Update app.py

app.py

@@ -5,9 +5,8 @@ import retrain
 import pandas as pd
 import os
 
-# 🔍 Advisor core logic
-def advisor_interface(query, context, feedback_choice):
-    evo_output, evo_reasoning = get_evo_response(query, context, enable_search=True)
+def advisor_interface(query, context, file, feedback_choice):
+    evo_output, evo_reasoning = get_evo_response(query, context, file)
     gpt_output = get_gpt_response(query, context)
 
     if feedback_choice != "No feedback":
@@ -16,44 +15,43 @@ def advisor_interface(query, context, feedback_choice):
 
     return evo_reasoning, gpt_output, load_history()
 
-# 🔁 Retrain logic
 def retrain_evo():
     retrain.fine_tune_on_feedback()
     return "✅ Evo retrained on feedback.", load_history()
 
-# 📜 Feedback log viewer
 def load_history():
     if os.path.exists("feedback_log.csv"):
         df = pd.read_csv("feedback_log.csv")
         return df.tail(10).to_markdown(index=False)
     return "No history available yet."
 
-# 🧠 UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 EvoRAG – Retrieval-Augmented Adaptive AI for Finance")
+    gr.Markdown("## 🧠 EvoRAG – Retrieval-Augmented Adaptive AI (General Purpose)")
 
-    with gr.Accordion("📌 Ask a Financial Question", open=True):
-        with gr.Row():
-            query = gr.Textbox(label="📝 Question", placeholder="e.g. Should we reduce exposure to Fund A?")
-        with gr.Row():
-            context = gr.Textbox(label="📂 Memo, Policy, or News Context", placeholder="e.g. Tech Fund A underperformed by 3.2%...")
+    with gr.Row():
+        query = gr.Textbox(label="📝 Ask any question", placeholder="e.g. What are the effects of inflation?")
+        context = gr.Textbox(label="📂 Optional background notes or memos", placeholder="Paste policy, notes, or ideas...")
+
+    with gr.Row():
+        file = gr.File(label="📎 Upload optional file (.txt or .pdf)", file_types=[".txt", ".pdf"])
 
     with gr.Row():
-        feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful", "No feedback"], label="📊 Was Evo’s answer useful?", value="No feedback")
+        feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful", "No feedback"], label="Was Evo’s answer useful?", value="No feedback")
 
     with gr.Row():
-        evo_out = gr.Textbox(label="🔬 EvoRAG Suggestion (with reasoning)", lines=4)
-        gpt_out = gr.Textbox(label="🤖 GPT-3.5 Suggestion", lines=4)
+        evo_out = gr.Textbox(label="🔬 EvoRAG Suggestion (with reasoning)", lines=6)
+        gpt_out = gr.Textbox(label="🤖 GPT-3.5 Suggestion", lines=6)
+
+    run_button = gr.Button("Run Advisors")
+    history_output = gr.Textbox(label="📜 Recent History")
 
-    run_button = gr.Button("🚀 Run Advisors")
-    run_button.click(fn=advisor_interface, inputs=[query, context, feedback], outputs=[evo_out, gpt_out, gr.Textbox(label="📜 Recent Feedback Log")])
+    run_button.click(fn=advisor_interface, inputs=[query, context, file, feedback], outputs=[evo_out, gpt_out, history_output])
 
     gr.Markdown("---")
+    gr.Markdown("### 🔁 Retrain Evo on Feedback")
+    retrain_button = gr.Button("📚 Retrain Evo")
+    retrain_status = gr.Textbox(label="🛠️ Retrain Status")
 
-    with gr.Accordion("🔁 Evo Live Retraining", open=False):
-        retrain_button = gr.Button("📚 Retrain Evo Now from Feedback")
-        retrain_output = gr.Textbox(label="🛠️ Retrain Status", interactive=False)
-        history_output = gr.Textbox(label="📜 Recent Feedback Log", interactive=False)
-        retrain_button.click(fn=retrain_evo, inputs=[], outputs=[retrain_output, history_output])
+    retrain_button.click(fn=retrain_evo, inputs=[], outputs=[retrain_status, history_output])
 
 demo.launch()