Delete app.py

app.py

@@ -1,71 +0,0 @@
-import streamlit as st
-from PIL import Image
-import requests
-from io import BytesIO
-from sentence_transformers import SentenceTransformer
-import faiss
-import pandas as pd
-
-# import sys
-# import subprocess
-# import streamlit as st
-
-# # Debug: Print Python executable and installed packages
-# st.write(f"Python executable: {sys.executable}")
-# installed_packages = subprocess.run([sys.executable, "-m", "pip", "list"], capture_output=True, text=True).stdout
-# st.text(installed_packages)
-
-model = SentenceTransformer('cointegrated/rubert-tiny2')
-index = faiss.read_index('faiss_index.index')
-data = pd.read_csv('datasetf.csv')
-
-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()] 
-
-def load_image(url):
-    try:
-        response = requests.get(url)
-        img = Image.open(BytesIO(response.content))
-    except Exception:
-        # If an error occurs, load the dummy image
-        img = Image.open("cat.jpg")  # Update the path to your dummy image
-    return img
-
-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}")