Update app.py

app.py

@@ -4,26 +4,32 @@ from retriever import build_index_from_file
 from logger import log_feedback
 
 def advisor_interface(query, file, feedback_choice):
-    # 🧾 Build RAG index if a file is uploaded
+    # Build FAISS index from uploaded file
     if file is not None:
         build_index_from_file(file.name)
 
-    # 🧠 Get EvoRAG + GPT responses
+    # Get Evo + GPT responses
     evo_output = evo_rag_response(query)
-    gpt_output = get_gpt_response(query, "")  # GPT optional context
+    gpt_output = get_gpt_response(query, "")  # optional context
 
-    # 🗳️ Log feedback
+    # Log feedback
     if feedback_choice != "No feedback":
-        log_feedback(query, "[RAG context]", evo_output, feedback_choice)
+        log_feedback(query, "[RAG+WEB context]", evo_output, feedback_choice)
 
     return evo_output, gpt_output
 
+# Manual retrain trigger
+def retrain_evo():
+    import retrain
+    retrain.fine_tune_on_feedback()
+    return "✅ Evo retrained on feedback."
+
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 EvoRAG – Retrieval-Augmented Adaptive AI for Finance")
+    gr.Markdown("## 🧠 EvoRAG+ – Retrieval-Augmented Adaptive AI for Finance")
 
     with gr.Row():
-        query = gr.Textbox(label="📝 Ask a financial question", placeholder="e.g. Should we reduce exposure to Fund A?")
-        file = gr.File(label="📂 Upload policy or memo (.pdf or .txt)", file_types=[".pdf", ".txt"])
+        query = gr.Textbox(label="📝 Ask a financial question", placeholder="e.g. Option 1: Reduce exposure to Fund A. Option 2: Maintain allocation.")
+        file = gr.File(label="📂 Upload memo or policy (.pdf or .txt)", file_types=[".pdf", ".txt"])
 
     feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful", "No feedback"], label="Was Evo’s answer useful?", value="No feedback")
 
@@ -34,4 +40,10 @@ with gr.Blocks() as demo:
     submit_btn = gr.Button("Run Advisors")
     submit_btn.click(fn=advisor_interface, inputs=[query, file, feedback], outputs=[evo_out, gpt_out])
 
+    gr.Markdown("---")
+
+    retrain_btn = gr.Button("🔁 Retrain Evo from Feedback")
+    retrain_status = gr.Textbox(label="Retraining Status")
+    retrain_btn.click(fn=retrain_evo, outputs=retrain_status)
+
 demo.launch()