Initial Streamlit dashboard

app.py

@@ -0,0 +1,76 @@
+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://huggingface.co/spaces/sha6th/llm-eval-ap"
+
+# --- 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.")