Update src/streamlit_app.py

src/streamlit_app.py

@@ -24,6 +24,12 @@ from sklearn.metrics import mean_squared_error, r2_score
 
 # SHAP
 import shap
+if "llm_result" not in st.session_state:
+    st.session_state["llm_result"] = None
+if "automl_summary" not in st.session_state:
+    st.session_state["automl_summary"] = {}
+if "shap_recommendations" not in st.session_state:
+    st.session_state["shap_recommendations"] = []
 
 
 # -------------------------
@@ -954,7 +960,9 @@ with tabs[4]:
                                     tokenizer = AutoTokenizer.from_pretrained(tiny_llm_path)
                                     assistant = pipeline("text-generation", model=model, tokenizer=tokenizer)
                                 else:
-                                    assistant = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+                                    assistant = pipeline("text2text-generation", model="google/flan-t5-small")
+
+
                 
                                 llm_prompt = f"""
                                 You are a metallurgical process advisor working in a steel manufacturing unit.
@@ -964,7 +972,7 @@ with tabs[4]:
                                 {deltas.to_dict(orient='index')}
                                 Write a concise 3-line message to the operator suggesting what to adjust this shift.
                                 """
-                                resp = assistant(llm_prompt, max_new_tokens=80, do_sample=True, temperature=0.6)[0]["generated_text"]
+                                resp = assistant(llm_prompt, max_new_tokens=120)[0]["generated_text"]
                                 st.info(resp)
                                 log("Operator LLM advisory note generated successfully.")
                             else:
@@ -1049,40 +1057,44 @@ with tabs[4]:
                 st.subheader("AI Recommendation Assistant (in-memory mode)")
                 st.caption("Generates quick local AI suggestions — no file writes required.")
                 
+                if "llm_result" not in st.session_state:
+                    st.session_state["llm_result"] = None
+                
                 if st.button("Get AI Recommendation (tiny local LLM)", key="ai_reco"):
                     summary = st.session_state.get("automl_summary", {})
                     if not summary:
                         st.warning("Please run AutoML first to generate context.")
                         st.stop()
-                
                     try:
                         from transformers import pipeline
+                        st.info("Loading compact instruction-tuned model (in-memory)...")
                 
-                        st.info("Loading tiny model in-memory (ephemeral)...")
-                        assistant = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+                        
+                        assistant = pipeline("text2text-generation", model="google/flan-t5-small")
                 
                         prompt = f"""
-                        You are an ML model tuning assistant.
-                        Given this AutoML summary, provide 3 actionable steps for improvement
-                        if overfitting, underfitting, or data quality issues are suspected.
+                        You are an ML model tuning advisor.
+                        Based on this AutoML summary, suggest 3 concise steps to improve performance
+                        if overfitting, underfitting, or data-quality issues are seen.
                 
                         Use case: {summary.get('use_case')}
                         Target: {summary.get('target')}
                         Final R²: {summary.get('final_r2')}
                         Final RMSE: {summary.get('final_rmse')}
                         Leaderboard: {summary.get('leaderboard')}
-                
-                        Respond in concise numbered steps.
                         """
                 
-                        result = assistant(prompt, max_new_tokens=90, temperature=0.7, do_sample=True)[0]["generated_text"]
-                        st.success("LLM Recommendation:")
-                        st.markdown(result)
-                        log("Tiny LLM in-memory advisory generated successfully.")
-                
+                        result = assistant(prompt, max_new_tokens=100)[0]["generated_text"]
+                        st.session_state["llm_result"] = result
+                        log("LLM in-memory recommendation generated successfully.")
                     except Exception as e:
-                        st.error(f"LLM generation failed: {e}")
-                        st.info("Make sure `transformers` is installed in your Space environment.")
+                        st.session_state["llm_result"] = f" LLM generation failed: {e}"
+                
+                # Persist output even after rerun
+                if st.session_state["llm_result"]:
+                    st.success("AI Recommendation (cached):")
+                    st.markdown(st.session_state["llm_result"])
+
     
 # -----  Target & Business Impact tab
 with tabs[5]: