Create validator.py

components/validator.py

@@ -1,22 +1,36 @@
+# components/validator.py
+
 import streamlit as st
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable
 from langchain_groq import ChatGroq
-from langchain.prompts import ChatPromptTemplate
-from utils.prompts import VALIDATOR_PROMPT
 from utils.session import get_canvas_data
+from utils.prompts import VALIDATOR_PROMPT
+
 
 def run_validator():
-    st.header("✅ Validate Canvas")
+    st.header("✅ Validate Your Canvas")
 
-    canvas_data = get_canvas_data()
-    if not canvas_data or any(not v.strip() for v in canvas_data.values()):
+    canvas = get_canvas_data()
+    if not canvas:
         st.warning("Please complete the Canvas Assistant first.")
         return
 
-    if st.button("Validate"):
-        with st.spinner("Validating canvas..."):
-            prompt = ChatPromptTemplate.from_template(VALIDATOR_PROMPT)
-            chain = prompt | ChatGroq(model="llama3-8b-8192", temperature=0.3)
-            full_canvas = "\n".join([f"{k}: {v}" for k, v in canvas_data.items()])
-            result = chain.invoke({"input": full_canvas})
-            st.subheader("Validation Results")
-            st.markdown(result.content)
+    # Display user input for reference
+    st.subheader("🧾 Your Canvas Summary")
+    for section, content in canvas.items():
+        st.markdown(f"**{section}**")
+        st.info(content)
+
+    with st.spinner("Analyzing your canvas..."):
+        # Prompt and chain setup
+        prompt = ChatPromptTemplate.from_template(
+            VALIDATOR_PROMPT + "\n\nCanvas Data:\n{input}"
+        )
+
+        chain: Runnable = prompt | ChatGroq(model="llama3-8b-8192", temperature=0.3)
+        full_canvas_text = "\n".join([f"{k}: {v}" for k, v in canvas.items()])
+        validation_result = chain.invoke({"input": full_canvas_text})
+
+        st.subheader("📊 Validation Result")
+        st.success(validation_result.content)