Token needed, correlation tool

.gitignore

@@ -0,0 +1 @@
+.env

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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+-----END CERTIFICATE-----

app.py

@@ -6,15 +6,24 @@ import yaml
 from tools.final_answer import FinalAnswerTool
 import numpy as np
 from typing import List, Union
+from local_model import LocalModel
+import logging
+from dotenv import load_dotenv
 
+import os
 from Gradio_UI import GradioUI
 
+logger = logging.getLogger(__name__)
+
+load_dotenv()
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
 def compute_correlation(
     arg1: List[Union[float, int]], arg2: List[Union[float, int]]
-) -> float:  # it's import to specify the return type
+) -> str:  # it's import to specify the return type
     # Keep this format for the description / args / args description but feel free to modify the tool
     """A tool to compute the correlation between two lists of values.
     Args:
@@ -22,7 +31,8 @@ def compute_correlation(
         arg2: a second list of values.
     """
 
-    corr = float(np.corrcoef(arg1, arg2)[0, 1])
+    corr = str(float(np.corrcoef(arg1, arg2)[0, 1]))
+    logger.info(f"Got correlation value: {corr}")
 
     return corr
 
@@ -44,23 +54,24 @@ def get_current_time_in_timezone(timezone: str) -> str:
 
 
 final_answer = FinalAnswerTool()
+# model = LocalModel("Qwen/Qwen2.5-1.5B", max_tokens=100, temperature=0.5)
 model = HfApiModel(
     max_tokens=2096,
     temperature=0.5,
-    model_id="https://wxknx1kg971u7k1n.us-east-1.aws.endpoints.huggingface.cloud",  # it is possible that this model may be overloaded
+    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",  # it is possible that this model may be overloaded
+    token=HF_TOKEN,
     custom_role_conversions=None,
 )
 
-
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
-
 with open("prompts.yaml", "r") as stream:
     prompt_templates = yaml.safe_load(stream)
 
 agent = CodeAgent(
     model=model,
-    tools=[final_answer, compute_correlation],  ## add your tools here (don't remove final answer)
+    tools=[
+        final_answer,
+        compute_correlation,
+    ],  ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,
@@ -72,4 +83,3 @@ agent = CodeAgent(
 
 
 GradioUI(agent).launch()
-

local_model.py

@@ -0,0 +1,45 @@
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import logging
+
+
+logger = logging.getLogger(__name__)
+
+logger.addHandler(logging.StreamHandler())
+
+
+class LocalModel:
+    def __init__(self, model_name: str, max_tokens: int, temperature: float):
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        # Load the model locally. For a demo, you may choose a lighter model if needed.
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name, torch_dtype=torch.float16
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.pipeline = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer,
+        )
+
+    def __call__(self, prompt: str, **kwargs) -> str:
+        # Adjust the call signature as needed by your agent
+        result = self.pipeline(
+            prompt,
+            max_new_tokens=self.max_tokens,
+            temperature=self.temperature,
+            **kwargs,
+        )
+
+        output = result[0]["generated_text"]
+        logger.info(f"Model output: {output}")
+        # Assuming the result is a list with one dict containing the generated text:
+        return result[0]["generated_text"]
+
+
+if __name__ == "__main__":
+    local_model = LocalModel("Qwen/Qwen2.5-1.5B", max_tokens=100, temperature=0.5)
+    output = local_model("A big foot")
+
+    print(output)

prompts.yaml

@@ -38,88 +38,6 @@
   final_answer(result)
   ```<end_code>
 
-  ---
-  Task:
-  "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
-  You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
-  {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
-
-  Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
-  Code:
-  ```py
-  translated_question = translator(question=question, src_lang="French", tgt_lang="English")
-  print(f"The translated question is {translated_question}.")
-  answer = image_qa(image=image, question=translated_question)
-  final_answer(f"The answer is {answer}")
-  ```<end_code>
-
-  ---
-  Task:
-  In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
-  What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
-
-  Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
-  Code:
-  ```py
-  pages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
-  print(pages)
-  ```<end_code>
-  Observation:
-  No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
-
-  Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
-  Code:
-  ```py
-  pages = search(query="1979 interview Stanislaus Ulam")
-  print(pages)
-  ```<end_code>
-  Observation:
-  Found 6 pages:
-  [Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
-
-  [Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
-
-  (truncated)
-
-  Thought: I will read the first 2 pages to know more.
-  Code:
-  ```py
-  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
-      whole_page = visit_webpage(url)
-      print(whole_page)
-      print("\n" + "="*80 + "\n")  # Print separator between pages
-  ```<end_code>
-  Observation:
-  Manhattan Project Locations:
-  Los Alamos, NM
-  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
-  (truncated)
-
-  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
-  Code:
-  ```py
-  final_answer("diminished")
-  ```<end_code>
-
-  ---
-  Task: "Which city has the highest population: Guangzhou or Shanghai?"
-
-  Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
-  Code:
-  ```py
-  for city in ["Guangzhou", "Shanghai"]:
-      print(f"Population {city}:", search(f"{city} population")
-  ```<end_code>
-  Observation:
-  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
-  Population Shanghai: '26 million (2019)'
-
-  Thought: Now I know that Shanghai has the highest population.
-  Code:
-  ```py
-  final_answer("Shanghai")
-  ```<end_code>
-
   ---
   Task: "What is the current age of the pope, raised to the power 0.36?"