Delete swarms/models/autotemp.py

swarms/models/autotemp.py

@@ -1,101 +0,0 @@
-import re
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from swarms.models.openai_models import OpenAIChat
-
-
-class AutoTempAgent:
-    """
-    AutoTemp is a tool for automatically selecting the best temperature setting for a given task.
-
-    Flow:
-        1. Generate outputs at a range of temperature settings.
-        2. Evaluate each output using the default temperature setting.
-        3. Select the best output based on the evaluation score.
-        4. Return the best output.
-
-
-    Args:
-        temperature (float, optional): The default temperature setting to use. Defaults to 0.5.
-        api_key (str, optional): Your OpenAI API key. Defaults to None.
-        alt_temps ([type], optional): A list of alternative temperature settings to try. Defaults to None.
-        auto_select (bool, optional): If True, the best temperature setting will be automatically selected. Defaults to True.
-        max_workers (int, optional): The maximum number of workers to use when generating outputs. Defaults to 6.
-
-    Returns:
-        [type]: [description]
-
-    Examples:
-        >>> from swarms.demos.autotemp import AutoTemp
-        >>> autotemp = AutoTemp()
-        >>> autotemp.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.", "0.4,0.6,0.8,1.0,1.2,1.4")
-        Best AutoTemp Output (Temp 0.4 | Score: 100.0):
-        Generate a 10,000 word blog on mental clarity and the benefits of meditation.
-
-    """
-
-    def __init__(
-        self,
-        temperature: float = 0.5,
-        api_key: str = None,
-        alt_temps=None,
-        auto_select=True,
-        max_workers=6,
-    ):
-        self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
-        self.auto_select = auto_select
-        self.max_workers = max_workers
-        self.temperature = temperature
-        self.alt_temps = alt_temps
-        self.llm = OpenAIChat(
-            openai_api_key=api_key,
-            temperature=temperature,
-        )
-
-    def evaluate_output(self, output: str):
-        """Evaluate the output using the default temperature setting."""
-        eval_prompt = f"""
-            Evaluate the following output which was generated at a temperature setting of {self.temperature}. 
-            Provide a precise score from 0.0 to 100.0, considering the criteria of relevance, clarity, utility, pride, and delight.
-
-            Output to evaluate:
-            ---
-            {output}
-            ---
-            """
-        score_text = self.llm(prompt=eval_prompt)
-        score_match = re.search(r"\b\d+(\.\d)?\b", score_text)
-        return round(float(score_match.group()), 1) if score_match else 0.0
-
-    def run(self, task: str, temperature_string):
-        """Run the AutoTemp agent."""
-        temperature_list = [
-            float(temp.strip()) for temp in temperature_string.split(",")
-        ]
-        outputs = {}
-        scores = {}
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            future_to_temp = {
-                executor.submit(self.llm.generate, task, temp): temp
-                for temp in temperature_list
-            }
-            for future in as_completed(future_to_temp):
-                temp = future_to_temp[future]
-                output_text = future.result()
-                outputs[temp] = output_text
-                scores[temp] = self.evaluate_output(output_text, temp)
-
-        if not scores:
-            return "No valid outputs generated.", None
-
-        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
-        best_temp, best_score = sorted_scores[0]
-        best_output = outputs[best_temp]
-
-        return (
-            f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
-            if self.auto_select
-            else "\n".join(
-                f"Temp {temp} | Score: {score}:\n{outputs[temp]}"
-                for temp, score in sorted_scores
-            )
-        )