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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+IMDB_Dataset.csv filter=lfs diff=lfs merge=lfs -text

IMDB_Dataset.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:707c8898a0883870cb896a1ed90f6468de200b84b3670534f0d796ef366ebdb0
+size 12489628

app.py

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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="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")
+        
+        # Initialize embeddings
+        embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2"
+        )
+        
+        # Initialize Pinecone
+        pc = Pinecone(api_key="pcsk_5KteZi_2sCjoDdRptBcLCqqBADE29Hkz75rG17GyhBPhtWm4Sd5nyZqoejF5ke3oKMVFYj")
+        
+        # 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()

imdb.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c23b36dc428f71e5d1c56c0c5fecc954a28e5f6555486931c50a6dc210b3ac80
+size 53421398

requirements.txt

@@ -0,0 +1,8 @@
+streamlit==1.32.2
+groq==0.4.3
+langchain-groq==0.0.5
+langchain==0.1.12
+langchain-huggingface==0.0.9
+langchain-community==0.0.28
+pinecone-client==3.1.0
+sentence-transformers==2.5.1