Final app final version

app.py

@@ -6,112 +6,136 @@ from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_community.vectorstores import FAISS
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_groq import ChatGroq
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyPDFLoader
-from langchain_core.output_parsers import StrOutputParser
 import os
 from dotenv import load_dotenv
+
+# Load environment variables
 load_dotenv()
 
-# API and model setting
-os.environ['HF_TOKEN']=os.getenv('HF_TOKEN')
-os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
-embeddings=HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+# API and model settings
+os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN')
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
 # Streamlit app
-st.title("𝖯𝖣𝖥𝖲𝖾𝗇𝗌𝖾 : 𝖯𝖣𝖥 𝖰𝗎𝖾𝗌𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝖠𝗇𝗌𝗐𝖾𝗋𝗂𝗇𝗀 𝗐𝗂𝗍𝗁 𝗌𝖾𝗌𝗌𝗂𝗈𝗇 𝖼𝗁𝖺𝗍 𝗁𝗂𝗌𝗍𝗈𝗋𝗒")
-st.write("upload pdfs and ask questions related to pdfs")
-llm=ChatGroq(model="Gemma2-9b-It")
-session_id=st.text_input("Session id",value="common_session")
+st.title("𝖯𝖣𝖥𝖲𝖾𝗇𝗌𝖾 : 𝖯𝖣𝖥 𝖰𝗎𝖾𝗌𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝖠𝗇𝗌𝗐𝖾𝗋𝗂𝗇𝗀 𝗐𝗂𝗍𝗁 𝖲𝖾𝗌𝗌𝗂𝗈𝗇 𝖢𝗁𝖺𝗍 𝖧𝗂𝗌𝗍𝗈𝗋𝗒")
+st.markdown('####')
+st.write("Upload PDFs and ask questions related to the content of the PDFs.")
+llm = ChatGroq(model="Gemma2-9b-It")
+session_id = st.text_input("Session ID", value="common_session")
 
-# manage chat history
+# Manage chat history
 if 'store' not in st.session_state:
-    st.session_state.store={}
-
-# Upload files and documents loading
-uploaded_files=st.file_uploader("Drop the pdf files here",type="pdf",accept_multiple_files=True)
+    st.session_state.store = {}
+st.markdown('####')
+# Upload files and document loading
+uploaded_files = st.file_uploader("Drop the PDF files here", type="pdf", accept_multiple_files=True)
+st.markdown('####')
 if uploaded_files:
-    documents=[]
+    documents = []
     for uploaded_file in uploaded_files:
-        temppdf=f"./temp.pdf"
-        with open(temppdf,"wb") as file:
+        temppdf = f"./temp.pdf"
+        with open(temppdf, "wb") as file:
             file.write(uploaded_file.getvalue())
-            file_name=uploaded_file.name
-        docs=PyPDFLoader(temppdf).load()
+        docs = PyPDFLoader(temppdf).load()
         documents.extend(docs)
+
     # Delete the temp file as we no longer need it
     if os.path.exists("./temp.pdf"):
         os.remove("./temp.pdf")
-    # Text splitting and embedding and storing in chromadb
-    text_splitter=RecursiveCharacterTextSplitter(chunk_size=5000,chunk_overlap=500)
-    splits=text_splitter.split_documents(documents)
+
+    # Text splitting and embedding, storing in FAISS index
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    splits = text_splitter.split_documents(documents)
     faiss_index = FAISS.from_documents(splits, embeddings)
-    retriever=faiss_index.as_retriever()
+    retriever = faiss_index.as_retriever()
 
     # Prompts
-    context_system_prompt=(
-        "Given a chat history and latest user question"
+    context_system_prompt = (
+        "Given a chat history and the latest user question, "
         "which might reference context in the chat history, "
-        "formulate a standalone question which can be understood "
-        "without the chat history. Do Not answer the question, "
-        "just reformulate it if needed and otherwise return it as it is"
+        "formulate a standalone question that can be understood "
+        "without the chat history. Do not answer the question, "
+        "just reformulate it if needed and otherwise return it as it is."
     )
-    context_prompt=ChatPromptTemplate.from_messages([
-        ("system",context_system_prompt),
+    context_prompt = ChatPromptTemplate.from_messages([
+        ("system", context_system_prompt),
         MessagesPlaceholder("chat_history"),
-        ("human","{input}")]
-    )
+        ("human", "{input}")
+    ])
 
-    history_aware_ret=create_history_aware_retriever(llm,retriever,context_prompt)
+    history_aware_ret = create_history_aware_retriever(llm, retriever, context_prompt)
 
-    system_prompt=(
-        "You are 'PDFSense' a PDF reading and answering assistant. "
+    system_prompt = (
+        "You are 'PDFSense', a PDF reading and answering assistant. "
         "Use the following pieces of retrieved context to answer "
-        "the question. If you don't know the answer, say that you dont know."
+        "the question. If you don't know the answer, say that you don't know. "
         "Answer the questions nicely."
         "\n\n"
         "{context}"
     )
 
-    prompt=ChatPromptTemplate.from_messages(
-        [
-            ("system",system_prompt),
-            MessagesPlaceholder("chat_history"),
-            ("human","{input}")
-        ]
-    )
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}")
+    ])
+
     # Chain for the chatbot
-    qa_chain=create_stuff_documents_chain(llm,prompt)
-    rag_chain=create_retrieval_chain(history_aware_ret,qa_chain)
+    qa_chain = create_stuff_documents_chain(llm, prompt)
+    rag_chain = create_retrieval_chain(history_aware_ret, qa_chain)
 
-    # Session Id storing in chat history
-    def get_session_history(session:str)-> BaseChatMessageHistory:
+    # Session ID storing in chat history
+    def get_session_history(session: str) -> BaseChatMessageHistory:
         if session_id not in st.session_state.store:
-            st.session_state.store[session_id]=ChatMessageHistory()
+            st.session_state.store[session_id] = ChatMessageHistory()
         return st.session_state.store[session_id]
-    
+
     # RAG with history
-    conversation_rag=RunnableWithMessageHistory(
+    conversation_rag = RunnableWithMessageHistory(
         rag_chain,
         get_session_history,
         input_messages_key="input",
         history_messages_key="chat_history",
-        output_messages_key="answer"                                        
-        )
-    
-    user_input=st.text_input("Enter question")
+        output_messages_key="answer"
+    )
+
+    user_input = st.text_input("Enter your question")
     if user_input:
-        session_history=get_session_history(session_id)
-        response=conversation_rag.invoke(
-            {"input":user_input},
+        session_history = get_session_history(session_id)
+        response = conversation_rag.invoke(
+            {"input": user_input},
             config={
-                "configurable":{"session_id":session_id}
+                "configurable": {"session_id": session_id}
             },
         )
-        st.write(st.session_state.store)
-        st.write("Assistant:",response['answer'])
-        st.write("Chat History",session_history.messages)
+
+        st.write("### Response")
+        st.success(response['answer'])
+        # Display the chat history
+        st.write("### Chat History")
+        for message in session_history.messages:
+            if isinstance(message, dict):  # Handle cases where messages might be dictionaries
+                role = message.get("role", "user")  # Default role is 'user'
+                content = message.get("content", "")
+            else:
+                # For LangChain message objects
+                role = "user" if isinstance(message, ChatMessageHistory) else "assistant"
+                content = message.content
+
+            if role == "user":
+                with st.chat_message("user"):
+                    st.success(content)
+            elif role == "assistant":
+                with st.chat_message("assistant"):
+                    st.success(content)
+            elif role == "system":
+                with st.chat_message("system"):
+                    st.markdown(f"**System Message:** {content}")
+        #st.write("Assistant:", response['answer'])

trialapp.py → trial.py

@@ -6,136 +6,112 @@ from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_community.vectorstores import FAISS
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
 from langchain_groq import ChatGroq
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyPDFLoader
+from langchain_core.output_parsers import StrOutputParser
 import os
 from dotenv import load_dotenv
-
-# Load environment variables
 load_dotenv()
 
-# API and model settings
-os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN')
-os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+# API and model setting
+os.environ['HF_TOKEN']=os.getenv('HF_TOKEN')
+os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
+embeddings=HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
 # Streamlit app
-st.title("𝖯𝖣𝖥𝖲𝖾𝗇𝗌𝖾 : 𝖯𝖣𝖥 𝖰𝗎𝖾𝗌𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝖠𝗇𝗌𝗐𝖾𝗋𝗂𝗇𝗀 𝗐𝗂𝗍𝗁 𝖲𝖾𝗌𝗌𝗂𝗈𝗇 𝖢𝗁𝖺𝗍 𝖧𝗂𝗌𝗍𝗈𝗋𝗒")
-st.markdown('####')
-st.write("Upload PDFs and ask questions related to the content of the PDFs.")
-llm = ChatGroq(model="Gemma2-9b-It")
-session_id = st.text_input("Session ID", value="common_session")
+st.title("𝖯𝖣𝖥𝖲𝖾𝗇𝗌𝖾 : 𝖯𝖣𝖥 𝖰𝗎𝖾𝗌𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝖠𝗇𝗌𝗐𝖾𝗋𝗂𝗇𝗀 𝗐𝗂𝗍𝗁 𝗌𝖾𝗌𝗌𝗂𝗈𝗇 𝖼𝗁𝖺𝗍 𝗁𝗂𝗌𝗍𝗈𝗋𝗒")
+st.write("upload pdfs and ask questions related to pdfs")
+llm=ChatGroq(model="Gemma2-9b-It")
+session_id=st.text_input("Session id",value="common_session")
 
-# Manage chat history
+# manage chat history
 if 'store' not in st.session_state:
-    st.session_state.store = {}
-st.markdown('####')
-# Upload files and document loading
-uploaded_files = st.file_uploader("Drop the PDF files here", type="pdf", accept_multiple_files=True)
-st.markdown('####')
+    st.session_state.store={}
+
+# Upload files and documents loading
+uploaded_files=st.file_uploader("Drop the pdf files here",type="pdf",accept_multiple_files=True)
 if uploaded_files:
-    documents = []
+    documents=[]
     for uploaded_file in uploaded_files:
-        temppdf = f"./temp.pdf"
-        with open(temppdf, "wb") as file:
+        temppdf=f"./temp.pdf"
+        with open(temppdf,"wb") as file:
             file.write(uploaded_file.getvalue())
-        docs = PyPDFLoader(temppdf).load()
+            file_name=uploaded_file.name
+        docs=PyPDFLoader(temppdf).load()
         documents.extend(docs)
-
     # Delete the temp file as we no longer need it
     if os.path.exists("./temp.pdf"):
         os.remove("./temp.pdf")
-
-    # Text splitting and embedding, storing in FAISS index
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
-    splits = text_splitter.split_documents(documents)
+    # Text splitting and embedding and storing in chromadb
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=5000,chunk_overlap=500)
+    splits=text_splitter.split_documents(documents)
     faiss_index = FAISS.from_documents(splits, embeddings)
-    retriever = faiss_index.as_retriever()
+    retriever=faiss_index.as_retriever()
 
     # Prompts
-    context_system_prompt = (
-        "Given a chat history and the latest user question, "
+    context_system_prompt=(
+        "Given a chat history and latest user question"
         "which might reference context in the chat history, "
-        "formulate a standalone question that can be understood "
-        "without the chat history. Do not answer the question, "
-        "just reformulate it if needed and otherwise return it as it is."
+        "formulate a standalone question which can be understood "
+        "without the chat history. Do Not answer the question, "
+        "just reformulate it if needed and otherwise return it as it is"
     )
-    context_prompt = ChatPromptTemplate.from_messages([
-        ("system", context_system_prompt),
+    context_prompt=ChatPromptTemplate.from_messages([
+        ("system",context_system_prompt),
         MessagesPlaceholder("chat_history"),
-        ("human", "{input}")
-    ])
+        ("human","{input}")]
+    )
 
-    history_aware_ret = create_history_aware_retriever(llm, retriever, context_prompt)
+    history_aware_ret=create_history_aware_retriever(llm,retriever,context_prompt)
 
-    system_prompt = (
-        "You are 'PDFSense', a PDF reading and answering assistant. "
+    system_prompt=(
+        "You are 'PDFSense' a PDF reading and answering assistant. "
         "Use the following pieces of retrieved context to answer "
-        "the question. If you don't know the answer, say that you don't know. "
+        "the question. If you don't know the answer, say that you dont know."
         "Answer the questions nicely."
         "\n\n"
         "{context}"
     )
 
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}")
-    ])
-
+    prompt=ChatPromptTemplate.from_messages(
+        [
+            ("system",system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human","{input}")
+        ]
+    )
     # Chain for the chatbot
-    qa_chain = create_stuff_documents_chain(llm, prompt)
-    rag_chain = create_retrieval_chain(history_aware_ret, qa_chain)
+    qa_chain=create_stuff_documents_chain(llm,prompt)
+    rag_chain=create_retrieval_chain(history_aware_ret,qa_chain)
 
-    # Session ID storing in chat history
-    def get_session_history(session: str) -> BaseChatMessageHistory:
+    # Session Id storing in chat history
+    def get_session_history(session:str)-> BaseChatMessageHistory:
         if session_id not in st.session_state.store:
-            st.session_state.store[session_id] = ChatMessageHistory()
+            st.session_state.store[session_id]=ChatMessageHistory()
         return st.session_state.store[session_id]
-
+    
     # RAG with history
-    conversation_rag = RunnableWithMessageHistory(
+    conversation_rag=RunnableWithMessageHistory(
         rag_chain,
         get_session_history,
         input_messages_key="input",
         history_messages_key="chat_history",
-        output_messages_key="answer"
-    )
-
-    user_input = st.text_input("Enter your question")
+        output_messages_key="answer"                                        
+        )
+    
+    user_input=st.text_input("Enter question")
     if user_input:
-        session_history = get_session_history(session_id)
-        response = conversation_rag.invoke(
-            {"input": user_input},
+        session_history=get_session_history(session_id)
+        response=conversation_rag.invoke(
+            {"input":user_input},
             config={
-                "configurable": {"session_id": session_id}
+                "configurable":{"session_id":session_id}
             },
         )
-
-        st.write("### Response")
-        st.success(response['answer'])
-        # Display the chat history
-        st.write("### Chat History")
-        for message in session_history.messages:
-            if isinstance(message, dict):  # Handle cases where messages might be dictionaries
-                role = message.get("role", "user")  # Default role is 'user'
-                content = message.get("content", "")
-            else:
-                # For LangChain message objects
-                role = "user" if isinstance(message, ChatMessageHistory) else "assistant"
-                content = message.content
-
-            if role == "user":
-                with st.chat_message("user"):
-                    st.success(content)
-            elif role == "assistant":
-                with st.chat_message("assistant"):
-                    st.success(content)
-            elif role == "system":
-                with st.chat_message("system"):
-                    st.markdown(f"**System Message:** {content}")
-        #st.write("Assistant:", response['answer'])
+        st.write(st.session_state.store)
+        st.write("Assistant:",response['answer'])
+        st.write("Chat History",session_history.messages)