Added agent from mistral and llama

agent_mistral.py

@@ -0,0 +1,197 @@
+import logging
+import os
+
+import tempfile
+
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import Tool
+from langchain_mistralai import ChatMistralAI
+from langchain_experimental.utilities import PythonREPL
+
+from tools_audio import transcribe_audio
+
+from tools_doc import (
+    analyze_csv_file,
+    analyze_excel_file,
+    download_file_from_url,
+    extract_text_from_image,
+    read_file,
+)
+from tools_video import (
+    review_youtube_video,
+    use_vision_model,
+    transcribe_youtube,
+    video_frames_to_images,
+)
+
+from tools_browser import website_scrape, web_search
+
+from answers import create_final_answer_graph, validate_answer
+
+logger = logging.getLogger(__name__)
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+
+class BasicAgent:
+    def __init__(self):
+        try:
+            logger.info("Initializing BasicAgent")
+
+            # Create the prompt template
+            prompt = ChatPromptTemplate.from_messages(
+                [
+                    (
+                        "system",
+                        """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+                """,
+                    ),
+                    ("placeholder", "{chat_history}"),
+                    ("human", "{input}"),
+                    ("placeholder", "{agent_scratchpad}"),
+                ]
+            )
+            logger.info("Created prompt template")
+
+            # Initialize Gemini model
+            logger.info("Creating Gemini model...")
+            llm = ChatMistralAI(
+                # model="models/gemini-2.5-flash-preview-04-17",
+                model="mistral-small-latest",
+                google_api_key=os.getenv("GEMINI_KEY"),
+                temperature=0.2,
+            )
+            logger.info("Created Gemini model successfully")
+
+            # Define available tools
+            tools = [
+                # GoogleSearchResults(
+                #     api_wrapper=GoogleSearchAPIWrapper(
+                #         google_api_key=os.getenv("GOOGLE_SEARCH_API_KEY"),
+                #         google_cse_id=os.getenv("GOOGLE_CSE_ID"),
+                #         k=5,  # Number of results to return
+                #     )
+                # ),
+                web_search,
+                analyze_csv_file,
+                analyze_excel_file,
+                download_file_from_url,
+                extract_text_from_image,
+                read_file,
+                review_youtube_video,
+                transcribe_audio,
+                transcribe_youtube,
+                use_vision_model,
+                video_frames_to_images,
+                website_scrape,
+                Tool(
+                    name="python_repl",
+                    description="A Python shell. Use this to execute python commands. Input # should be a valid python command. If you want to see the output of a value, # you should print it out with `print(...)`.",
+                    func=PythonREPL().run,
+                ),
+            ]
+            logger.info("Tools: %s", tools)
+
+            # Create the agent
+            agent = create_tool_calling_agent(llm, tools, prompt)
+            logger.info("Created tool calling agent")
+
+            # Create the agent executor
+            self.agent_executor = AgentExecutor(
+                agent=agent,
+                tools=tools,
+                return_intermediate_steps=True,
+                verbose=True,
+            )
+            logger.info("Created agent executor")
+
+            # Create the graph
+            self.validation_graph = create_final_answer_graph()
+
+        except Exception as e:
+            logger.error("Error initializing agent: %s", e, exc_info=True)
+            raise
+
+    def __call__(self, question: str, task_id: str) -> str:
+        """Execute the agent with the given question and optional file.
+        Args:
+            question (str): The question to answer
+            task_id (str): The task ID to fetch the file
+        """
+        max_retries = 3
+        attempt = 0
+
+        # Create a temporary directory that will be automatically cleaned up
+        print("HELLO")
+        with tempfile.TemporaryDirectory() as temp_dir:
+            while attempt < max_retries:
+                # default_api_url = os.getenv("DEFAULT_API_URL")
+                default_api_url = DEFAULT_API_URL
+                file_url = f"{default_api_url}/files/{task_id}"
+
+                try:
+                    print("HELLO-A")
+                    # Download file to temporary directory
+                    file = download_file_from_url.invoke(
+                        {
+                            "url": file_url,
+                            "directory": temp_dir,
+                        }
+                    )
+                except Exception as e:
+                    logger.error(f"Error downloading file: {e}")
+                    file = None
+
+                try:
+                    print("HELLO-B")
+                    attempt += 1
+                    logger.info(f"Attempt {attempt} of {max_retries}")
+
+                    # Prepare input with file information
+                    if file and file.get("type") != "error":
+                        input_data = {
+                            "input": question
+                            + f" [File: type={file.get('type', 'None')}, path={file.get('path', 'None')}]",
+                        }
+                    else:
+                        input_data = {
+                            "input": question,
+                        }
+
+                    # Run the agent to get the answer
+                    result = self.agent_executor.invoke(input_data)
+                    answer = result.get("output", "")
+
+                    logger.info(f"Attempt {attempt} result: {result}")
+
+                    # Run validation
+                    validation_result = validate_answer(
+                        self.validation_graph,
+                        answer,
+                        [result.get("intermediate_steps", [])],
+                    )
+
+                    valid_answer = validation_result.get("valid_answer", False)
+                    final_answer = validation_result.get("final_answer", "")
+
+                    if valid_answer:
+                        logger.info(f"Valid answer found on attempt {attempt}")
+                        return final_answer
+
+                    logger.warning(
+                        f"Validation failed on attempt {attempt}: {final_answer}"
+                    )
+                    if attempt >= max_retries:
+                        raise Exception(
+                            f"Failed to get valid answer after {max_retries} attempts. Last error: {final_answer}"
+                        )
+
+                except Exception as e:
+                    logger.error(
+                        f"Error in attempt {attempt}: {e}", exc_info=True
+                    )
+                    if attempt >= max_retries:
+                        raise Exception(
+                            f"Failed after {max_retries} attempts. Last error: {str(e)}"
+                        )
+                    continue

agent_openrouter_llama.py

@@ -0,0 +1,197 @@
+import logging
+import os
+
+import tempfile
+
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import Tool
+from langchain_experimental.utilities import PythonREPL
+
+from langchain_openai import ChatOpenAI
+
+
+from tools_audio import transcribe_audio
+
+from tools_doc import (
+    analyze_csv_file,
+    analyze_excel_file,
+    download_file_from_url,
+    extract_text_from_image,
+    read_file,
+)
+from tools_video import (
+    review_youtube_video,
+    use_vision_model,
+    transcribe_youtube,
+    video_frames_to_images,
+)
+
+from tools_browser import website_scrape, web_search
+
+from answers import create_final_answer_graph, validate_answer
+
+logger = logging.getLogger(__name__)
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+
+class BasicAgent:
+    def __init__(self):
+        try:
+            logger.info("Initializing BasicAgent")
+
+            # Create the prompt template
+            prompt = ChatPromptTemplate.from_messages(
+                [
+                    (
+                        "system",
+                        """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+                """,
+                    ),
+                    ("placeholder", "{chat_history}"),
+                    ("human", "{input}"),
+                    ("placeholder", "{agent_scratchpad}"),
+                ]
+            )
+            logger.info("Created prompt template")
+
+            # Initialize Gemini model
+            llm = ChatOpenAI(
+                openai_api_key=os.getenv("OPENROUTER_API_KEY"),
+                openai_api_base="https://openrouter.ai/api/v1",
+                model_name="meta-llama/llama-4-scout:free",
+            )
+            logger.info("Created Gemini model successfully")
+
+            # Define available tools
+            tools = [
+                # GoogleSearchResults(
+                #     api_wrapper=GoogleSearchAPIWrapper(
+                #         google_api_key=os.getenv("GOOGLE_SEARCH_API_KEY"),
+                #         google_cse_id=os.getenv("GOOGLE_CSE_ID"),
+                #         k=5,  # Number of results to return
+                #     )
+                # ),
+                web_search,
+                analyze_csv_file,
+                analyze_excel_file,
+                download_file_from_url,
+                extract_text_from_image,
+                read_file,
+                review_youtube_video,
+                transcribe_audio,
+                transcribe_youtube,
+                use_vision_model,
+                video_frames_to_images,
+                website_scrape,
+                Tool(
+                    name="python_repl",
+                    description="A Python shell. Use this to execute python commands. Input # should be a valid python command. If you want to see the output of a value, # you should print it out with `print(...)`.",
+                    func=PythonREPL().run,
+                ),
+            ]
+            logger.info("Tools: %s", tools)
+
+            # Create the agent
+            agent = create_tool_calling_agent(llm, tools, prompt)
+            logger.info("Created tool calling agent")
+
+            # Create the agent executor
+            self.agent_executor = AgentExecutor(
+                agent=agent,
+                tools=tools,
+                return_intermediate_steps=True,
+                verbose=True,
+            )
+            logger.info("Created agent executor")
+
+            # Create the graph
+            self.validation_graph = create_final_answer_graph()
+
+        except Exception as e:
+            logger.error("Error initializing agent: %s", e, exc_info=True)
+            raise
+
+    def __call__(self, question: str, task_id: str) -> str:
+        """Execute the agent with the given question and optional file.
+        Args:
+            question (str): The question to answer
+            task_id (str): The task ID to fetch the file
+        """
+        max_retries = 3
+        attempt = 0
+
+        # Create a temporary directory that will be automatically cleaned up
+        print("HELLO")
+        with tempfile.TemporaryDirectory() as temp_dir:
+            while attempt < max_retries:
+                # default_api_url = os.getenv("DEFAULT_API_URL")
+                default_api_url = DEFAULT_API_URL
+                file_url = f"{default_api_url}/files/{task_id}"
+
+                try:
+                    print("HELLO-A")
+                    # Download file to temporary directory
+                    file = download_file_from_url.invoke(
+                        {
+                            "url": file_url,
+                            "directory": temp_dir,
+                        }
+                    )
+                except Exception as e:
+                    logger.error(f"Error downloading file: {e}")
+                    file = None
+
+                try:
+                    print("HELLO-B")
+                    attempt += 1
+                    logger.info(f"Attempt {attempt} of {max_retries}")
+
+                    # Prepare input with file information
+                    if file and file.get("type") != "error":
+                        input_data = {
+                            "input": question
+                            + f" [File: type={file.get('type', 'None')}, path={file.get('path', 'None')}]",
+                        }
+                    else:
+                        input_data = {
+                            "input": question,
+                        }
+
+                    # Run the agent to get the answer
+                    result = self.agent_executor.invoke(input_data)
+                    answer = result.get("output", "")
+
+                    logger.info(f"Attempt {attempt} result: {result}")
+
+                    # Run validation
+                    validation_result = validate_answer(
+                        self.validation_graph,
+                        answer,
+                        [result.get("intermediate_steps", [])],
+                    )
+
+                    valid_answer = validation_result.get("valid_answer", False)
+                    final_answer = validation_result.get("final_answer", "")
+
+                    if valid_answer:
+                        logger.info(f"Valid answer found on attempt {attempt}")
+                        return final_answer
+
+                    logger.warning(
+                        f"Validation failed on attempt {attempt}: {final_answer}"
+                    )
+                    if attempt >= max_retries:
+                        raise Exception(
+                            f"Failed to get valid answer after {max_retries} attempts. Last error: {final_answer}"
+                        )
+
+                except Exception as e:
+                    logger.error(
+                        f"Error in attempt {attempt}: {e}", exc_info=True
+                    )
+                    if attempt >= max_retries:
+                        raise Exception(
+                            f"Failed after {max_retries} attempts. Last error: {str(e)}"
+                        )
+                    continue

app.py

@@ -9,7 +9,9 @@ import pandas as pd
 import requests
 from dotenv import load_dotenv
 
-from agent_gemini import BasicAgent
+# from agent_gemini import BasicAgent
+# from agent_mistral import BasicAgent
+from agent_openrouter_llama import BasicAgent
 
 # Load environment variables from .env file
 load_dotenv()