Update app.py

app.py

@@ -1,25 +1,134 @@
 import streamlit as st
 from dotenv import load_dotenv
 import os
-from htmlTemplate import css, bot_template, user_template
+import traceback
+
+# PDF and NLP Libraries
 import PyPDF2
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings.spacy_embeddings import SpacyEmbeddings
-from langchain_community.llms import LlamaCpp
-from langchain.embeddings import HuggingFaceEmbeddings 
+from sentence_transformers import SentenceTransformer, util
+
+# Embedding and Vector Store
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
+
+# LLM and Conversational Chain
+from langchain_groq import ChatGroq
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain.prompts import PromptTemplate
-from sentence_transformers import SentenceTransformer, util
-from langchain_openai import AzureOpenAIEmbeddings
-from langchain_openai import OpenAIEmbeddings
-from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-from langchain_openai import ChatOpenAI
 
+# Custom Templates
+from htmlTemplate import css, bot_template, user_template
+
+# Load environment variables
+os.environ["GROQ_API_KEY"]= "sss"
+
+# LLM Template for focused responses
+llmtemplate = """You're an AI information specialist with a strong emphasis on extracting accurate information from markdown documents. Your expertise involves summarizing data succinctly while adhering to strict guidelines about neutrality and clarity.
+Your task is to answer a specific question based on a provided markdown document. Here is the question you need to address:  
+{question}
+Keep in mind the following instructions:  
+- Your response should be direct and factual, limited to 50 words and 2-3 sentences.  
+- Avoid using introductory phrases like "yes" or "no."  
+- Maintain an ethical and unbiased tone, steering clear of harmful or offensive content.  
+- If the document lacks relevant information, respond with "I cannot provide an answer based on the provided document."  
+- Do not fabricate information, include questions, or use confirmatory phrases.  
+- Remember not to prompt for additional information or ask any questions.  
+Ensure your response is strictly based on the content of the markdown document.
+"""
+
+def prepare_docs(pdf_docs):
+    """Extract text from uploaded PDF documents"""
+    docs = []
+    metadata = []
+    content = []
+
+    for pdf in pdf_docs:
+        pdf_reader = PyPDF2.PdfReader(pdf)
+        for index, text in enumerate(pdf_reader.pages):
+            doc_page = {
+                'title': f"{pdf.name} page {index + 1}",
+                'content': pdf_reader.pages[index].extract_text()
+            }
+            docs.append(doc_page)
+    
+    for doc in docs:
+        content.append(doc["content"])
+        metadata.append({"title": doc["title"]})
+    
+    return content, metadata
+
+def get_text_chunks(content, metadata):
+    """Split documents into manageable chunks"""
+    text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+        chunk_size=1024,
+        chunk_overlap=256,
+    )
+    split_docs = text_splitter.create_documents(content, metadatas=metadata)
+    print(f"Split documents into {len(split_docs)} passages")
+    return split_docs
+
+def ingest_into_vectordb(split_docs):
+    """Create vector embeddings and store in FAISS"""
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        model_kwargs={'device':'cpu'}
+    )
+    db = FAISS.from_documents(split_docs, embeddings)
+    DB_FAISS_PATH = 'vectorstore/db_faiss'
+    db.save_local(DB_FAISS_PATH)
+    return db
+
+def get_conversation_chain(vectordb):
+    """Create conversational retrieval chain"""
+    llm = ChatGroq(model="llama3-70b-8192", temperature=0.25)
+    retriever = vectordb.as_retriever()
+
+    memory = ConversationBufferMemory(
+        memory_key='chat_history', 
+        return_messages=True, 
+        output_key='answer'
+    )
+
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        memory=memory,
+        return_source_documents=True
+    )
+    
+    print("Conversational Chain created for the LLM using the vector store")
+    return conversation_chain
+
+def validate_answer_against_sources(response_answer, source_documents):
+    """Validate AI's response against source documents"""
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    similarity_threshold = 0.5  
+    source_texts = [doc.page_content for doc in source_documents]
+
+    answer_embedding = model.encode(response_answer, convert_to_tensor=True)
+    source_embeddings = model.encode(source_texts, convert_to_tensor=True)
+
+    cosine_scores = util.pytorch_cos_sim(answer_embedding, source_embeddings)
 
+    return any(score.item() > similarity_threshold for score in cosine_scores[0])
+
+def handle_userinput(user_question):
+    """Process user input and display chat history"""
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+    
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(user_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
 
 def main():
+    """Main Streamlit application"""
     load_dotenv()
 
     st.set_page_config(
@@ -28,7 +137,6 @@ def main():
         layout="wide"
     )
     st.write(css, unsafe_allow_html=True)
-    
 
     # Welcome section
     st.title("📚 PDF Insights AI")
@@ -39,6 +147,7 @@ def main():
     - 📄 Support for multiple PDF files
     """)
 
+    # Initialize session state
     if "conversation" not in st.session_state:
         st.session_state.conversation = None
     if "chat_history" not in st.session_state:
@@ -67,16 +176,10 @@ def main():
             else:
                 with st.spinner("Processing your documents..."):
                     try:
-                        # get pdf text
+                        # Process documents
                         content, metadata = prepare_docs(pdf_docs)
-
-                        # get the text chunks
                         split_docs = get_text_chunks(content, metadata)
-
-                        # create vector store
                         vectorstore = ingest_into_vectordb(split_docs)
-
-                        # create conversation chain
                         st.session_state.conversation = get_conversation_chain(vectorstore)
                         
                         st.success("Documents processed successfully! You can now ask questions.")
@@ -93,4 +196,7 @@ def main():
         if st.session_state.conversation is None:
             st.warning("Please upload and process documents first.")
         else:
-            handle_userinput(user_question)
+            handle_userinput(user_question)
+
+if __name__ == '__main__':
+    main()