modify spo logger and app

metagpt/ext/spo/app.py

@@ -1,3 +1,4 @@
+from loguru import logger as _logger
 import sys
 from pathlib import Path
 
@@ -10,6 +11,7 @@ sys.path.append(str(root_path))
 
 from metagpt.ext.spo.components.optimizer import PromptOptimizer
 from metagpt.ext.spo.utils.llm_client import SPO_LLM
+from metagpt.const import METAGPT_ROOT
 
 
 def load_yaml_template(template_path):
@@ -159,34 +161,60 @@ def main():
         }
         st.code(yaml.dump(preview_data, allow_unicode=True), language="yaml")
 
-    # Start optimization button
-    if st.button("Start Optimization"):
-        try:
-            # Initialize LLM
-            SPO_LLM.initialize(
-                optimize_kwargs={"model": opt_model, "temperature": opt_temp},
-                evaluate_kwargs={"model": eval_model, "temperature": eval_temp},
-                execute_kwargs={"model": exec_model, "temperature": exec_temp},
-            )
+        # 创建一个固定的容器来显示日志
+        st.subheader("Optimization Logs")
+        log_container = st.empty()
 
-            # Create optimizer instance
-            optimizer = PromptOptimizer(
-                optimized_path="workspace",
-                initial_round=initial_round,
-                max_rounds=max_rounds,
-                template=f"{template_name}.yaml",
-                name=template_name,
-                iteration=True,
-            )
+        class StreamlitSink:
+            def write(self, message):
+                # 获取当前日志内容
+                current_logs = st.session_state.get('logs', [])
+                current_logs.append(message.strip())
+                st.session_state.logs = current_logs
 
-            # Run optimization with progress bar
-            with st.spinner("Optimizing prompts..."):
-                optimizer.optimize()
+                # 使用 code 块显示日志
+                log_container.code(
+                    "\n".join(current_logs),
+                    language="plaintext"
+                )
 
-            st.success("Optimization completed!")
+        # 配置loguru日志
+        streamlit_sink = StreamlitSink()
+        _logger.remove()
+        _logger.add(
+            streamlit_sink.write,
+            format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {name}:{function}:{line} - {message}"
+        )
+        _logger.add(METAGPT_ROOT / "logs/{time:YYYYMMDD}.txt", level="DEBUG")
+        # Start optimization button
+        if st.button("Start Optimization"):
+            try:
+                # Initialize LLM
+                SPO_LLM.initialize(
+                    optimize_kwargs={"model": opt_model, "temperature": opt_temp},
+                    evaluate_kwargs={"model": eval_model, "temperature": eval_temp},
+                    execute_kwargs={"model": exec_model, "temperature": exec_temp},
+                )
 
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}")
+                # Create optimizer instance
+                optimizer = PromptOptimizer(
+                    optimized_path="workspace",
+                    initial_round=initial_round,
+                    max_rounds=max_rounds,
+                    template=f"{template_name}.yaml",
+                    name=template_name,
+                    iteration=True,
+                )
+
+                # Run optimization with progress bar
+                with st.spinner("Optimizing prompts..."):
+                    optimizer.optimize()
+
+                st.success("Optimization completed!")
+
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+                _logger.error(f"Error during optimization: {str(e)}")
 
 
 if __name__ == "__main__":

metagpt/ext/spo/components/optimizer.py

@@ -44,13 +44,13 @@ class PromptOptimizer:
                 asyncio.set_event_loop(loop)
                 prompt = loop.run_until_complete(self._optimize_prompt())
                 self.round += 1
-                logger.info(f"Prompt generated in round {self.round}: {prompt}")
+                # logger.info(f"Prompt generated in round {self.round}: {prompt}")
 
         else:
             loop = asyncio.new_event_loop()
             asyncio.set_event_loop(loop)
             prompt = loop.run_until_complete(self._test_prompt())
-            logger.info(f"Prompt generated in round {self.round}: {prompt}")
+            # logger.info(f"Prompt generated in round {self.round}: {prompt}")
 
     async def _optimize_prompt(self):
         prompt_path = f"{self.root_path}/prompts"
@@ -59,6 +59,9 @@ class PromptOptimizer:
         data = self.data_utils.load_results(prompt_path)
 
         if self.round == 1:
+
+            logger.info("\n⚡ RUNNING Round 1 PROMPT ⚡\n")
+
             directory = self.prompt_utils.create_round_directory(prompt_path, self.round)
             # Load prompt using prompt_utils
 
@@ -77,7 +80,9 @@ class PromptOptimizer:
 
         samples = self.data_utils.get_best_round()
 
-        logger.info(f"choose {samples['round']}")
+        logger.info(f"\n🚀Round {self.round+1} OPTIMIZATION STARTING 🚀\n")
+        logger.info(f"\nSelecting prompt for round {samples['round']} and advancing to the iteration phase\n")
+
 
         golden_answer = self.data_utils.list_to_markdown(qa)
         best_answer = self.data_utils.list_to_markdown(samples["answers"])
@@ -96,7 +101,7 @@ class PromptOptimizer:
 
         modification = extract_content(response, "modification")
 
-        logger.info(f"Modification of this round: {modification}")
+        logger.info(f"Modification of {self.round+1} round: {modification}")
 
         prompt = extract_content(response, "prompt")
 
@@ -105,9 +110,16 @@ class PromptOptimizer:
         else:
             self.prompt = ""
 
+        logger.info("\n🎯 NEW PROMPT GENERATED 🎯\n")
+        logger.info(f"\nRound {self.round+1} Prompt: {self.prompt}\n")
+
         self.prompt_utils.write_prompt(directory, prompt=self.prompt)
 
-        new_samples = await self.evaluation_utils.execute_prompt(self, directory, data)
+        logger.info("\n⚡ RUNNING OPTIMIZED PROMPT ⚡\n")
+
+        new_samples = await self.evaluation_utils.execute_prompt(self, directory)
+
+        logger.info("\n📊 EVALUATING OPTIMIZED PROMPT 📊\n")
 
         success, answers = await self.evaluation_utils.evaluate_prompt(
             self, samples, new_samples, path=prompt_path, data=data, initial=False
@@ -115,9 +127,11 @@ class PromptOptimizer:
 
         self.prompt_utils.write_answers(directory, answers=answers)
 
-        logger.info(f"Current round optimization successful:{success}")
+        logger.info("\n🎯 OPTIMIZATION RESULT 🎯\n")
+        logger.info(f"\nRound {self.round + 1} Optimization: {'✅ SUCCESS' if success else '❌ FAILED'}\n")
 
-        logger.info(f"now is {self.round + 1}")
+
+        # logger.info(f"now is {self.round + 1}")
 
         return prompt