Create components/validator.py

components/validator.py

@@ -1,33 +1,36 @@
 # components/validator.py
 
 import streamlit as st
-from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable
 from langchain_groq import ChatGroq
-from utils.session import load_from_json, save_to_json
+from utils.session import get_canvas_data
+from utils.prompts import VALIDATOR_PROMPT
+
 
 def run_validator():
-    st.subheader("✅ Validate Your Canvas Model")
-    canvas_data = load_from_json("canvas_data")
+    st.header("✅ Validate Your Canvas")
 
-    if not canvas_data:
-        st.warning("⚠️ Please complete the Canvas Assistant first.")
+    canvas = get_canvas_data()
+    if not canvas:
+        st.warning("Please complete the Canvas Assistant first.")
         return
 
-    with st.spinner("Analyzing your business model..."):
-        prompt = ChatPromptTemplate.from_template("""
-        You are an expert startup evaluator. Evaluate the following business model canvas:
-        {canvas_data}
-        For each of the 9 blocks, provide feedback on:
-        1. Clarity
-        2. Feasibility
-        3. Strategic alignment
-        End with an overall rating from 1 to 10 and suggest 3 improvements.
-        """)
-        chain = prompt | ChatGroq(model="llama3-70b-8192", temperature=0.3) | StrOutputParser()
-        result = chain.invoke({"canvas_data": canvas_data})
-        save_to_json("canvas_feedback", result)
-
-    st.success("✅ Canvas evaluated successfully.")
-    st.markdown("### 📊 Feedback Summary")
-    st.markdown(result)
+    # 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)