Fuse stream and direct run calls (#296)

* fuse stream and non stream calls

src/smolagents/agents.py

@@ -15,9 +15,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import time
+from collections import deque
 from dataclasses import dataclass
 from enum import IntEnum
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
 
 from rich import box
 from rich.console import Console, Group
@@ -498,13 +499,17 @@ You have been provided with these additional arguments, that you can access usin
             return result
 
         if stream:
-            return self.stream_run(self.task)
+            # The steps are returned as they are executed through a generator to iterate on.
-        else:
+            return self._run(task=self.task)
-            return self.direct_run(self.task)
+        # Outputs are returned only at the end as a string. We only look at the last step
+        return deque(self._run(task=self.task), maxlen=1)[0]
 
-    def stream_run(self, task: str):
+    def _run(self, task: str) -> Generator[str, None, None]:
         """
-        Runs the agent in streaming mode, yielding steps as they are executed: should be launched only in the `run` method.
+        Runs the agent in streaming mode and returns a generator of all the steps.
+
+        Args:
+            task (`str`): The task to perform.
         """
         final_answer = None
         self.step_number = 0
@@ -555,59 +560,7 @@ You have been provided with these additional arguments, that you can access usin
 
         yield handle_agent_output_types(final_answer)
 
-    def direct_run(self, task: str):
+    def planning_step(self, task, is_first_step: bool, step: int) -> None:
-        """
-        Runs the agent in direct mode, returning outputs only at the end: should be launched only in the `run` method.
-        """
-        final_answer = None
-        self.step_number = 0
-        while final_answer is None and self.step_number < self.max_steps:
-            step_start_time = time.time()
-            step_log = ActionStep(step=self.step_number, start_time=step_start_time)
-            try:
-                if self.planning_interval is not None and self.step_number % self.planning_interval == 0:
-                    self.planning_step(
-                        task,
-                        is_first_step=(self.step_number == 0),
-                        step=self.step_number,
-                    )
-                self.logger.log(
-                    Rule(
-                        f"[bold]Step {self.step_number}",
-                        characters="━",
-                        style=YELLOW_HEX,
-                    ),
-                    level=LogLevel.INFO,
-                )
-
-                # Run one step!
-                final_answer = self.step(step_log)
-
-            except AgentError as e:
-                step_log.error = e
-            finally:
-                step_end_time = time.time()
-                step_log.end_time = step_end_time
-                step_log.duration = step_end_time - step_start_time
-                self.logs.append(step_log)
-                for callback in self.step_callbacks:
-                    callback(step_log)
-                self.step_number += 1
-
-        if final_answer is None and self.step_number == self.max_steps:
-            error_message = "Reached max steps."
-            final_step_log = ActionStep(error=AgentMaxStepsError(error_message))
-            self.logs.append(final_step_log)
-            final_answer = self.provide_final_answer(task)
-            self.logger.log(Text(f"Final answer: {final_answer}"), level=LogLevel.INFO)
-            final_step_log.action_output = final_answer
-            final_step_log.duration = 0
-            for callback in self.step_callbacks:
-                callback(final_step_log)
-
-        return handle_agent_output_types(final_answer)
-
-    def planning_step(self, task, is_first_step: bool, step: int):
         """
         Used periodically by the agent to plan the next steps to reach the objective.