Create app.py

app.py

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+import os
+import logging
+import torch
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langchain_community.document_loaders import PDFMinerLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+
+# Setup
+logging.basicConfig(level=logging.INFO)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+persist_directory = "db"
+uploaded_files_dir = "uploaded_files"
+os.makedirs(uploaded_files_dir, exist_ok=True)
+
+checkpoint = "MBZUAI/LaMini-T5-738M"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+base_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+
+def data_ingestion():
+    try:
+        documents = []
+        for filename in os.listdir(uploaded_files_dir):
+            if filename.endswith(".pdf"):
+                file_path = os.path.join(uploaded_files_dir, filename)
+                loader = PDFMinerLoader(file_path)
+                docs = loader.load()
+                for doc in docs:
+                    if hasattr(doc, 'page_content') and len(doc.page_content.strip()) > 0:
+                        documents.append(doc)
+        
+        if not documents:
+            st.error("No valid text extracted from uploaded PDFs.")
+            return
+
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+        texts = splitter.split_documents(documents)
+
+        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
+        db.persist()
+        st.success("Document ingested and stored successfully.")
+
+    except Exception as e:
+        st.error(f"Error during data ingestion: {str(e)}")
+
+def qa_llm():
+    pipe = pipeline(
+        'text2text-generation',
+        model=base_model,
+        tokenizer=tokenizer,
+        max_length=256,
+        do_sample=True,
+        temperature=0.3,
+        top_p=0.95,
+        device=0 if torch.cuda.is_available() else -1
+    )
+    llm = HuggingFacePipeline(pipeline=pipe)
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    retriever = db.as_retriever()
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+    return qa
+
+def process_query(query):
+    try:
+        qa = qa_llm()
+        tailored_prompt = f"""
+        You are an expert chatbot designed to assist Chartered Accountants (CAs) in the field of audits.
+        Your goal is to provide accurate and comprehensive answers to any questions related to audit policies,
+        procedures, and accounting standards based on the uploaded PDF documents.
+
+        User question: {query}
+        """
+        result = qa({"query": tailored_prompt})
+        return result["result"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Streamlit UI
+st.set_page_config(page_title="CA Audit Chatbot", layout="centered")
+st.title("📚 Chartered Accountant Audit Assistant")
+st.markdown("Upload a PDF file and ask audit-related questions. This AI assistant will answer based on document content.")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload PDF file", type=["pdf"])
+if uploaded_file is not None:
+    save_path = os.path.join(uploaded_files_dir, uploaded_file.name)
+    with open(save_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    st.success("PDF uploaded successfully!")
+    if st.button("Ingest Document"):
+        data_ingestion()
+
+# Query input
+user_query = st.text_input("Ask a question about the audit document:")
+if user_query:
+    response = process_query(user_query)
+    st.markdown("### 📌 Answer:")
+    st.write(response)