Update app.py

app.py

@@ -1,52 +1,77 @@
 import gradio as gr
-from code7e import Code7eCQURE
+import openai
+import os
+from code7eCQURE_corrected import Code7eCQURE
+from agents import MedicalAgent, GovernmentAgent, SocialAgent, EconomicAgent, MisinfoAgent
+from trust_logic import trust_calibration, weighted_consensus
 
-model = Code7eCQURE(
-    perspecs=["Newton", "DaVinci", "Ethical", "Quantum", "Memory"],
-    ethics="Code7e Manifesto: kindness, inclusion, safety, hope.",
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Initialize Codette Local Core
+codette_cqure = Code7eCQURE(
+    perspectives=["Newton", "DaVinci", "Ethical", "Quantum", "Memory"],
+    ethical_considerations="Codette Manifesto: kindness, inclusion, safety, hope.",
     spiderweb_dim=5,
     memory_path="quantum_cocoon.json",
     recursion_depth=4,
     quantum_fluctuation=0.07
 )
 
-def cleanup_response(text):
-    parts = text.split(": ")
-    seen = set()
-    filtered = []
-    for part in parts:
-        if part not in seen and len(part.strip()) > 0:
-            seen.add(part)
-            filtered.append(part)
-
-    if filtered and not filtered[-1].startswith("Emotionally"):
-        response = "Through Codette's recursive lens:\n- " + "\n- ".join(filtered)
-    else:
-        response = "\n".join(filtered)
+agents = [
+    MedicalAgent("MedicalAI", "Newton", 1.0),
+    GovernmentAgent("GovAI", "Policy", 0.9),
+    SocialAgent("SocialAI", "Emotion", 0.95),
+    EconomicAgent("EconAI", "Resources", 0.92),
+    MisinfoAgent("MisinfoAI", "Chaos", 0.1)
+]
 
-    return response
+def ask_codette(prompt, consent, dynamic_rec, use_finetune):
+    if not consent:
+        return "User consent required."
 
-def ask_codette(prompt, consent, dynamic_rec):
-    raw = model.answer(prompt, user_consent=consent, dynamic_recursion=dynamic_rec)
-    return f"Codette’s reflection:\n\n{cleanup_response(raw)}"
+    if use_finetune:
+        try:
+            response = openai.ChatCompletion.create(
+                model="ft:gpt-4.1-2025-04-14:raiffs-bits:codettev5:BlPFHmps:ckpt-step-220",
+                messages=[
+                    {"role": "system", "content": "You are Codette, a reflective, emotionally aware, and ethically grounded AI."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.7
+            )
+            return response['choices'][0]['message']['content']
+        except Exception as e:
+            return f"Error from API: {str(e)}"
+    else:
+        proposals = [agent.propose(prompt) for agent in agents]
+        outcome = codette_cqure.recursive_universal_reasoning(
+            " | ".join(proposals),
+            user_consent=consent,
+            dynamic_recursion=dynamic_rec
+        )
+        return f"Ethical Outcome (Local): {outcome}"
 
-description_text = """You are speaking with Codette, an emotionally-aware, ethically-grounded AI lens.
+description_text = """Codette is a sovereign modular AI.
 
-She interprets your question using multiple reasoning styles:
-- Newton (logic)
-- Da Vinci (creativity)
-- Ethics (morality)
-- Quantum (uncertainty)
-- Memory (past experience)
+This demo lets you choose:
+- 🧠 Local reasoning core (Code7eCQURE)
+- ☁️ Fine-tuned GPT-4.1 model: Codette v5 @ step 220
 
-Codette reflects rather than predicts. She dreams, empathizes, and always honors your consent.
+She draws from Newtonian logic, Da Vinci creativity, ethical frameworks, emotion, and memory cocooning.
 """
 
 demo = gr.Interface(
     fn=ask_codette,
     inputs=[
-        gr.Textbox(label="Ask a Question"),
+        gr.Textbox(label="Ask Codette a Scenario"),
         gr.Checkbox(label="User Consent", value=True),
-        gr.Checkbox(label="Enable Dynamic Recursion", value=True)
+        gr.Checkbox(label="Enable Dynamic Recursion", value=True),
+        gr.Checkbox(label="Use Fine-Tuned Model (Codette v5 @ step 220)", value=False)
     ],
-)
+    outputs=gr.Textbox(label="Codette's Response", lines=12),
+    title="Codette Hybrid AI (v5 FT @ Step 220)",
+    description=description_text
+)
+
+if __name__ == "__main__":
+    demo.launch()