st.container():
+                link = data.loc[idx, 'url']
+                poster_url = data.loc[idx, 'poster']
+                title = data.loc[idx, 'title']
+                description = data.loc[idx, 'description']
+
+                img = load_image(poster_url)  # Use the load_image function
+                
+                col1, col2 = st.columns([1, 2])
+                with col1:
+                    st.image(img, caption=title, use_column_width='always')
+                with col2:
+                    st.write(description)
+                    st.markdown(f"[More Info]({link})", unsafe_allow_html=True)
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")

functions/loader.py

@@ -0,0 +1,25 @@
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+import faiss
+import pandas as pd
+
+
+
+@st.cache_data
+def load_model():
+    model = SentenceTransformer('cointegrated/rubert-tiny2')
+    return model
+
+@st.cache_data
+def load_faiss_index():
+    index = faiss.read_index('data/faiss_index.index')
+    return index
+
+@st.cache_data
+def load_dataset():
+    data = pd.read_csv('data/cleaned_data.csv')
+    return data
+
+model = load_model()
+index = load_faiss_index()
+data = load_dataset()

functions/logic.py

@@ -0,0 +1,25 @@
+from PIL import Image
+import requests
+from io import BytesIO
+from functions.loader import model, data
+
+
+
+def load_image(url):
+    try:
+        response = requests.get(url)
+        img = Image.open(BytesIO(response.content))
+    except Exception:
+        img = Image.open("data/cat.jpg")
+    return img
+
+
+def vectorize(descriptions):
+    embeddings = model.encode(descriptions)
+    return embeddings
+
+
+def find_similar_shows(user_description, index, k=5):
+    query_vector = vectorize([user_description])
+    _, indices = index.search(query_vector, k)
+    return data.iloc[indices.flatten()]