Update app.py

app.py

@@ -1,66 +1,67 @@
-# app.py
-
+import os
 import gradio as gr
-from inference import get_evo_response, get_gpt_response
 import pandas as pd
-import os
-
-LOG_PATH = "logs/feedback_log.csv"
-os.makedirs(os.path.dirname(LOG_PATH), exist_ok=True)
-if not os.path.exists(LOG_PATH):
-    pd.DataFrame(columns=["question", "context", "option1", "option2", "evo_answer", "feedback"]).to_csv(LOG_PATH, index=False)
-
-def advisor_interface(question, context, options_text, feedback=None):
-    options = [o.strip() for o in options_text.split("\n") if o.strip()]
-    if len(options) != 2:
-        return "Please enter exactly two options (one per line).", "", ""
+from inference import get_evo_response, get_gpt_response
 
-    option1, option2 = options
-    evo_answer, confidence, s1, s2 = get_evo_response(question, option1, option2)
-    gpt_output = get_gpt_response(question, option1, option2)
+LOG_PATH = "feedback_log.csv"
+if os.path.dirname(LOG_PATH):
+    os.makedirs(os.path.dirname(LOG_PATH), exist_ok=True)
 
-    # Log feedback if given
-    if feedback:
-        df = pd.read_csv(LOG_PATH)
-        df = df.append({
-            "question": question,
-            "context": context,
-            "option1": option1,
-            "option2": option2,
-            "evo_answer": evo_answer,
-            "feedback": feedback
-        }, ignore_index=True)
+def log_feedback(question, context, evo_answer, feedback):
+    df = pd.DataFrame([[question, context, evo_answer, feedback]],
+                      columns=["question", "context", "evo_answer", "feedback"])
+    if os.path.exists(LOG_PATH):
+        df.to_csv(LOG_PATH, mode="a", header=False, index=False)
+    else:
         df.to_csv(LOG_PATH, index=False)
 
-    evo_msg = f"Evo suggests: **{evo_answer}** (Confidence: {confidence:.2f})"
-    context_used = f"Option 1 Score: {s1:.2f}\nOption 2 Score: {s2:.2f}"
-    return evo_msg + "\n\n" + context_used, gpt_output, load_history()
-
 def load_history():
-    df = pd.read_csv(LOG_PATH)
-    if df.empty:
+    if not os.path.exists(LOG_PATH):
         return "No feedback yet."
+    df = pd.read_csv(LOG_PATH)
     return df.tail(10).to_markdown(index=False)
 
-with gr.Blocks() as demo:
-    gr.Markdown("🧠 **EvoRAG – General-Purpose Adaptive AI with Web Reasoning**")
+def advisor_interface(query, context, options_input):
+    options = [opt.strip() for opt in options_input.strip().split("\n") if opt.strip()]
+    if len(options) != 2:
+        return "Please enter exactly two options (one per line).", "", ""
+
+    evo_answer, evo_reasoning, evo_conf, evo_context = get_evo_response(query, options, context)
+    gpt_answer = get_gpt_response(query, context)
+    history = load_history()
+    return (
+        f"### Evo Suggestion\n**Answer**: {evo_answer} (Confidence: {evo_conf:.2f})\n\n**Reasoning**: {evo_reasoning}\n\n_Context used:_\n{evo_context}",
+        f"### GPT-3.5 Suggestion\n{gpt_answer}",
+        history
+    )
+
+def feedback_interface(question, context, evo_answer, feedback):
+    log_feedback(question, context, evo_answer, feedback)
+    return "✅ Feedback logged."
+
+with gr.Blocks(title="EvoRAG – General-Purpose Adaptive AI with Web Reasoning") as demo:
+    gr.Markdown("## 🧠 EvoRAG – General-Purpose Adaptive AI with Web Reasoning")
 
     with gr.Row():
         with gr.Column():
-            question = gr.Textbox(label="📝 Ask anything")
-            context = gr.Textbox(label="📂 Optional Context or Notes", lines=2)
-            options = gr.Textbox(label="🧠 Options (Enter two options, one per line)", placeholder="Option 1\nOption 2")
-            run_btn = gr.Button("🔍 Run Advisors")
+            query = gr.Textbox(label="📝 Ask anything", placeholder="e.g. Who is the president of the US?")
+            context = gr.Textbox(label="📂 Optional Context or Notes", lines=3, placeholder="Paste extra info or leave blank")
+            options = gr.Textbox(label="🧠 Options (2 lines)", lines=2, placeholder="Option 1\nOption 2")
+            submit = gr.Button("🔍 Run Advisors")
+
+        with gr.Column():
+            evo_output = gr.Markdown()
+            gpt_output = gr.Markdown()
+
+    with gr.Row():
+        feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful"], label="Was Evo’s answer useful?")
+        submit_feedback = gr.Button("📬 Submit Feedback")
+        feedback_status = gr.Textbox(interactive=False)
 
-            feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful", "No feedback"], label="Was Evo’s answer useful?")
-            evo_out = gr.Markdown()
-            gpt_out = gr.Markdown()
-            history = gr.Markdown()
+    with gr.Accordion("📜 Recent History", open=False):
+        history_display = gr.Markdown()
 
-        run_btn.click(
-            advisor_interface,
-            inputs=[question, context, options, feedback],
-            outputs=[evo_out, gpt_out, history]
-        )
+    submit.click(fn=advisor_interface, inputs=[query, context, options], outputs=[evo_output, gpt_output, history_display])
+    submit_feedback.click(fn=feedback_interface, inputs=[query, context, evo_output, feedback], outputs=[feedback_status])
 
 demo.launch()