Update app.py

app.py

@@ -78,7 +78,7 @@ class Context:
         self.test_cases = test_cases
         self.test_results = test_results
         self.documentation = documentation
-        # Initialize with the user's task
+        # Initialize with the user's task.
         self.conversation_history = conversation_history or [{"agent": "User", "message": original_task}]
 
     def add_conversation_entry(self, agent_name: str, message: str):
@@ -119,7 +119,6 @@ class OrchestratorAgent:
                 )
             plan = await call_model(prompt, model="gpt-4o", api_key=api_key)
             context.add_conversation_entry("Orchestrator", f"Plan:\n{plan}")
-            # Report update after planning
             self.log_queue.put(("update", context.conversation_history))
             if "REQUEST_HUMAN_FEEDBACK" in plan:
                 question = plan.split("REQUEST_HUMAN_FEEDBACK\n", 1)[1].strip()
@@ -159,6 +158,7 @@ class CodeReviewerAgent:
         )
         review = await call_model(prompt, model="gpt-4o", api_key=api_key)
         context.add_conversation_entry("Code Reviewer", f"Review:\n{review}")
+        # If review is not "APPROVE", store the review comments.
         if "APPROVE" not in review.upper():
             structured_review = {"comments": []}
             for line in review.splitlines():
@@ -239,7 +239,7 @@ class AgentDispatcher:
             context = await self.agents[agent_name].generate_documentation(context, api_key)
         else:
             raise ValueError(f"Unknown agent: {agent_name}")
-        # After each dispatch, push an update with the current conversation history.
+        # Push an update with the current conversation history.
         self.log_queue.put(("update", context.conversation_history))
         return context
 
@@ -250,7 +250,9 @@ class AgentDispatcher:
             return "orchestrator"
         if not context.code:
             return "coder"
-        if not any("APPROVE" in comment.get("issue", "").upper() for review in context.review_comments for comment in review.get("comments", [])):
+        # Instead of checking review_comments (which may be empty if reviewer approved),
+        # check the conversation history for a Code Reviewer entry containing "APPROVE".
+        if not any("APPROVE" in entry["message"].upper() for entry in context.conversation_history if entry["agent"].lower() == "code reviewer"):
             return "code_reviewer"
         if not context.test_cases:
             return "qa_tester"
@@ -278,14 +280,15 @@ async def multi_agent_conversation(task_message: str, log_queue: queue.Queue, ap
             context = await dispatcher.dispatch(next_agent, context, api_key, model="gpt-3.5-turbo-16k")
         else:
             context = await dispatcher.dispatch(next_agent, context, api_key)
+        # Check approval by scanning the conversation history for a Code Reviewer entry that includes "APPROVE"
         if next_agent == "code_reviewer":
-            approved = any("APPROVE" in comment.get("issue", "").upper() 
-                           for review in context.review_comments 
-                           for comment in review.get("comments", []))
-            if not approved:
-                next_agent = "coder"
-            else:
+            approved = any("APPROVE" in entry["message"].upper() 
+                           for entry in context.conversation_history 
+                           if entry["agent"].lower() == "code reviewer")
+            if approved:
                 next_agent = await dispatcher.determine_next_agent(context, api_key)
+            else:
+                next_agent = "coder"
         else:
             next_agent = await dispatcher.determine_next_agent(context, api_key)
         if next_agent == "coder" and coder_iterations > 5:
@@ -298,7 +301,7 @@ def process_conversation_generator(task_message: str, api_key: str,
                                    human_event: threading.Event, human_input_queue: queue.Queue,
                                    log_queue: queue.Queue) -> Generator[Any, None, None]:
     """
-    Runs the multi-agent conversation in a background thread and yields only conversation history updates
+    Runs the multi-agent conversation in a background thread and yields conversation history updates
     in the proper messages format.
     """
     def run_conversation():
@@ -312,16 +315,14 @@ def process_conversation_generator(task_message: str, api_key: str,
             msg = log_queue.get(timeout=0.1)
             if isinstance(msg, tuple):
                 if msg[0] in ("update", "result"):
-                    # Yield the updated conversation history in proper format.
                     yield gr.update(value=convert_history(msg[1]), visible=True)
                 else:
-                    # Log messages can be printed or handled here if needed.
+                    # Optionally handle log messages here.
                     pass
         except queue.Empty:
             pass
         time.sleep(0.1)
     
-    # Final update if needed
     yield gr.update(visible=True)
 
 # -------------------- Multi-Agent Chat Function --------------------