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import streamlit as st
import traceback
from groq import Groq
from langchain_groq import ChatGroq
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Pinecone as PineconeVectorStore
from pinecone import Pinecone
def initialize_recommendation_system():
try:
# Initialize Groq
groq_client = Groq(api_key=st.secrets["GROQ_API_KEY"])
# Initialize embeddings
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
# Initialize Pinecone
pc = Pinecone(api_key=st.secrets["PINECONE_API_KEY"])
# Get the index
index_name = "imdb-index"
index = pc.Index(index_name)
# Check index stats
index_stats = index.describe_index_stats()
# Initialize vector store
docsearch = PineconeVectorStore.from_existing_index(
index_name=index_name,
embedding=embeddings,
namespace=""
)
# Initialize LLM
llm = ChatGroq(
model_name="llama3-8b-8192",
api_key=st.secrets["GROQ_API_KEY"],
temperature=0
)
# Define prompt template
template = """You are a movie recommender system that helps users find movies that match their preferences.
Use the following pieces of context to answer the question at the end.
For each question, suggest three movies, with a short description of the plot and the reason why the user might like it.
Format your response in a clear, easy-to-read way with line breaks between movies.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Your response:"""
PROMPT = PromptTemplate(
template=template, input_variables=["context", "question"]
)
# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True,
chain_type_kwargs={"prompt": PROMPT}
)
return qa_chain
except Exception as e:
st.error(f"Error initializing the recommendation system: {str(e)}")
st.error(traceback.format_exc())
return None
def get_recommendations(query, qa_chain):
try:
with st.spinner('🎬 Finding perfect movies for you...'):
st.write(f"Searching for query: {query}")
result = qa_chain.invoke({"query": query})
recommendations = result['result']
return recommendations
except Exception as e:
st.error(f"Error getting recommendations: {str(e)}")
st.error(traceback.format_exc())
return None
def main():
# Custom CSS to reduce margins
st.markdown("""
<style>
.block-container {
padding-left: 2rem !important;
padding-right: 2rem !important;
max-width: 95rem !important;
}
.stButton button {
width: 100%;
}
</style>
""", unsafe_allow_html=True)
# Initialize session state keys if they don't exist
if 'initialized' not in st.session_state:
st.session_state.initialized = False
# Header
st.title("🎬 Movie Recommendation System")
st.markdown("### Find your next favorite movie!")
# Initialize the system if not already done
if not st.session_state.initialized:
with st.spinner('Initializing recommendation system...'):
qa_chain = initialize_recommendation_system()
if qa_chain:
st.session_state.qa_chain = qa_chain
st.session_state.initialized = True
# Create columns for layout with adjusted ratios
col1, col2 = st.columns([3, 1]) # Changed ratio from [2, 1] to [3, 1] for better space utilization
with col1:
# Search input
query = st.text_input(
"What kind of movie are you looking for?",
placeholder="e.g., 'A sci-fi movie with time travel' or 'A romantic comedy set in New York'",
key="movie_query"
)
# Search button
if st.button("Get Recommendations 🔍", type="primary"):
if query:
recommendations = get_recommendations(query, st.session_state.qa_chain)
if recommendations:
# Process and extract movie details
recommendations_list = recommendations.strip().split('\n')
formatted_recommendations = []
for line in recommendations_list:
# Ensure movie names are detected and formatted
if "Movie:" in line or line.startswith("*"):
formatted_recommendations.append(f"**{line.strip()}**")
else:
formatted_recommendations.append(line.strip())
# Combine into a single formatted block
final_output = "\n\n".join(formatted_recommendations)
# Display recommendations in one box
st.markdown(f"""
<div style="border: 1px solid #ddd; border-radius: 8px; padding: 15px; margin-bottom: 15px; box-shadow: 2px 2px 5px rgba(0, 0, 0, 0.1);">
<h4>🎥 Movie Recommendations:</h4>
<p style="white-space: pre-line;">{final_output}</p>
</div>
""", unsafe_allow_html=True)
else:
st.warning("No recommendations found. Please try a different query.")
else:
st.warning("Please enter what kind of movie you're looking for!")
if __name__ == "__main__":
main() |