Update get_model get_gemini_keys

.gitignore

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

app.py

@@ -1,17 +1,15 @@
+# ruff: noqa: F401
+import inspect
 import os
+
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
-from ycecream import y
-
-from smolagents import ToolCollection, CodeAgent, LiteLLMRouterModel, VisitWebpageTool, Tool
-
+import requests
 import wikipediaapi
-
-from mcp import StdioServerParameters
-
 from basic_agent import BasicAgent
+from mcp import StdioServerParameters
+from smolagents import CodeAgent, LiteLLMRouterModel, Tool, ToolCollection, VisitWebpageTool
+from ycecream import y
 
 y.configure(sln=1)
 
@@ -37,10 +35,7 @@ class BasicAgent:
 # """
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
+    """Fetch all questions, run the BasicAgent on them, submit all answers, and display the results."""
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
@@ -211,4 +206,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

basic_agent.py

@@ -1,40 +1,114 @@
+# pylint: disable=line-too-long,missing-module-docstring,missing-class-docstring,missing-function-docstring,broad-exception-caught, unused-variable, too-many-statements,too-many-return-statements,too-many-locals,redefined-builtin,unused-import
+# ruff: noqa: F401
+
+import os
+import typing
+from dataclasses import dataclass, field
+
+import pandas as pd
+import requests
 import rich
 from loguru import logger
-import requests
+import smolagents
+from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, HfApiModel, VisitWebpageTool
+
+from get_model import get_model
 
 print = rich.get_console().print
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+SPACE_ID = os.getenv("SPACE_ID", "mikeee/final-assignment")
+
+AUTHORIZED_IMPORTS = [
+    "requests",
+    "zipfile",
+    "pandas",
+    "numpy",
+    "sympy",
+    "json",
+    "bs4",
+    "pubchempy",
+    "xml",
+    "yahoo_finance",
+    "Bio",
+    "sklearn",
+    "scipy",
+    "pydub",
+    "PIL",
+    "chess",
+    "PyPDF2",
+    "pptx",
+    "torch",
+    "datetime",
+    "fractions",
+    "csv",
+    "io",
+    "glob",
+]
+
 
+@dataclass
 class BasicAgent:
-    def __init__(self):
+    model: smolagents.models.Model = HfApiModel()
+    tools: list = field(default_factory=lambda: [])
+    # def __init__(self):
+    def __post_init__(self):
+        """Run post_init."""
         logger.debug("BasicAgent initialized.")
+        self.agent = CodeAgent(
+            tools=self.tools,
+            model=self.model,
+            verbosity_level=3,
+            additional_authorized_imports=AUTHORIZED_IMPORTS,
+            planning_interval=4,
+        )
+
+    def get_answer(self, question: str):
+        return f"ans to {question[:220]}..."
+
     def __call__(self, question: str) -> str:
         # print(f"Agent received question (first 50 chars): {question[:50]}...")
         print(f"Agent received question: {question}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
 
-def main():
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        logger.debug(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
+        # fixed_answer = "This is a default answer."
+        # print(f"Agent returning fixed answer: {fixed_answer}")
+        # return fixed_answer
+        try:
+            # answer = self.get_answer(question)
+            answer = self.agent(question)
+        except Exception as e:
+            logger.error(e)
+            answer = str(e)[:10] + "..."
+
+        return answer
 
+
+def main():
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-    username = "mikeee"
-    repo_name = "final-assignment"
+    submit_url = f"{api_url}/submit"  # noqa
+
+    # username = "mikeee"
+    # repo_name = "final-assignment"
+
+    username, _, repo_name = SPACE_ID.partition("/")
+
     space_id = f"{username}/{repo_name}"
 
+    model = get_model(cat="gemini")
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        # agent = BasicAgent()
+        agent = BasicAgent(
+            model=model,
+            tools=[
+                DuckDuckGoSearchTool(),
+                VisitWebpageTool(),
+            ]
+        )
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
+
     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
@@ -42,20 +116,20 @@ def main():
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=30)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.JSONDecodeError as e:
+        print(f"Error decoding JSON response from questions endpoint: {e}")
+        print(f"Response text: {response.text[:500]}")
+        return f"Error decoding server response for questions: {e}", None
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
@@ -63,8 +137,11 @@ def main():
     # 3. Run your Agent
     results_log = []
     answers_payload = []
+
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+
+    # for item in questions_data:
+    for item in questions_data[-1:]:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
@@ -75,20 +152,21 @@ def main():
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
     # 4. Prepare Submission
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}  # noqa
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
     print(answers_payload)
 
-
+    agent.agent.visualize()
+    return None, None
 
 if __name__ == "__main__":
-    main()
+    main()

get_gemini_keys.py

@@ -0,0 +1,35 @@
+"""Get gemini keys."""
+import re
+from pathlib import Path
+
+import rich
+import yaml
+from dotenv import dotenv_values
+
+
+def get_gemini_keys(file=r".env-gemini", dotenv=False):
+    """Get gemini keys."""
+    if Path(file).name.startswith(".env"):
+        dotenv = True
+
+    if isinstance(dotenv, bool) or isinstance(dotenv, float):
+        dotenv = bool(dotenv)
+
+    if dotenv is True:
+        try:
+            keys = yaml.load(dotenv_values(file).get("GEMINI_API_KEYS"), yaml.Loader)
+        except Exception as e:
+            print(e)
+            return []
+        return keys
+
+    try:
+        text = Path(file).read_text()
+        # return re.findall(r"AIzaSy[A-Z][\w-]+", text)
+        return re.findall(r"AIzaSy[A-Z][\w-]{32}", text)
+    except Exception as e:
+        print(e)
+        return []
+
+if __name__ == "__main__":
+    rich.get_console().print(get_gemini_keys())

get_model.py

@@ -0,0 +1,96 @@
+"""Create and return a model."""
+
+import os
+import re
+from platform import node
+
+from get_gemini_keys import get_gemini_keys
+from loguru import logger
+from smolagents import HfApiModel, LiteLLMRouterModel
+
+
+def get_model(cat: str = "hf", provider=None, model_id=None):
+    """
+    Create and return a model.
+
+    Args:
+        cat: category
+        provider: for HfApiModel (cat='hf')
+        model_id: model name
+
+    if no gemini_api_keys, return HfApiModel()
+
+    """
+    if cat.lower() in ["gemini"]:
+        # get gemini_api_keys
+        # dedup
+        _ = re.findall(r"AIzaSy[A-Z][\w-]{32}", os.getenv("GEMINI_API_KEYS", ""))
+        gemini_api_keys = dict.fromkeys(get_gemini_keys() + _)
+
+        # assert gemini_api_keys, "No GEMINI_API_KEYS, set env var GEMINI_API_KEYS or put them in .env-gemini and try again."
+        if not gemini_api_keys:
+            logger.warning(
+                "cat='gemini' but no GEMINI_API_KEYS found, "
+                " returning HfApiModel()..."
+                " Set env var GEMINI_API_KEYS and/or .env-gemini "
+                " with free space gemini-api-keys if you want to try 'gemini' "
+            )
+            return HfApiModel()
+
+        # setup proxy for gemini and for golay (local)
+        if "golay" in node():
+            os.environ.update(
+                HTTPS_PROXY="http://localhost:8081",
+                HTTP_PROXY="http://localhost:8081",
+                ALL_PROXY="http://localhost:8081",
+                NO_PROXY="localhost,127.0.0.1,oracle",
+            )
+
+        if model_id is None:
+            model_id = "gemini-2.5-flash-preview-04-17"
+
+        # model_id = "gemini-2.5-flash-preview-04-17"
+        llm_loadbalancer_model_list_gemini = []
+        for api_key in gemini_api_keys:
+            llm_loadbalancer_model_list_gemini.append(
+                {
+                    "model_name": "model-group-1",
+                    "litellm_params": {
+                        "model": f"gemini/{model_id}",
+                        "api_key": api_key,
+                    },
+                },
+            )
+
+        model_id = "deepseek-ai/DeepSeek-V3"
+        llm_loadbalancer_model_list_siliconflow = [
+            {
+                "model_name": "model-group-2",
+                "litellm_params": {
+                    "model": f"openai/{model_id}",
+                    "api_key": os.getenv("SILICONFLOW_API_KEY"),
+                    "api_base": "https://api.siliconflow.cn/v1",
+                },
+            }
+        ]
+
+        fallbacks = []
+        model_list = llm_loadbalancer_model_list_gemini
+        if os.getenv("SILICONFLOW_API_KEY"):
+            fallbacks = [{"model-group-1": "model-group-2"}]
+            model_list += llm_loadbalancer_model_list_siliconflow
+
+        model = LiteLLMRouterModel(
+            model_id="model-group-1",
+            model_list=model_list,
+            client_kwargs={
+                "routing_strategy": "simple-shuffle",
+                "num_retries": 3,
+                # "retry_after": 130,   # waits min  s before retrying request
+                "fallbacks": fallbacks,
+            },
+        )
+        return model
+
+    # if cat.lower() in ["hf"]:  default
+    return HfApiModel(provider=provider, model_id=model_id)

requirements.txt

@@ -5,5 +5,15 @@ loguru
 ycecream
 mcp[cli]
 mcpadapt
+
+# for smolagents.LiteLLMModel,  smolagents.LiteLLMRouterModel (LiteLLMRouterModel does not seem to work)
+litellm
+
+# for smolagents.OpenAIServerModel
+openai
 smolagents@git+https://github.com/huggingface/smolagents.git
 wikipedia-api
+
+yaml
+rich
+python-dotenv