Create components/plan_generator.py

components/plan_generator.py

@@ -1,37 +1,41 @@
 # components/plan_generator.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 langchain_core.output_parsers import StrOutputParser
-import os
+from utils.session import get_canvas_data
 from utils.prompts import STRATEGY_PROMPT
 from utils.helpers import generate_pdf
 
+
 def run_plan_generator():
-    st.header("📑 Business Plan & Pitch Generator")
+    st.header("📑 Auto Plan Generator")
 
-    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 plan using your Business Model Canvas answers...")
+    # Display Canvas Summary
+    st.subheader("🧾 Your Canvas Summary")
+    for section, content in canvas.items():
+        st.markdown(f"**{section}**")
+        st.info(content)
 
-    with st.spinner("Thinking like a startup advisor..."):
-        llm = ChatGroq(
-            temperature=0.3,
-            model_name="llama3-70b-8192",
-            groq_api_key=os.getenv("GROQ_API_KEY")
+    with st.spinner("Generating business plan..."):
+        prompt = ChatPromptTemplate.from_template(
+            STRATEGY_PROMPT + "\n\nCanvas Data:\n{input}"
         )
-
-        prompt = ChatPromptTemplate.from_template(STRATEGY_PROMPT)
-        chain = prompt | llm | StrOutputParser()
-
-        full_plan = chain.invoke({"input": st.session_state.bmc_answers})
-
-    st.subheader("📘 Business Plan")
-    st.write(full_plan)
-
-    if st.button("📤 Export as PDF"):
-        generate_pdf("business_plan", full_plan)
-        st.success("✅ PDF Exported Successfully!")
+        chain: Runnable = prompt | ChatGroq(model="llama3-8b-8192", temperature=0.4)
+        full_canvas_text = "\n".join([f"{k}: {v}" for k, v in canvas.items()])
+        result = chain.invoke({"input": full_canvas_text})
+
+        st.subheader("📄 Generated Business Plan")
+        st.success(result.content)
+
+        # Allow download
+        pdf_path = generate_pdf(result.content , content)
+        with open(pdf_path, "rb") as f:
+            st.download_button("📥 Download as PDF", f, file_name="Business_Plan.pdf")
+