Update app.py

app.py

@@ -179,19 +179,6 @@ if query:
     with st.spinner("🔄 Retrieving relevant context..."):
         retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
         contexts = retriever.invoke(query)
-        # Debugging: Check what was retrieved
-        #st.write("Retrieved Contexts:", contexts)
-        #st.write("Number of Contexts:", len(contexts)
-        
-        context = [d.page_content for d in contexts]
-        # Debugging: Check extracted context
-        #st.write("Extracted Context (page_content):", context)
-        #st.write("Number of Extracted Contexts:", len(context))
-        
-        #st.write("📄 **Relevant Passages Retrieved:**")
-        #for idx, doc in enumerate(contexts):
-            #st.write(f"**Excerpt {idx+1}:** {doc.page_content}")
-
         
         relevancy_prompt = """You are an expert judge tasked with evaluating whether the  EACH OF THE CONTEXT provided in the CONTEXT LIST is self sufficient to answer the QUERY asked.
         Analyze the provided QUERY AND  CONTEXT to determine if each Ccontent in the CONTEXT LIST contains Relevant information to answer the QUERY.
@@ -207,7 +194,7 @@ if query:
         - 1: The content does not has the necessary information to answer the QUERY
         
         
-EXAMPLE:
+        EXAMPLE:
         INPUT (for context only, not to be used for faithfulness evaluation):
         What is the capital of France?