Create components/decision_engine.py

components/decision_engine.py

@@ -1,32 +1,34 @@
 # components/decision_engine.py
 
 import streamlit as st
-import os
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable
 from langchain_groq import ChatGroq
-from langchain.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
+from utils.session import get_canvas_data
 from utils.prompts import DECISION_ENGINE_PROMPT
 
+
 def run_decision_engine():
     st.header("🎯 Strategy Suggestions")
 
-    if "bmc_answers" not in st.session_state:
-        st.warning("⚠️ Please complete the Canvas Assistant first.")
+    canvas = get_canvas_data()
+    if not canvas:
+        st.warning("Please complete the Canvas Assistant first.")
         return
 
-    st.markdown("Generating AI-powered business recommendations based on your canvas...")
+    # Show canvas summary
+    st.subheader("📋 Canvas Overview")
+    for section, content in canvas.items():
+        st.markdown(f"**{section}**")
+        st.info(content)
 
-    with st.spinner("Analyzing strategic opportunities..."):
-        llm = ChatGroq(
-            temperature=0.4,
-            model_name="llama3-70b-8192",
-            groq_api_key=os.getenv("GROQ_API_KEY")
+    with st.spinner("Analyzing canvas for strategic insights..."):
+        prompt = ChatPromptTemplate.from_template(
+            DECISION_ENGINE_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()])
+        result = chain.invoke({"input": full_canvas_text})
 
-        prompt = ChatPromptTemplate.from_template(DECISION_ENGINE_PROMPT)
-        chain = prompt | llm | StrOutputParser()
-
-        recommendations = chain.invoke({"input": st.session_state.bmc_answers})
-
-    st.subheader("🧠 Recommended Actions")
-    st.write(recommendations)
+        st.subheader("🧠 AI Strategy Suggestions")
+        st.success(result.content)