Changed model

.gitignore

@@ -1,2 +1,4 @@
 # Ignore Python cache directories
 **/__pycache__/
+venv/
+.DS_Store

.python-version

@@ -0,0 +1 @@
+3.11.9

Gradio_UI.py

@@ -26,6 +26,8 @@ import gradio as gr
 from gradio.components import Markdown
 from gradio.components import Chatbot, Textbox, State, Button
 
+from smolagents.memory import TokenUsage
+
 def pull_messages_from_step(
     step_log: MemoryStep,
 ):
@@ -109,16 +111,19 @@ def pull_messages_from_step(
         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
         # 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:,}"
-            )
+            input_tokens = step_log.input_token_count if step_log.input_token_count is not None else 0
+            output_tokens = step_log.output_token_count if step_log.output_token_count is not None else 0
+            token_str = f" | Input-tokens:{input_tokens:,} | Output-tokens:{output_tokens:,}"
             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
+            if step_duration:
+                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="-----")
@@ -143,8 +148,19 @@ def stream_to_gradio(
     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
+            
+            # total_output_tokens += agent.model.last_output_token_count
+
+            if agent.memory.steps:
+                last_step = agent.memory.steps[-1]
+                # import pdb; pdb.set_trace() 
+                if isinstance(last_step, ActionStep) and hasattr(last_step, "token_usage"):
+                    token_usage = last_step.token_usage
+                    if token_usage and isinstance(token_usage, TokenUsage):
+                        total_input_tokens += token_usage.input_tokens
+
+
+            
             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
@@ -155,6 +171,7 @@ def stream_to_gradio(
             yield message
 
     final_answer = step_log  # Last log is the run's final_answer
+    # import pdb; pdb.set_trace()
     final_answer = handle_agent_output_types(final_answer)
 
     if isinstance(final_answer, AgentText):
@@ -173,7 +190,8 @@ def stream_to_gradio(
             content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
         )
     else:
-        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
+        # import pdb; pdb.set_trace()
+        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer.output)}")
 
 
 class GradioUI:
@@ -260,56 +278,71 @@ class GradioUI:
         )
 
     def launch(self, **kwargs):  # <-- Moved inside the class and added self
-        with gr.Blocks("base") as demo:
+        import gradio as gr
+        
+        css = """
+        .gradio-container { height: 100vh !important; }   /* make the whole app viewport‑tall */
+        #chatbot { 
+        flex-grow: 1 !important;    /* fill extra vertical space */
+        overflow: auto !important;  /* scroll when content overflows */
+        }
+        """
+        
+        with gr.Blocks(theme="base", fill_height=True, fill_width=True, css=css) as demo:
+
             # State to store chat messages and prompt strings
             stored_messages = State([])
             prompt_state = State("")
             
-            with gr.Row():    
+            with gr.Sidebar():    
 
                 # Markdown Introduction
                 gr.Markdown("""
-                    # NBAi - NBA Stats Chatbot 🏀
+                    # 🏀 NBAi 🏀
+                    ## NBA Stats Chatbot
                     
-                    Welcome to **NBAi**, your personal NBA statistics assistant! This app fetches and presents NBA box scores from last night's games, giving you insights on player performance, team stats, and more. 
-
+                    Welcome to **NBAi**, your personal NBA statistics assistant! 
+                    
+                    This app fetches and presents NBA box scores from last night's games, giving you insights on player performance, team stats, and more. 
+                    <br/><br/>
                     ## Features
                     - Ask questions like:
                         - Who had the most points last night?
                         - Who grabbed the most rebounds?
                         - Who had the highest assist-to-turnover ratio?
-
+                    <br/><br/>
                     ## Tools Used 
                     - **[smolagents](https://github.com/huggingface/smolagents)** for building multi-step agents.
                     - **[Gradio](https://www.gradio.app/docs)** for the user interface.
                     - **[BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)** and **[Pandas](https://pandas.pydata.org/docs/index.html)** for web scraping and data processing.
-
+                    <br/><br/>
                     ## How to Use 
                     - Click one of the quick prompt buttons below or type your own question.
                     - The chatbot will respond with detailed NBA statistics from last night's games.
                     
                     ---
-                """)
-                with gr.Column():   
-                    # Chatbot Interface
-                    chatbot = Chatbot(
-                        label="NBAi Chatbot",
-                        type="messages",
-                        avatar_images=(
-                            None,
-                            "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
-                        ),
-                        resizeable=True,
-                        scale=1,
-                    )
+                """, elem_id="sidebar-markdown")
+                             # Quick Prompt Buttons
+                with gr.Column():
+                    btn_points = Button(value="Most Points", variant="primary")
+                    btn_rebounds = Button(value="Most Rebounds", variant="primary")
+                    btn_assist_to_turnover = Button(value="Best Assist-to-Turnover Ratio", variant="primary")
+            with gr.Column():   
+                # Chatbot Interface
+                chatbot = Chatbot(
+                    label="NBAi Chatbot",
+                    type="messages",
+                    avatar_images=(
+                        None,
+                        "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
+                    ),
+                    resizeable=True,
+                    elem_id="chatbot"
+                )
 
-                    # Textbox for Custom User Input
-                    text_input = Textbox(lines=1, label="Your Question")
-                    # Quick Prompt Buttons
-                    with gr.Row():
-                        btn_points = Button(value="Most Points", variant="primary")
-                        btn_rebounds = Button(value="Most Rebounds", variant="primary")
-                        btn_assist_to_turnover = Button(value="Best Assist-to-Turnover Ratio", variant="primary")
+                # Textbox for Custom User Input
+                text_input = Textbox(lines=1, label="Your Question")
+   
 
  
 

Gradio_UI3.py

@@ -0,0 +1,381 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import mimetypes
+import os
+import re
+import shutil
+from typing import Optional
+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
+import gradio as gr
+from gradio.components import Markdown
+from gradio.components import Chatbot, Textbox, State, Button
+
+from smolagents.memory import TokenUsage
+
+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
+        # Calculate duration and token information
+        step_footnote = f"{step_number}"
+        if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
+            input_tokens = step_log.input_token_count if step_log.input_token_count is not None else 0
+            output_tokens = step_log.output_token_count if step_log.output_token_count is not None else 0
+            token_str = f" | Input-tokens:{input_tokens:,} | Output-tokens:{output_tokens:,}"
+            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
+            if step_duration:
+                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_output_tokens += agent.model.last_output_token_count
+
+            if agent.memory.steps:
+                last_step = agent.memory.steps[-1]
+                # import pdb; pdb.set_trace() 
+                if isinstance(last_step, ActionStep) and hasattr(last_step, "token_usage"):
+                    token_usage = last_step.token_usage
+                    if token_usage and isinstance(token_usage, TokenUsage):
+                        total_input_tokens += token_usage.input_tokens
+
+
+            
+            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
+    # import pdb; pdb.set_trace()
+    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:
+        # import pdb; pdb.set_trace()
+        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer.output)}")
+
+
+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):  # <-- Moved inside the class and added self
+        import gradio as gr
+        
+        css = """
+        .gradio-container { height: 100vh !important; }   /* make the whole app viewport‑tall */
+        #chatbot { 
+        flex-grow: 1 !important;    /* fill extra vertical space */
+        overflow: auto !important;  /* scroll when content overflows */
+        }
+        """
+        
+        with gr.Blocks(theme="base", fill_height=True, fill_width=True, css=css) as demo:
+
+            # State to store chat messages and prompt strings
+            stored_messages = State([])
+            prompt_state = State("")
+            
+            with gr.Sidebar():    
+
+                # Markdown Introduction
+                gr.Markdown("""
+                    # 🏀 NBAi 🏀
+                    ## NBA Stats Chatbot
+                    
+                    Welcome to **NBAi**, your personal NBA statistics assistant! 
+                    
+                    This app fetches and presents NBA box scores from last night's games, giving you insights on player performance, team stats, and more. 
+                    <br/><br/>
+                    ## Features
+                    - Ask questions like:
+                        - Who had the most points last night?
+                        - Who grabbed the most rebounds?
+                        - Who had the highest assist-to-turnover ratio?
+                    <br/><br/>
+                    ## Tools Used 
+                    - **[smolagents](https://github.com/huggingface/smolagents)** for building multi-step agents.
+                    - **[Gradio](https://www.gradio.app/docs)** for the user interface.
+                    - **[BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)** and **[Pandas](https://pandas.pydata.org/docs/index.html)** for web scraping and data processing.
+                    <br/><br/>
+                    ## How to Use 
+                    - Click one of the quick prompt buttons below or type your own question.
+                    - The chatbot will respond with detailed NBA statistics from last night's games.
+                    
+                    ---
+                """, elem_id="sidebar-markdown")
+                             # Quick Prompt Buttons
+                with gr.Column():
+                    btn_points = Button(value="Most Points", variant="primary")
+                    btn_rebounds = Button(value="Most Rebounds", variant="primary")
+                    btn_assist_to_turnover = Button(value="Best Assist-to-Turnover Ratio", variant="primary")
+            with gr.Column():   
+                # Chatbot Interface
+                chatbot = Chatbot(
+                    label="NBAi Chatbot",
+                    type="messages",
+                    avatar_images=(
+                        None,
+                        "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
+                    ),
+                    resizeable=True,
+                    elem_id="chatbot"
+                )
+
+                # Textbox for Custom User Input
+                text_input = Textbox(lines=1, label="Your Question")
+   
+
+ 
+
+            # Bindings for Buttons using prompt_state
+            btn_points.click(
+                lambda: "Who had the most points in last night's NBA games?",
+                [],
+                prompt_state
+            ).then(self.interact_with_agent, [prompt_state, stored_messages], chatbot)
+
+            btn_rebounds.click(
+                lambda: "Who had the most rebounds in last night's NBA games?",
+                [],
+                prompt_state
+            ).then(self.interact_with_agent, [prompt_state, stored_messages], chatbot)
+
+            btn_assist_to_turnover.click(
+                lambda: "Who had the highest ratio of assists to turnovers in last night's NBA games?",
+                [],
+                prompt_state
+            ).then(self.interact_with_agent, [prompt_state, stored_messages], chatbot)
+
+            # Custom Input Submission
+            text_input.submit(
+                self.interact_with_agent,
+                [text_input, stored_messages],
+                chatbot
+            )
+        
+        
+        
+        
+        demo.launch(debug=True, share=True, **kwargs)
+
+
+__all__ = ["stream_to_gradio", "GradioUI"]

app.py

@@ -12,6 +12,10 @@ from bs4 import BeautifulSoup  # Fixed Import
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
+import pdb
+
+
+
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 
 @tool
@@ -135,13 +139,16 @@ search_tool = DuckDuckGoSearchTool()
 visit_webpage_tool = VisitWebpageTool()
 user_input_tool = UserInputTool()
 
-model = HfApiModel(
-    max_tokens=2096,
-    temperature=0.5,
-    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
-    custom_role_conversions=None,
+import os 
+from smolagents import OpenAIServerModel
+
+model = OpenAIServerModel(
+    model_id="gpt-4o",             
+    api_base="https://api.openai.com/v1", 
+    api_key=os.environ["OPENAI_API_KEY"] 
 )
 
+ 
 # Import tool from Hub
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
@@ -149,6 +156,8 @@ image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_co
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
     
+# pdb.set_trace() 
+
 # Setup the Agent
 agent = CodeAgent(
     model=model,

prompts.yaml

@@ -319,3 +319,11 @@
   "report": |-
     Here is the final answer from your managed agent '{{name}}':
     {{final_answer}}
+
+final_answer:
+  pre_messages: |-
+    You are about to provide a final answer based on the task and steps performed.
+  post_messages: |-
+    Now write your final answer below using everything you have learned. End with a short summary of your reasoning.
+
+    Thank you for using the NBA Box Scores Agent. Please contact justin@viz-explainer if you have any questions.

requirements.txt

@@ -1,7 +1,9 @@
 markdownify
-smolagents==1.13.0
+git+https://github.com/huggingface/smolagents.git
 requests
 duckduckgo_search
 pandas
 matplotlib
-bs4
+bs4
+gradio
+smolagents[openai]