Create app.py

app.py

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+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import streamlit as st
+import pickle
+import os
+
+# Load CSV file
+csv_file = "Hydra-Movie-Scrape.csv"
+df = pd.read_csv(csv_file)
+
+# Load the precomputed embeddings
+with open("embeddings.pkl", "rb") as f:
+    doc_embeddings = pickle.load(f)
+
+# Convert the embeddings into a NumPy array (FAISS requires float32)
+embedding_matrix = np.array(doc_embeddings).astype("float32")
+
+# Build a FAISS index for efficient similarity search
+index = faiss.IndexFlatL2(embedding_matrix.shape[1])
+index.add(embedding_matrix)
+
+# Load the SentenceTransformer model for encoding the query
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Function to retrieve the most relevant movies based on a query
+def retrieve(query, top_k=10):
+    query_embedding = model.encode(query)
+    query_vector = np.array(query_embedding).astype("float32")
+    distances, indices = index.search(np.array([query_vector]), top_k)
+    return indices[0]
+
+# Streamlit app layout
+st.title("Movie Dataset RAG Application")
+query = st.text_input("Ask a question about movies:")
+
+if st.button("Submit"):
+    if query:
+        indices = retrieve(query)
+        response = ""
+        for idx in indices:
+            if idx != -1:
+                movie_details = df.iloc[idx]
+                response += f"*Title*: {movie_details['Title']}\n"
+                response += f"*Year*: {movie_details['Year']}\n"
+                response += f"*Director*: {movie_details['Director']}\n"
+                response += f"*Cast*: {movie_details['Cast']}\n"
+                response += f"*Summary*: {movie_details['Summary']}\n\n"
+        
+        if response:
+            st.write("Here are some movies that match your query:")
+            st.markdown(response)
+        else:
+            st.write("No relevant documents found.")
+    else:
+        st.write("Please enter a query.")