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Initial_commit_01

main.py

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+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.storage import LocalFileStore
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+import streamlit as st
+import os
+import shutil
+from dotenv import load_dotenv
+from streamlit_chat import message  
+
+load_dotenv()
+
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API')
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = os.getenv('LANGSMITH_API')
+
+UPLOAD_DIR = "uploaded_files"
+
+def cleanup_files():
+    if os.path.isdir(UPLOAD_DIR):
+        shutil.rmtree(UPLOAD_DIR, ignore_errors=True)
+    if 'file_handle' in st.session_state:
+        st.session_state.file_handle.close()
+
+if 'cleanup_done' not in st.session_state:
+    st.session_state.cleanup_done = False
+
+if not st.session_state.cleanup_done:
+    cleanup_files()
+
+if not os.path.exists(UPLOAD_DIR):
+    os.makedirs(UPLOAD_DIR)
+
+st.title("Chat with Your PDF!!")
+uploaded_file = st.file_uploader("Upload a file")
+
+if uploaded_file is not None:
+    file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
+    file_path = os.path.abspath(file_path)
+
+    with open(file_path, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    st.write("You're Ready For a Chat with your PDF")
+
+    docs = PyPDFLoader(file_path).load_and_split()
+
+    embedding = HuggingFaceBgeEmbeddings(
+        model_name='BAAI/llm-embedder',
+    )
+
+    store = LocalFileStore("./cache/")
+    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+        embedding, store, namespace='embeddings'
+    )
+
+    vector_base = FAISS.from_documents(
+        docs,
+        embedding
+    )
+
+    template = '''You are an Experienced Business Person Having a 
+                great Knowledge About Various Types of Business Activities
+                but here you are hired to Give the answers to the {question} only based on {context}
+                .if you are unaware of the question Just reply it with I\'m Unaware of your Query.
+                Use three sentences maximum and keep the answer concise.'''
+
+    prompt = ChatPromptTemplate.from_template(template)
+    retriever = vector_base.as_retriever()
+
+    llm = ChatGroq(
+        model='mixtral-8x7b-32768',
+        temperature=0,
+    )
+
+    if 'history' not in st.session_state:
+        st.session_state.history = []
+
+    query = st.text_input("Enter your question")
+
+    if st.button("Submit !"):
+        if query:
+            chain = (
+                {'context': retriever, 'question': RunnablePassthrough()} 
+                | prompt | llm | StrOutputParser()
+            )
+            answer = chain.invoke(query)
+            st.session_state.history.append({'question': query, 'answer': answer})
+
+    if st.session_state.history:
+        st.write("### Previous Questions and Answers")
+        for idx, entry in enumerate(st.session_state.history):
+            message(f"**Q{idx + 1}:** {entry['question']}", is_table=True, key=f"question_{idx}",avatar_style='no-avatar')
+            message(f"**A{idx + 1}:** {entry['answer']}", is_table=True, key=f"answer_{idx}",avatar_style='no-avatar')
+
+
+if st.session_state.cleanup_done:
+    cleanup_files()

requirements.txt

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+python-dotenv
+streamlit
+langchain-google-genai
+langchain-core
+langchain-community
+pymupdf
+faiss-cpu
+langchain-groq
+langsmith
+streamlit-chat