Update app.py

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import time
+import sys
+import os
+import json
+
+# Local imports
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+from utils.chunking import smart_chunk_text
+from utils.retriever import HybridRetriever
+from utils.generator import generate_answer
+from utils.evaluation import evaluate_response
+from utils.guardrails import validate_input, validate_output
+
+
+# ---------------------------
+# Streamlit Page Config
+# ---------------------------
+st.set_page_config(page_title="Allstate Financial QA")
+st.title("📊 Allstate Financial QA System")
+
+
+# ---------------------------
+# Cached Loaders
+# ---------------------------
+@st.cache_resource
+def load_retriever():
+    texts = []
+    for file in os.listdir("data/processed"):
+        if file.endswith(".txt") or file.endswith(".json"):
+            with open(os.path.join("data/processed", file), "r") as f:
+                texts.append(f.read())
+    chunks = smart_chunk_text(texts, chunk_size=100)
+    embedder = SentenceTransformer("all-MiniLM-L6-v2")
+    return HybridRetriever(chunks, embedder)
+
+
+@st.cache_resource
+def load_finetuned_model():
+    tokenizer = AutoTokenizer.from_pretrained("jayyd/financial-qa-model")
+    model = AutoModelForCausalLM.from_pretrained("jayyd/financial-qa-model")
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    return pipe
+
+
+# ---------------------------
+# UI Inputs
+# ---------------------------
+query = st.text_input("Ask a financial question")
+method = st.radio("Choose method:", ["RAG", "Fine-Tuned"])
+
+
+# ---------------------------
+# Main App Logic
+# ---------------------------
+if query:
+    # Validate input
+    is_valid, message = validate_input(query)
+    if not is_valid:
+        st.error(message)
+        st.stop()
+
+    start_time = time.time()
+
+    if method == "RAG":
+        with st.spinner("Retrieving and generating answer..."):
+            retriever = load_retriever()
+            chunks = retriever.retrieve(query)
+            # Extractive only
+            answer, supporting_context = generate_answer(query, chunks)
+
+            # Evaluate
+            metrics = evaluate_response(query, answer, chunks)
+            confidence = metrics.get("confidence", 0.0)
+            is_valid, message = validate_output(answer, confidence)
+
+        # Show answer
+        st.subheader("Answer")
+        st.write(answer)
+
+        if not is_valid:
+            st.warning(message)
+
+        # Supporting context (expandable)
+        with st.expander("Supporting Context"):
+            st.write(supporting_context)
+
+        # Sidebar metrics
+        response_time = time.time() - start_time
+        st.sidebar.markdown("### Response Metrics")
+        st.sidebar.markdown(f"Response Time: {response_time:.2f}s")
+        st.sidebar.markdown(f"Confidence Score: {confidence:.2f}")
+        st.sidebar.markdown(f"Number of Retrieved Chunks: {metrics.get('num_chunks', 0)}")
+        st.sidebar.markdown(f"Chunk Relevance Score: {metrics.get('chunk_relevance', 0):.2f}")
+
+    else:
+        with st.spinner("Generating answer from fine-tuned model..."):
+            pipe = load_finetuned_model()
+            prompt = f"Q: {query}\nA:"
+            raw_output = pipe(prompt, max_new_tokens=100)[0]["generated_text"]
+
+            # Clean the output: remove prompt part
+            output = raw_output.split("A:")[-1].strip()
+
+            # Evaluate
+            metrics = evaluate_response(query, output)
+            confidence = metrics.get("confidence", 0.0)
+            is_valid, message = validate_output(output, confidence)
+
+        # Show answer
+        st.subheader("Answer")
+        st.write(output)
+
+        if not is_valid:
+            st.warning(message)
+
+        # Sidebar metrics
+        response_time = time.time() - start_time
+        st.sidebar.markdown("### Response Metrics")
+        st.sidebar.markdown(f"Response Time: {response_time:.2f}s")
+        st.sidebar.markdown(f"Confidence Score: {confidence:.2f}")
+        st.sidebar.markdown(f"Answer Length: {metrics.get('answer_length', 0)} words")