Modify package structure

README.md

@@ -1,8 +1,8 @@
 ---
 title: Gaia_benchmark_agent
-emoji: 🕵🏻‍♂️
-colorFrom: indigo
-colorTo: indigo
+emoji: 🤖
+colorFrom: red
+colorTo: red
 sdk: gradio
 sdk_version: 5.25.2
 app_file: src/app.py

src/{agent.py → agent/QuestionAnsweringAgent.py}

@@ -1,7 +1,7 @@
 import os
 import glob
 from smolagents import CodeAgent
-from prompt import get_prompt
+from prompt.prompt import get_prompt
 
 class QuestionAnsweringAgent(CodeAgent):
     def __init__(self, *args, **kwargs):

src/app.py

@@ -8,16 +8,16 @@ from tools.file_tools import read_file_as_text
 from tools.youtube_tools import download_youtube_url_images, download_youtube_url_audio
 from tools.image_processing_tools import ask_question_about_image
 
-from agent import QuestionAnsweringAgent
+from agent.QuestionAnsweringAgent import QuestionAnsweringAgent
 
-from submission import build_gradio_interface
+from ui.builder import GradioUI
 
 model = InferenceClientModel(
     provider="auto",
     model_id="Qwen/Qwen3-Coder-30B-A3B-Instruct",
     temperature=0,
     top_p=1.0, 
-    seed=42  
+    seed=42
 )
 
 agent_tools = [
@@ -28,16 +28,26 @@ agent_tools = [
     ask_question_about_image
 ] 
 
+additional_authorized_imports=[
+    "os", 
+    "bs4", 
+    "PIL", 
+    "transformers", 
+    "torch", 
+    "requests", 
+    "glob"
+]
+
 agent = QuestionAnsweringAgent(
     name="question_answering_expert",
     model=model,
-    tools=agent_tools,
     add_base_tools=True,
+    tools=agent_tools,
+    additional_authorized_imports=additional_authorized_imports,
     planning_interval=None,
-    additional_authorized_imports=["os", "bs4", "PIL", "transformers", "torch", "requests", "glob"],
     max_steps=10,
     verbosity_level=2,  # For better debugging
 )
 
 if __name__ == "__main__":
-    build_gradio_interface(agent)
+    GradioUI(agent).launch()

src/{submission.py → eval/submission.py}

@@ -7,6 +7,7 @@ import numpy as np
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 SELECTED_QUESTIONS = [3]
+
 def run_and_submit_all(agent, profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -141,7 +142,7 @@ def run_and_submit_all(agent, profile: gr.OAuthProfile | None):
         return status_message, results_df
 
 
-def build_gradio_interface(agent):
+
     # --- Build Gradio Interface using Blocks ---
     with gr.Blocks() as demo:
         gr.Markdown("# Basic Agent Evaluation Runner")

src/ui/builder.py

@@ -0,0 +1,312 @@
+import mimetypes
+import os
+import re
+import shutil
+from typing import Optional
+import gradio as gr
+
+from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
+from smolagents.agents import ActionStep, MultiStepAgent
+from smolagents.memory import MemoryStep
+from smolagents.utils import _is_package_available
+
+from eval.submission import run_and_submit_all
+
+def pull_messages_from_step(
+    step_log: MemoryStep,
+    ):
+    """Extract ChatMessage objects from agent steps with proper nesting"""
+    import gradio as gr
+
+    if isinstance(step_log, ActionStep):
+        # Output the step number
+        step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""
+        yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
+
+        # First yield the thought/reasoning from the LLM
+        if hasattr(step_log, "model_output") and step_log.model_output is not None:
+            # Clean up the LLM output
+            model_output = step_log.model_output.strip()
+            # Remove any trailing <end_code> and extra backticks, handling multiple possible formats
+            model_output = re.sub(r"```\s*<end_code>", "```", model_output)  # handles ```<end_code>
+            model_output = re.sub(r"<end_code>\s*```", "```", model_output)  # handles <end_code>```
+            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)  # handles ```\n<end_code>
+            model_output = model_output.strip()
+            yield gr.ChatMessage(role="assistant", content=model_output)
+
+        # For tool calls, create a parent message
+        if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:
+            first_tool_call = step_log.tool_calls[0]
+            used_code = first_tool_call.name == "python_interpreter"
+            parent_id = f"call_{len(step_log.tool_calls)}"
+
+            # Tool call becomes the parent message with timing info
+            # First we will handle arguments based on type
+            args = first_tool_call.arguments
+            if isinstance(args, dict):
+                content = str(args.get("answer", str(args)))
+            else:
+                content = str(args).strip()
+
+            if used_code:
+                # Clean up the content by removing any end code tags
+                content = re.sub(r"```.*?\n", "", content)  # Remove existing code blocks
+                content = re.sub(r"\s*<end_code>\s*", "", content)  # Remove end_code tags
+                content = content.strip()
+                if not content.startswith("```python"):
+                    content = f"```python\n{content}\n```"
+
+            parent_message_tool = gr.ChatMessage(
+                role="assistant",
+                content=content,
+                metadata={
+                    "title": f"🛠️ Used tool {first_tool_call.name}",
+                    "id": parent_id,
+                    "status": "pending",
+                },
+            )
+            yield parent_message_tool
+
+            # Nesting execution logs under the tool call if they exist
+            if hasattr(step_log, "observations") and (
+                step_log.observations is not None and step_log.observations.strip()
+            ):  # Only yield execution logs if there's actual content
+                log_content = step_log.observations.strip()
+                if log_content:
+                    log_content = re.sub(r"^Execution logs:\s*", "", log_content)
+                    yield gr.ChatMessage(
+                        role="assistant",
+                        content=f"{log_content}",
+                        metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
+                    )
+
+            # Nesting any errors under the tool call
+            if hasattr(step_log, "error") and step_log.error is not None:
+                yield gr.ChatMessage(
+                    role="assistant",
+                    content=str(step_log.error),
+                    metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},
+                )
+
+            # Update parent message metadata to done status without yielding a new message
+            parent_message_tool.metadata["status"] = "done"
+
+        # Handle standalone errors but not from tool calls
+        elif hasattr(step_log, "error") and step_log.error is not None:
+            yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})
+
+        # Calculate duration and token information
+        step_footnote = f"{step_number}"
+        if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
+            token_str = (
+                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
+            )
+            step_footnote += token_str
+        if hasattr(step_log, "duration"):
+            step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None
+            step_footnote += step_duration
+        step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
+        yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
+        yield gr.ChatMessage(role="assistant", content="-----")
+
+
+def stream_to_gradio(
+    agent,
+    task: str,
+    reset_agent_memory: bool = False,
+    additional_args: Optional[dict] = None
+    ):
+    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+    if not _is_package_available("gradio"):
+        raise ModuleNotFoundError(
+            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+        )
+    import gradio as gr
+
+    total_input_tokens = 0
+    total_output_tokens = 0
+
+    for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
+        # Track tokens if model provides them
+        if hasattr(agent.model, "last_input_token_count"):
+            total_input_tokens += agent.model.last_input_token_count
+            total_output_tokens += agent.model.last_output_token_count
+            if isinstance(step_log, ActionStep):
+                step_log.input_token_count = agent.model.last_input_token_count
+                step_log.output_token_count = agent.model.last_output_token_count
+
+        for message in pull_messages_from_step(
+            step_log,
+        ):
+            yield message
+
+    final_answer = step_log  # Last log is the run's final_answer
+    final_answer = handle_agent_output_types(final_answer)
+
+    if isinstance(final_answer, AgentText):
+        yield gr.ChatMessage(
+            role="assistant",
+            content=f"**Final answer:**\n{final_answer.to_string()}\n",
+        )
+    elif isinstance(final_answer, AgentImage):
+        yield gr.ChatMessage(
+            role="assistant",
+            content={"path": final_answer.to_string(), "mime_type": "image/png"},
+        )
+    elif isinstance(final_answer, AgentAudio):
+        yield gr.ChatMessage(
+            role="assistant",
+            content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
+        )
+    else:
+        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
+
+
+class GradioUI:
+    """A one-line interface to launch your agent in Gradio"""
+    def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):
+        if not _is_package_available("gradio"):
+            raise ModuleNotFoundError(
+                "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
+            )
+        self.agent = agent
+        self.file_upload_folder = file_upload_folder
+        if self.file_upload_folder is not None:
+            if not os.path.exists(file_upload_folder):
+                os.mkdir(file_upload_folder)
+
+    def interact_with_agent(self, prompt, messages):
+        import gradio as gr
+
+        messages.append(gr.ChatMessage(role="user", content=prompt))
+        yield messages
+        for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
+            messages.append(msg)
+            yield messages
+        yield messages
+
+    def upload_file(
+        self,
+        file,
+        file_uploads_log,
+        allowed_file_types=[
+            "application/pdf",
+            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+            "text/plain",
+        ],
+        ):
+        """
+        Handle file uploads, default allowed types are .pdf, .docx, and .txt
+        """
+        import gradio as gr
+
+        if file is None:
+            return gr.Textbox("No file uploaded", visible=True), file_uploads_log
+
+        try:
+            mime_type, _ = mimetypes.guess_type(file.name)
+        except Exception as e:
+            return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log
+
+        if mime_type not in allowed_file_types:
+            return gr.Textbox("File type disallowed", visible=True), file_uploads_log
+
+        # Sanitize file name
+        original_name = os.path.basename(file.name)
+        sanitized_name = re.sub(
+            r"[^\w\-.]", "_", original_name
+        )  # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores
+
+        type_to_ext = {}
+        for ext, t in mimetypes.types_map.items():
+            if t not in type_to_ext:
+                type_to_ext[t] = ext
+
+        # Ensure the extension correlates to the mime type
+        sanitized_name = sanitized_name.split(".")[:-1]
+        sanitized_name.append("" + type_to_ext[mime_type])
+        sanitized_name = "".join(sanitized_name)
+
+        # Save the uploaded file to the specified folder
+        file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))
+        shutil.copy(file.name, file_path)
+
+        return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]
+
+    def log_user_message(self, text_input, file_uploads_log):
+        return (
+            text_input
+            + (
+                f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
+                if len(file_uploads_log) > 0
+                else ""
+            ),
+            "",
+        )
+
+    def launch(self, **kwargs):
+        with gr.Blocks() as demo:
+            gr.Markdown("# Question Answering Agent Evaluation Runner")
+            gr.Markdown(
+                """
+                **Welcome to the Question Answering Agent !**
+                ## 1.  Please start by logging in to your Hugging Face account using the button below. This uses your HF username for submission.
+                """
+            )
+            gr.LoginButton()
+            gr.Markdown(
+                """
+                ---
+                ## 2. Interact with the agent below.
+                """
+            )
+            stored_messages = gr.State([])
+            file_uploads_log = gr.State([])
+            chatbot = gr.Chatbot(
+                label="Agent",
+                type="messages",
+                avatar_images=(
+                    None,
+                    "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
+                ),
+                resizeable=True,
+                scale=1,
+            )
+            # If an upload folder is provided, enable the upload feature
+            if self.file_upload_folder is not None:
+                upload_file = gr.File(label="Upload a file")
+                upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)
+                upload_file.change(
+                    self.upload_file,
+                    [upload_file, file_uploads_log],
+                    [upload_status, file_uploads_log],
+                )
+            text_input = gr.Textbox(lines=1, label="Chat Message")
+            text_input.submit(
+                self.log_user_message,
+                [text_input, file_uploads_log],
+                [stored_messages, text_input],
+            ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])
+            
+            gr.Markdown(
+                """
+                ---
+                ## 3.Run Evaluation on GAIA Benchmark & Submit All Answers
+                """
+            )            
+            run_button = gr.Button("Run Evaluation on GAIA Benchmark & 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)
+
+            def run_with_login_state(profile: gr.OAuthProfile):
+                return run_and_submit_all(self.agent, profile)
+
+            run_button.click(
+                fn=run_with_login_state,
+                outputs=[status_output, results_table]
+            )
+        demo.launch(debug=True, share=False, **kwargs)
+    
+