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| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| st.set_page_config(page_title="LLM Hallucination Detector", layout="wide") | |
| st.title("🔍 LLM Evaluation & Hallucination Detection Framework") | |
| API_URL= "https://sha6th-llm-eval-ap.hf.space" | |
| # --- Tabs --- | |
| tab1, tab2 = st.tabs(["Evaluate New Response", "History"]) | |
| # ---------------- TAB 1: Evaluate ---------------- | |
| with tab1: | |
| st.subheader("Evaluate an LLM Response") | |
| context = st.text_area("Context (Ground Truth)", height=100) | |
| question = st.text_input("Question") | |
| llm_response = st.text_area("LLM Response", height=100) | |
| if st.button("Evaluate"): | |
| if not context.strip() or not question.strip() or not llm_response.strip(): | |
| st.error("All fields are required.") | |
| else: | |
| with st.spinner("Running evaluation..."): | |
| response = requests.post(f"{API_URL}/evaluate", json={ | |
| "context": context, | |
| "question": question, | |
| "llm_response": llm_response | |
| }) | |
| if response.status_code == 200: | |
| result = response.json() | |
| verdict = result["final_verdict"] | |
| if verdict == "Hallucinated": | |
| st.error(f"**Verdict: {verdict}**") | |
| elif verdict == "Faithful": | |
| st.success(f"**Verdict: {verdict}**") | |
| else: | |
| st.warning(f"**Verdict: {verdict}**") | |
| col1, col2, col3, col4 = st.columns(4) | |
| col1.metric("Cosine (Relevance)", result["cosine"]["score"], result["cosine"]["verdict"]) | |
| col2.metric("BERTScore (Faithfulness)", result["bert_score"]["score"], result["bert_score"]["verdict"]) | |
| col3.metric("NLI", result["nli"]["score"], result["nli"]["verdict"]) | |
| col4.metric("Fluency", "-", result["fluency"]["verdict"]) | |
| st.json(result) | |
| else: | |
| st.error(f"Error: {response.json()['detail']}") | |
| # ---------------- TAB 2: History ---------------- | |
| with tab2: | |
| st.subheader("Past Evaluations") | |
| if st.button("Refresh History"): | |
| st.rerun() | |
| response = requests.get(f"{API_URL}/history") | |
| if response.status_code == 200: | |
| data = response.json() | |
| if data["total"] == 0: | |
| st.info("No evaluations yet.") | |
| else: | |
| df = pd.DataFrame(data["evaluations"]) | |
| df = df[["id", "question", "llm_response", "final_verdict", "created_at"]] | |
| st.dataframe(df, use_container_width=True) | |
| st.subheader("Verdict Distribution") | |
| verdict_counts = df["final_verdict"].value_counts() | |
| st.bar_chart(verdict_counts) | |
| else: | |
| st.error("Could not fetch history.") |