Create app.py

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+
+def main():
+    st.title("PDF Chat with Groq LLM")
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+    
+    if uploaded_file is not None:
+        # Save the uploaded PDF temporarily
+        with open("temp.pdf", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        
+        # Load the PDF
+        loader = PyPDFLoader("temp.pdf")
+        pages = loader.load()
+        
+        # Split the text
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000, 
+            chunk_overlap=200
+        )
+        texts = text_splitter.split_documents(pages)
+        
+        # Create embeddings
+        embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2"
+        )
+        
+        # Create vector store
+        vectorstore = FAISS.from_documents(texts, embeddings)
+        
+        # Initialize Groq LLM
+        llm = ChatGroq(
+            temperature=0.7, 
+            model_name='llama3-70b-8192'
+        )
+        
+        # Create QA chain
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=llm, 
+            chain_type="stuff", 
+            retriever=vectorstore.as_retriever(search_kwargs={"k": 3})
+        )
+        
+        # Chat input
+        query = st.text_input("Ask a question about the PDF:")
+        
+        if query:
+            # Get response
+            response = qa_chain.invoke(query)
+            st.write("Response:", response['result'])
+
+if __name__ == "__main__":
+    main()