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| import streamlit as st | |
| import os | |
| import numpy as np | |
| import re | |
| import tempfile | |
| import torch | |
| from datetime import datetime | |
| from langchain_community.document_loaders import PDFPlumberLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.llms import Ollama | |
| from langchain.retrievers import BM25Retriever, EnsembleRetriever | |
| from sentence_transformers import CrossEncoder | |
| from transformers import pipeline | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain.chains import LLMChain | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from langchain.llms.huggingface_pipeline import HuggingFacePipeline | |
| from huggingface_hub import login | |
| # Load the model and tokenizer | |
| model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B" | |
| # Initialize classifier once for input guardrail | |
| classifier = pipeline("zero-shot-classification", | |
| model="typeform/distilbert-base-uncased-mnli") | |
| # Streamlit UI Configuration | |
| st.set_page_config(page_title="Multi-File Financial Analyzer", layout="wide") | |
| st.title("📊 Financial Analysis System") | |
| # Sidebar Controls | |
| with st.sidebar: | |
| st.header("Configuration Panel") | |
| model_choice = st.selectbox("LLM Model", | |
| [model_name], | |
| help="Choose the core analysis engine") | |
| chunk_size = st.slider("Document Chunk Size", 500, 2000, 1000) | |
| rerank_threshold = st.slider("Re-ranking Threshold", 0.0, 1.0, 0.1) | |
| # File Upload Handling for multiple files | |
| uploaded_files = st.file_uploader("Upload Financial PDFs", | |
| type="pdf", | |
| accept_multiple_files=True) | |
| if uploaded_files: | |
| all_docs = [] | |
| with st.spinner("Processing Multiple Financial Documents..."): | |
| for uploaded_file in uploaded_files: | |
| # Create temporary file for each PDF | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp: | |
| tmp.write(uploaded_file.getvalue()) | |
| tmp_path = tmp.name | |
| # Load and process each document | |
| loader = PDFPlumberLoader(tmp_path) | |
| docs = loader.load() | |
| all_docs.extend(docs) | |
| # Combined Document Processing | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=chunk_size, | |
| chunk_overlap=200, | |
| separators=["\n\n", "\n", "\. ", "! ", "? ", " ", ""] | |
| ) | |
| documents = text_splitter.split_documents(all_docs) | |
| # Hybrid Retrieval Setup for combined documents | |
| embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | |
| vector_store = FAISS.from_documents(documents, embedder) | |
| bm25_retriever = BM25Retriever.from_documents(documents) | |
| bm25_retriever.k = 5 | |
| faiss_retriever = vector_store.as_retriever(search_kwargs={"k": 5}) | |
| ensemble_retriever = EnsembleRetriever( | |
| retrievers=[bm25_retriever, faiss_retriever], | |
| weights=[0.4, 0.6] | |
| ) | |
| # Re-ranking Model | |
| cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') | |
| # Financial Analysis LLM Configuration | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| padding_side="left" # Important for some models | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| ) | |
| # Create pipeline with generation parameters | |
| pipeline_llm = pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| max_new_tokens=1024, | |
| temperature=0.3, | |
| top_p=0.95, | |
| repetition_penalty=1.15, | |
| return_full_text=False # Important for response formatting | |
| ) | |
| llm = HuggingFacePipeline(pipeline=pipeline_llm) | |
| # Update prompt template | |
| PROMPT_TEMPLATE = """ | |
| <|system|> | |
| You are a senior financial analyst. Analyze these financial reports: | |
| 1. Compare key metrics between documents | |
| 2. Identify trends across reporting periods | |
| 3. Highlight differences/similarities | |
| 4. Provide risk assessment | |
| 5. Offer recommendations | |
| Format response with clear sections and bullet points. Keep under 300 words. | |
| Context: {context} | |
| Question: {question} | |
| <|assistant|> | |
| """ | |
| # chat prompt template | |
| qa_prompt = PromptTemplate( | |
| template=PROMPT_TEMPLATE, | |
| input_variables=["context", "question"] | |
| ) | |
| llm_chain = LLMChain(llm=llm, prompt=qa_prompt) | |
| # Interactive Q&A Interface | |
| st.header("🔍 Cross-Document Financial Inquiry") | |
| # Suggested Comparative Questions | |
| comparative_questions = [ | |
| "Analyze changes in debt structure across both reports", | |
| "Show expense ratio differences between the two years", | |
| "What are the main liquidity changes across both periods?", | |
| ] | |
| user_query = st.selectbox("Sample Financial Questions", | |
| [""] + comparative_questions) | |
| user_input = st.text_input("Or enter custom financial query:", | |
| value=user_query) | |
| if user_input: | |
| # Input Validation Guardrail | |
| classification = classifier(user_input, | |
| ["financial", "other"], | |
| multi_label=False) | |
| print(f"-- Guard rail check is completed for query with prob:{classification['scores'][0]}") | |
| if classification['scores'][0] < 0.2: | |
| st.error("Query not related to financial. Ask about financial related queries") | |
| st.stop() | |
| with st.spinner("Performing Cross-Document Analysis..."): | |
| # Hybrid Document Retrieval | |
| initial_docs = ensemble_retriever.get_relevant_documents(user_input) | |
| # Context Re-ranking | |
| doc_pairs = [(user_input, doc.page_content) for doc in initial_docs] | |
| rerank_scores = cross_encoder.predict(doc_pairs) | |
| sorted_indices = np.argsort(rerank_scores)[::-1] | |
| ranked_docs = [initial_docs[i] for i in sorted_indices] | |
| filtered_docs = [d for d, s in zip(ranked_docs, rerank_scores) | |
| if s > rerank_threshold][:7] | |
| print(f"-- Retrieved chunks:{filtered_docs}") | |
| # Confidence Calculation | |
| confidence_score = np.mean(rerank_scores[sorted_indices][:3]) * 100 | |
| confidence_score = min(100, max(0, round(confidence_score, 1))) | |
| # Response Generation | |
| context = "\n".join([doc.page_content for doc in filtered_docs]) | |
| print(f"-- Retrieved context:{context}") | |
| analysis = llm_chain.run( | |
| context=context, | |
| question=user_input | |
| ) | |
| print(f"Analysis result:{analysis}") | |
| # Response Cleaning | |
| clean_analysis = re.sub(r"<think>|</think>|\n{3,}", "", analysis) | |
| clean_analysis = re.sub(r'(\d)([A-Za-z])', r'\1 \2', clean_analysis) | |
| clean_analysis = re.sub(r'(\d{1,3})(\d{3})', r'\1,\2', clean_analysis) | |
| # Input Display | |
| st.subheader("User Query+Context to the LLM") | |
| st.markdown(f"```\n{qa_prompt.format(context=context, question=user_input)}\n```") | |
| # Results Display | |
| st.subheader("Integrated Financial Analysis") | |
| st.markdown(f"```\n{clean_analysis}\n```") | |
| st.progress(int(confidence_score)/100) | |
| st.caption(f"Analysis Confidence: {confidence_score}%") | |
| # Export Functionality | |
| if st.button("Generate Financial Analysis Report"): | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| export_content = f"COMPARATIVE QUERY: {user_input}\n\nANALYSIS:\n{clean_analysis}" | |
| st.download_button("Download Full Report", export_content, | |
| file_name=f"Comparative_Analysis_{timestamp}.txt", | |
| mime="text/plain") | |
| else: | |
| st.info("Please upload PDF financial reports to begin financial analysis") |