Added Evaluation to streamlit.py

streamlit.py

@@ -3,6 +3,22 @@ import os
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 import streamlit as st
 from agent.graph import app
+from agent.state import ResearchState
+from ragas import evaluate
+from ragas.metrics import faithfulness, answer_relevancy
+from datasets import Dataset
+from ragas.llms import LangchainLLMWrapper
+from ragas.embeddings import LangchainEmbeddingsWrapper
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+
+evaluator_llm = LangchainLLMWrapper(
+    ChatGroq(model="llama-3.3-70b-versatile", api_key="", n=1)
+)
+evaluator_embeddings = LangchainEmbeddingsWrapper(
+    HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+)
+
 
 st.title("AI Research Assistant")
 topic = st.text_input("Enter research topic:")
@@ -18,4 +34,21 @@ if st.button("Research"):
             "reflection_passed": False,
             "loop_count": 0
         })
-    st.markdown(result['report'])
+
+    dataset = Dataset.from_dict({
+            "question": [result['topic']],
+            "answer": [result['report']],
+            "contexts": [result['scraped_content']],
+    })
+
+    scores = evaluate(
+            dataset=dataset,
+            metrics=[faithfulness, answer_relevancy],
+            llm=evaluator_llm,
+            embeddings=evaluator_embeddings)
+
+    st.subheader("Report Quality Scores")
+    st.metric("Faithfulness", round(scores['faithfulness'][0], 2))
+    st.metric("Answer Relevancy", round(scores['answer_relevancy'][0], 2))
+
+    st.markdown(result['report'])