Update app.py

app.py

@@ -7,7 +7,7 @@ import dagshub
 dagshub.init(repo_owner='homuhe', repo_name='PromptPlenum-Tracking', mlflow=True)
 
 # ==========================================
-# 1. KONFIGURATION & PROMPTS (UNIVERSAL EDITION)
+# 1. KONFIGURATION & PROMPTS
 # ==========================================
 
 MODERATOR_MODEL = "meta-llama/Llama-3.3-70B-Instruct:groq"
@@ -112,7 +112,7 @@ class PromptManager:
         )
 
 # ==========================================
-# 2. CORE SERVICES (API & UI Helpers)
+# 2. CORE SERVICES
 # ==========================================
 class LLMService:
     """Kapselt die gesamte API-Kommunikation mit Hugging Face und trackt sie in MLflow Traces."""
@@ -163,7 +163,7 @@ class UIHelper:
         return f"**<span style='color: {color}; font-size: 1.1em;'>👤 {name}</span>**\n\n> {content}"
 
 # ==========================================
-# 3. DER ORCHESTRATOR (Die Business Logik)
+# 3. DER ORCHESTRATOR
 # ==========================================
 class PlenumOrchestrator:
     """Steuert den gesamten Diskussions- und Generierungsprozess."""
@@ -177,74 +177,82 @@ class PlenumOrchestrator:
             yield [{"role": "assistant", "content": "Bitte gib ein Thema oder eine Frage ein."}]
             return
 
-        history = [{"role": "user", "content": user_prompt}]
-        yield history
-        
-        discussion_history = "" 
-        
-        # --- KICK-OFF ---
-        history.append({"role": "assistant", "content": self.ui.header("🎤 MODERATOR: SITZUNGSERÖFFNUNG")})
-        yield history
-        
-        kickoff_res = self.llm.ask(
-            MODERATOR_MODEL, 
-            self.prompts.get_moderator_kickoff_sys(), 
-            f"User-Anfrage: '{user_prompt}'\nBriefe das Team."
-        )
-        discussion_history += f"Moderator (Kick-off): {kickoff_res}\n\n"
-        history.append({"role": "assistant", "content": self.ui.message("🎤 Moderator", kickoff_res, "#FF5A4D")})
-        yield history
+        with mlflow.start_span(name="Plenum_Session", span_type="CHAIN") as session_span:
+            
+            # Wir loggen den Start-Input für die Übersicht
+            session_span.set_inputs({"user_prompt": user_prompt, "rounds": rounds})
 
-        # --- ZYKLEN ---
-        for r in range(int(rounds)):
-            history.append({"role": "assistant", "content": self.ui.header(f"🔄 ZYKLUS {r+1} - EXPERTENDEBATTE", "#4241A6")})
+            history = [{"role": "user", "content": user_prompt}]
             yield history
             
-            # Moderator Steuerung (ab Runde 2)
-            if r > 0:
-                mid_res = self.llm.ask(
-                    MODERATOR_MODEL, 
-                    self.prompts.get_moderator_mid_sys(), 
-                    f"Protokoll:\n{discussion_history}\n\nGib die Anweisung."
-                )
-                discussion_history += f"Moderator (Steuerung): {mid_res}\n\n"
-                history.append({"role": "assistant", "content": self.ui.message("🎤 Moderator (Steuerung)", mid_res, "#FF5A4D")})
-                yield history
+            discussion_history = "" 
+            
+            # --- KICK-OFF ---
+            history.append({"role": "assistant", "content": self.ui.header("🎤 MODERATOR: SITZUNGSERÖFFNUNG")})
+            yield history
+            
+            kickoff_res = self.llm.ask(
+                MODERATOR_MODEL, 
+                self.prompts.get_moderator_kickoff_sys(), 
+                f"User-Anfrage: '{user_prompt}'\nBriefe das Team."
+            )
+            discussion_history += f"Moderator (Kick-off): {kickoff_res}\n\n"
+            history.append({"role": "assistant", "content": self.ui.message("🎤 Moderator", kickoff_res, "#FF5A4D")})
+            yield history
 
-            # Experten antworten
-            for name, config in COUNCIL_MEMBERS.items():
-                sys_msg = self.prompts.get_expert_sys(config["role"])
-                user_msg = self.prompts.get_expert_user_prompt(user_prompt, discussion_history, name)
-                
-                answer = self.llm.ask(config["model"], sys_msg, user_msg)
-                discussion_history += f"{name}: {answer}\n\n"
+            # --- ZYKLEN ---
+            for r in range(int(rounds)):
+                history.append({"role": "assistant", "content": self.ui.header(f"🔄 ZYKLUS {r+1} - EXPERTENDEBATTE", "#4241A6")})
+                yield history
                 
-                history.append({"role": "assistant", "content": self.ui.message(name, answer)})
-                yield history 
+                # Moderator Steuerung (ab Runde 2)
+                if r > 0:
+                    mid_res = self.llm.ask(
+                        MODERATOR_MODEL, 
+                        self.prompts.get_moderator_mid_sys(), 
+                        f"Protokoll:\n{discussion_history}\n\nGib die Anweisung."
+                    )
+                    discussion_history += f"Moderator (Steuerung): {mid_res}\n\n"
+                    history.append({"role": "assistant", "content": self.ui.message("🎤 Moderator (Steuerung)", mid_res, "#FF5A4D")})
+                    yield history
 
-        # --- ANALYSE ---
-        history.append({"role": "assistant", "content": self.ui.header("🧠 MODERATOR: ANALYSE DER DISKUSSION")})
-        yield history
-        
-        consensus_res = self.llm.ask(
-            MODERATOR_MODEL, 
-            self.prompts.get_analysis_sys(), 
-            self.prompts.get_analysis_user(discussion_history)
-        )
-        history.append({"role": "assistant", "content": f"> {consensus_res}"})
-        yield history
+                # Experten antworten
+                for name, config in COUNCIL_MEMBERS.items():
+                    sys_msg = self.prompts.get_expert_sys(config["role"])
+                    user_msg = self.prompts.get_expert_user_prompt(user_prompt, discussion_history, name)
+                    
+                    answer = self.llm.ask(config["model"], sys_msg, user_msg)
+                    discussion_history += f"{name}: {answer}\n\n"
+                    
+                    history.append({"role": "assistant", "content": self.ui.message(name, answer)})
+                    yield history 
 
-        # --- FINALE AUSGABE ---
-        history.append({"role": "assistant", "content": self.ui.header("🏆 FINALE AUSGABE")})
-        yield history
-        
-        final_res = self.llm.ask(
-            MODERATOR_MODEL, 
-            self.prompts.get_final_sys(), 
-            self.prompts.get_final_user(user_prompt, consensus_res)
-        )
-        history.append({"role": "assistant", "content": final_res})
-        yield history
+            # --- ANALYSE ---
+            history.append({"role": "assistant", "content": self.ui.header("🧠 MODERATOR: ANALYSE DER DISKUSSION")})
+            yield history
+            
+            consensus_res = self.llm.ask(
+                MODERATOR_MODEL, 
+                self.prompts.get_analysis_sys(), 
+                self.prompts.get_analysis_user(discussion_history)
+            )
+            history.append({"role": "assistant", "content": f"> {consensus_res}"})
+            yield history
+
+            # --- FINALE AUSGABE ---
+            history.append({"role": "assistant", "content": self.ui.header("🏆 FINALE AUSGABE")})
+            yield history
+            
+            final_res = self.llm.ask(
+                MODERATOR_MODEL, 
+                self.prompts.get_final_sys(), 
+                self.prompts.get_final_user(user_prompt, consensus_res)
+            )
+            history.append({"role": "assistant", "content": final_res})
+            yield history
+
+            # Wir loggen das Endergebnis in den Parent-Span
+            session_span.set_outputs({"final_result": final_res})
 
 # Instanziiere den Orchestrator
 orchestrator = PlenumOrchestrator()