Update compare-financial-report.py

compare-financial-report.py

@@ -1,168 +1,189 @@
-import streamlit as st
-import numpy as np
-import re
-import tempfile
-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
-
-# 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("📊 Comparative Financial Analysis System")
-
-# Sidebar Controls
-with st.sidebar:
-    st.header("Configuration Panel")
-    model_choice = st.selectbox("LLM Model", 
-                              ["deepseek-r1:1.5b", "llama3.2:1b"],
-                              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, 0.5)
-
-# File Upload Handling for multiple files
-uploaded_files = st.file_uploader("Upload 2 Financial PDFs", 
-                                type="pdf", 
-                                accept_multiple_files=True)
-
-if len(uploaded_files) == 2:
-    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
-        llm = Ollama(model=model_choice)
-        PROMPT_TEMPLATE = """
-        As a senior financial analyst, analyze the following context from multiple financial reports:
-        1. Compare key metrics between both documents
-        2. Identify trends across reporting periods
-        3. Highlight significant differences or similarities
-        4. Provide integrated risk assessment
-        5. Offer comprehensive recommendations
-        
-        Context: {context}
-        Question: {question}
-        
-        Format with clear section headers and bullet points.
-        Maintain comparative analysis throughout.
-        Keep under 300 words.
-        """
-        qa_prompt = PromptTemplate(
-            template=PROMPT_TEMPLATE,
-            input_variables=["context", "question"]
-        )
-        llm_chain = LLMChain(llm=llm, prompt=qa_prompt)  # Proper LLMChain initialization
-
-    # Interactive Q&A Interface
-    st.header("🔍 Cross-Document Financial Inquiry")
-    
-    # Suggested Comparative Questions
-    comparative_questions = [
-        "Compare revenue growth between both fiscal years",
-        "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?",
-        "How does net profit margin compare between the two reports?"
-    ]
-    user_query = st.selectbox("Sample Comparative Questions", 
-                            [""] + comparative_questions)
-    user_input = st.text_input("Or enter custom comparative query:", 
-                             value=user_query)
-
-    if user_input:
-        # Input Validation Guardrail
-        classification = classifier(user_input, 
-                                  ["financial comparison", "other"],
-                                  multi_label=False)
-        if classification['scores'][0] < 0.2:
-            st.error("Query not comparative/financial. Ask about financial comparisons between documents.")
-            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]
-            
-            # 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])
-            analysis = llm_chain.run(
-                context=context, 
-                question=user_input
-            )
-            
-            # 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)
-
-            # 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 Comparative 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")
-
-elif len(uploaded_files) > 0:
-    st.warning("Please upload exactly 2 financial documents for comparative analysis")
-else:
+import streamlit as st
+import numpy as np
+import re
+import tempfile
+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
+
+# Log in with your token (optional if already logged in via CLI)
+# login(token=HF_API_TOKEN)
+
+# Load the model and tokenizer
+model_name = "meta-llama/Llama-3.2-1B-Instruct"
+model_name = "HuggingFaceTB/SmolLM2-360M-Instruct"
+
+# 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("📊 Comparative Financial Analysis System")
+
+# Sidebar Controls
+with st.sidebar:
+    st.header("Configuration Panel")
+    model_choice = st.selectbox("LLM Model", 
+                              ["deepseek-r1:1.5b", "llama3.2:1b"],
+                              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, 0.5)
+
+# File Upload Handling for multiple files
+uploaded_files = st.file_uploader("Upload 2 Financial PDFs", 
+                                type="pdf", 
+                                accept_multiple_files=True)
+
+if len(uploaded_files) == 2:
+    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
+        # llm = Ollama(model=model_choice)
+
+        ##
+        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+        
+        # Create a local pipeline
+        pipeline_llm = pipeline("text-generation", model=model, tokenizer=tokenizer)
+        # Wrap the local pipeline with Langchain
+        llm = HuggingFacePipeline(pipeline=pipeline_llm)
+        #
+        
+        PROMPT_TEMPLATE = """
+        As a senior financial analyst, analyze the following context from multiple financial reports:
+        1. Compare key metrics between both documents
+        2. Identify trends across reporting periods
+        3. Highlight significant differences or similarities
+        4. Provide integrated risk assessment
+        5. Offer comprehensive recommendations
+        
+        Context: {context}
+        Question: {question}
+        
+        Format with clear section headers and bullet points.
+        Maintain comparative analysis throughout.
+        Keep under 300 words.
+        """
+        qa_prompt = PromptTemplate(
+            template=PROMPT_TEMPLATE,
+            input_variables=["context", "question"]
+        )
+        llm_chain = LLMChain(llm=llm, prompt=qa_prompt)  # Proper LLMChain initialization
+
+    # Interactive Q&A Interface
+    st.header("🔍 Cross-Document Financial Inquiry")
+    
+    # Suggested Comparative Questions
+    comparative_questions = [
+        "Compare revenue growth between both fiscal years",
+        "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?",
+        "How does net profit margin compare between the two reports?"
+    ]
+    user_query = st.selectbox("Sample Comparative Questions", 
+                            [""] + comparative_questions)
+    user_input = st.text_input("Or enter custom comparative query:", 
+                             value=user_query)
+
+    if user_input:
+        # Input Validation Guardrail
+        classification = classifier(user_input, 
+                                  ["financial comparison", "other"],
+                                  multi_label=False)
+        if classification['scores'][0] < 0.2:
+            st.error("Query not comparative/financial. Ask about financial comparisons between documents.")
+            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]
+            
+            # 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])
+            analysis = llm_chain.run(
+                context=context, 
+                question=user_input
+            )
+            
+            # 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)
+
+            # 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 Comparative 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")
+
+elif len(uploaded_files) > 0:
+    st.warning("Please upload exactly 2 financial documents for comparative analysis")
+else:
     st.info("Please upload 2 PDF financial reports to begin comparative analysis")