Create components/validator.py

components/validator.py

@@ -1,36 +1,38 @@
-# 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 utils.session import get_canvas_data
-from utils.prompts import VALIDATOR_PROMPT
-
+from langchain_core.messages import AIMessage
+from langchain_community.chat_models import ChatGroq
+from utils.prompts import VALIDATION_PROMPT
+from utils.session import get_state_value, update_state
 
 def run_validator():
-    st.header("✅ Validate Your Canvas")
+    st.title("✅ Validate Canvas")
+    st.markdown("AI will evaluate your business model for clarity and coherence.")
+
+    responses = get_state_value("canvas_responses", {})
 
-    canvas = get_canvas_data()
-    if not canvas:
+    if not responses or len(responses) < 10:
         st.warning("Please complete the Canvas Assistant first.")
         return
 
-    # 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)
+    canvas_data = ""
+    for i in range(10):
+        canvas_data += f"{responses[str(i)]}\n"
+
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", VALIDATION_PROMPT),
+        ("human", "{input}")
+    ])
+
+    chain = prompt | ChatGroq(model_name="llama3-8b-8192", temperature=0.4)
+    
+    if st.button("Validate Canvas"):
+        with st.spinner("Validating your business model..."):
+            result = chain.invoke({"input": canvas_data})
+            update_state("validation_result", result.content)
+            st.success("Canvas validated successfully!")
+
+    validation_result = get_state_value("validation_result", None)
+    if validation_result:
+        st.markdown("### 🔎 Validation Result:")
+        st.markdown(validation_result)