New tooling for math, youtube and wikipedia.

added math, wikipedia and youtube tools. Testing indexing questions

app.py

@@ -1,301 +1,287 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-from smolagents import DuckDuckGoSearchTool,HfApiModel,load_tool,PythonInterpreterTool,VisitWebpageTool,Tool, CodeAgent
-import hashlib
-import json
-from transformers import AutoTokenizer, AutoModelForCausalLM , pipeline, TransformersEngine
-import wikipedia
-import torch
-from tooling import ModelMathTool, WikipediaTool
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-import os
-
-
-cache= {}
-
-
-web_search = DuckDuckGoSearchTool()
-python_interpreter = PythonInterpreterTool()
-visit_webpage_tool = VisitWebpageTool()
-wiki_tool = WikipediaTool()
-
-#model_math_tool = ModelMathTool()
-
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
-
-
-
-
-
-def load_cached_answer(question_id: str) -> str:
-    if question_id in cache.keys():
-        return cache[question_id]
-    else:
-        return None
-
-def cache_answer(question_id: str, answer: str):
-    cache[question_id] = answer
-
-
-# --- Model Setup ---
-MODEL_NAME = 'Qwen/Qwen2.5-3B-Instruct'#'meta-llama/Llama-3.2-3B-Instruct'
-#"Qwen/Qwen2.5-VL-3B-Instruct"#'meta-llama/Llama-2-7b-hf'#'meta-llama/Llama-3.1-8B-Instruct'#'TinyLlama/TinyLlama-1.1B-Chat-v1.0'#'mistralai/Mistral-7B-Instruct-v0.2'#'microsoft/DialoGPT-small'# 'EleutherAI/gpt-neo-2.7B'#'distilbert/distilgpt2'#'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'#'mistralai/Mistral-7B-Instruct-v0.2'
-
-
-
-
-
-def load_model(model_name):
-    """Download and load the model and tokenizer."""
-    try:
-        print(f"Loading model {MODEL_NAME}...")
-        model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
-        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-
-        
-        print(f"Model {MODEL_NAME} loaded successfully.")
-
-        transformers_engine = TransformersEngine(pipeline("text-generation", model=model, tokenizer=tokenizer))
-
-        
-        return transformers_engine, model
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        raise
-
-# Load the model and tokenizer locally
-#model, tokenizer = load_model()
-model = HfApiModel()#model_id=MODEL_NAME, max_tokens=512)
-
-from smolagents import TransformersModel
-
-
-
-model_id ="meta-llama/Llama-3.2-3B-Instruct"#"microsoft/phi-2"# not working out of the box"google/gemma-2-2b-it" #toobig"Qwen/Qwen1.5-7B-Chat"#working but stupid: "meta-llama/Llama-3.2-3B-Instruct"
-#model = TransformersModel(
-#    model_id=model_id,
-#    max_new_tokens=256)
-
-#model = HfApiModel()
-
-
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.agent = CodeAgent(
-            model=model,
-            tools=[web_search,python_interpreter,visit_webpage_tool,wiki_tool], 
-            max_steps=5,
-            verbosity_level=1,
-            grammar=None,
-            planning_interval=3,
-            add_base_tools=True,
-            additional_authorized_imports=['requests','wikipedia']
-            
-        )
-
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        answer = self.agent.run(question)
-        return answer
-
-
-    
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays 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
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    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)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        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(f"Fetched {len(questions_data)} questions.")
-    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
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            cached = load_cached_answer(task_id)
-            if cached:
-                submitted_answer = cached
-                print(f"Loaded cached answer for task {task_id}")
-            else:
-                submitted_answer = agent(question_text)
-                cache_answer(task_id, submitted_answer)
-                print(f"Generated and cached answer for task {task_id}")
-    
-            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}"})
-
-    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}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+from smolagents import DuckDuckGoSearchTool, HfApiModel, PythonInterpreterTool, VisitWebpageTool, CodeAgent
+import hashlib
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TransformersEngine
+import wikipedia
+import torch
+from tooling import MathModelQuerer, WikipediaPageFetcher, YoutubeTranscriptFetcher, CodeModelQuerer
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+import os
+
+cache = {}
+
+web_search = DuckDuckGoSearchTool()
+python_interpreter = PythonInterpreterTool()
+visit_webpage_tool = VisitWebpageTool()
+wiki_tool = WikipediaPageFetcher()
+yt_transcript_fetcher = YoutubeTranscriptFetcher()
+math_model_querer = MathModelQuerer()
+code_model_querer = CodeModelQuerer()
+
+
+def load_cached_answer(question_id: str) -> str:
+    if question_id in cache.keys():
+        return cache[question_id]
+    else:
+        return None
+
+
+def cache_answer(question_id: str, answer: str):
+    cache[question_id] = answer
+
+
+# --- Model Setup ---
+MODEL_NAME = 'Qwen/Qwen2.5-3B-Instruct'  # 'meta-llama/Llama-3.2-3B-Instruct'
+
+
+# "Qwen/Qwen2.5-VL-3B-Instruct"#'meta-llama/Llama-2-7b-hf'#'meta-llama/Llama-3.1-8B-Instruct'#'TinyLlama/TinyLlama-1.1B-Chat-v1.0'#'mistralai/Mistral-7B-Instruct-v0.2'#'microsoft/DialoGPT-small'# 'EleutherAI/gpt-neo-2.7B'#'distilbert/distilgpt2'#'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'#'mistralai/Mistral-7B-Instruct-v0.2'
+
+
+def load_model(model_name):
+    """Download and load the model and tokenizer."""
+    try:
+        print(f"Loading model {MODEL_NAME}...")
+        model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        print(f"Model {MODEL_NAME} loaded successfully.")
+
+        transformers_engine = TransformersEngine(pipeline("text-generation", model=model, tokenizer=tokenizer))
+
+        return transformers_engine, model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        raise
+
+
+# Load the model and tokenizer locally
+# model, tokenizer = load_model()
+model = HfApiModel()  # model_id=MODEL_NAME, max_tokens=512)
+model_id = "reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B"  # "microsoft/phi-2"# not working out of the box"google/gemma-2-2b-it" #toobig"Qwen/Qwen1.5-7B-Chat"#working but stupid: "meta-llama/Llama-3.2-3B-Instruct"
+model = HfApiModel(model_id)
+#from smolagents import TransformersModel
+# model = TransformersModel(
+#     model_id=model_id,
+#     max_new_tokens=256)
+
+# model = HfApiModel()
+
+
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.agent = CodeAgent(
+            model=model,
+            tools=[web_search, python_interpreter, visit_webpage_tool, wiki_tool, code_model_querer, math_model_querer],
+            max_steps=3,
+            verbosity_level=1,
+            grammar=None,
+            planning_interval=3,
+            add_base_tools=True,
+            additional_authorized_imports=['requests', 'wikipedia', 'pandas']
+
+        )
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        answer = self.agent.run(question)
+        return answer
+
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays 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
+
+    if profile:
+        username = f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    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)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        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(f"Fetched {len(questions_data)} questions.")
+    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
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    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:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            cached = load_cached_answer(task_id)
+            if cached:
+                submitted_answer = cached
+                print(f"Loaded cached answer for task {task_id}")
+            else:
+                submitted_answer = agent(question_text)
+                cache_answer(task_id, submitted_answer)
+                print(f"Generated and cached answer for task {task_id}")
+
+            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}"})
+
+    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}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup:  # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-" * (60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,7 +1,10 @@
-gradio
-requests
-smolagents
-transformers
-wikipedia
-torch
-accelerate
+gradio
+requests
+smolagents
+transformers
+wikipedia
+torch
+accelerate
+youtube_transcript_api
+spacy
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl

tooling.py

@@ -1,65 +1,125 @@
-from smolagents import Tool
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
-import torch
-
-
-class ModelMathTool(Tool):
-    name = "math_model"
-    description = "Answers advanced math questions using a pretrained math model."
-
-    inputs = {
-        "problem": {
-            "type": "string",
-            "description": "Math problem to solve.",
-        }
-    }
-
-    output_type = "string"
-
-    def __init__(self, model_name= "deepseek-ai/deepseek-math-7b-base"):
-        print(f"Loading math model: {model_name}")
-
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        print("loaded tokenizer")
-        self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
-        print("loaded auto model")
-
-        self.model.generation_config = GenerationConfig.from_pretrained(model_name)
-        print("loaded coonfig")
-
-        self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
-        print("loaded pad token")
-
-
-
-    def forward(self, problem: str) -> str:
-        print(f"[MathModelTool] Question: {problem}")
-
-        inputs = self.tokenizer(problem, return_tensors="pt")
-        outputs =self.model.generate(**inputs, max_new_tokens=100)
-
-        result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-        return result
-
-class WikipediaTool(Tool):
-    name = "wikip_tool"
-    description = "Searches Wikipedia and provides summary about the queried topic."
-
-    inputs = {
-        "query": {
-            "type": "string",
-            "description": "Topic of wikipedia search",
-        }
-    }
-
-    output_type = "string"
-
-
-    def __init__(self):
-        import wikipedia
-
-    def forward(self, query: str) -> str:
-        return wikipedia.summary(query, sentences=3)
-
-
+from smolagents import Tool
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+from wikipedia_utils import *
+from youtube_utils import *
+
+
+class MathModelQuerer(Tool):
+    name = "math_model"
+    description = "Answers advanced math questions using a pretrained math model."
+
+    inputs = {
+        "problem": {
+            "type": "string",
+            "description": "Math problem to solve.",
+        }
+    }
+
+    output_type = "string"
+
+    def __init__(self, model_name="deepseek-ai/deepseek-math-7b-base"):
+        print(f"Loading math model: {model_name}")
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        print("loaded tokenizer")
+        self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+        print("loaded auto model")
+
+        self.model.generation_config = GenerationConfig.from_pretrained(model_name)
+        print("loaded coonfig")
+
+        self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
+        print("loaded pad token")
+
+    def forward(self, problem: str) -> str:
+        try:
+            print(f"[MathModelTool] Question: {problem}")
+
+            inputs = self.tokenizer(problem, return_tensors="pt")
+            outputs = self.model.generate(**inputs, max_new_tokens=100)
+
+            result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            return result
+        except:
+            return f"Failed using the tool {self.name}"
+
+
+class CodeModelQuerer(Tool):
+    name = "code_querer"
+    description = "Given a problem description, generates a piece of code used specialized LLM model. Returns output of the model."
+
+    inputs = {
+        "problem": {
+            "type": "string",
+            "description": "Description of a code sample to be generated",
+        }
+    }
+
+    output_type = "string"
+
+    def __init__(self, model_name="Qwen/Qwen2.5-Coder-32B-Instruct"):
+        from smolagents import HfApiModel
+        print(f"Loading llm for Code tool: {model_name}")
+        self.model = HfApiModel()
+
+    def forward(self, problem: str) -> str:
+        try:
+            return self.model.generate(problem, max_new_tokens=512)
+        except:
+            return f"Failed using the tool {self.name}"
+
+
+class WikipediaPageFetcher(Tool):
+    name = "wiki_page_fetcher"
+    description = "Searches Wikipedia and provides summary about the queried topic as a string."
+
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "Topic of wikipedia search",
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, query: str) -> str:
+        try:
+            wiki_query = query(query)
+            wiki_page = fetch_wikipedia_page(wiki_query)
+            return wiki_page
+        except:
+            return f"Failed using the tool {self.name}"
+
+
+class YoutubeTranscriptFetcher(Tool):
+    name = "youtube_transcript_fetcher"
+    description = "Attempts to fetch a youtube transcript in english, if provided with a query \\" \
+                  " that contains a youtube link with video id. Returns a transcript content as a string. Alternatively, if tool  is provided with a\\"" \
+                  youtube video id, it can fetch the transcript directly."
+
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "A query that includes youtube id."
+        },
+        "video_id" : {
+            "type" : "string",
+            "description" : "Optional string with video id from youtube.",
+            "nullable"  : True
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, query: str, video_id=None) -> str:
+        try:
+            if video_id is None:
+                video_id = get_youtube_video_id(query)
+
+            fetched_transcript = fetch_transcript_english(video_id)
+
+            return post_process_transcript(fetched_transcript)
+        except:
+            return f"Failed using the tool {self.name}"

wikipedia_utils.py

@@ -1,6 +1,7 @@
 import wikipedia
 import spacy
 
+
 def get_wiki_query(query):
     try:
         ### spacy code
@@ -11,11 +12,10 @@ def get_wiki_query(query):
         # Parse the sentence
         doc = nlp(query)
 
-
         # Entity path (people, evenrs, books)
         entities_components = [entity_substring.text for entity_substring in doc.ents]
         if len(entities_components) > 0:
-            subject_of_the_query= ""
+            subject_of_the_query = ""
             for substrings in entities_components:
                 subject_of_the_query = subject_of_the_query + substrings
 
@@ -34,7 +34,7 @@ def get_wiki_query(query):
 
 
     except Exception as e:
-        print("Failed parsing a query subject from query" ,query)
+        print("Failed parsing a query subject from query", query)
         print(e)
 
 
@@ -43,18 +43,10 @@ def fetch_wikipedia_page(wiki_query):
         matched_articles = wikipedia.search(wiki_query)
         if len(matched_articles) > 0:
             used_article = matched_articles[0]
-            page_content = wikipedia.page(used_article,auto_suggest=False)
+            page_content = wikipedia.page(used_article, auto_suggest=False)
             return page_content.content
         else:
             return ""
     except Exception as e:
         print("Could not fetch the wikipedia article using ", wiki_query)
         print(e)
-
-test_queries = ["How many albums did Amy Winehouse publish?", "Who is Evora Cesaria?","Is cat an animal?"]
-wiki_queries = []
-wiki_pages= []
-for tq in test_queries:
-    wiki_queries.append(get_wiki_query(tq))
-for wq in wiki_queries:
-    wiki_pages.append(fetch_wikipedia_page(wq))