Update app.py

app.py

@@ -181,264 +181,6 @@ class ToolRegistry:
             raise
 
 
-def create_agent():
-    """Creates a fresh agent instance with properly configured tools."""
-    # Initialize model
-    model = LiteLLMModel(
-        custom_role_conversions=CUSTOM_ROLE_CONVERSIONS,
-        model_id="openrouter/deepseek/deepseek-chat-v3-0324:free",  # currently serving:
-    )  # DEEPSEEK = openrouter/perplexity/r1-1776 <--- boss model
-
-    # Initialize tools
-    text_limit = 30000
-    browser = SimpleTextBrowser(**BROWSER_CONFIG)
-
-    # Collect all tools in a single list
-    web_tools = ToolRegistry.load_web_tools(model, browser, text_limit)
-    image_generator = ToolRegistry.load_image_generation_tools()
-    clean_text = TextCleanerTool()  # Instantiate TextCleanerTool
-
-    # Combine all tools into a single list (not a tuple)
-    all_tools = [visualizer] + web_tools + [image_generator] + [clean_text]
-
-    # Validate tools before creating agent
-    for tool in all_tools:
-        if not isinstance(tool, Tool):
-            raise ValueError(
-                "Invalid tool type: "
-                f"{type(tool)}. All tools must be instances of Tool class."
-            )
-
-    return CodeAgent(
-        model=model,
-        tools=all_tools,  # Pass a single list containing all tools
-        max_steps=12,
-        verbosity_level=2,
-        additional_authorized_imports=AUTHORIZED_IMPORTS,
-        planning_interval=1,
-    )
-
-
-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 messages as Gradio ChatMessages."""
-    for step_log in agent.run(
-        task, stream=True, reset=reset_agent_memory, additional_args=additional_args
-    ):
-        yield from pull_messages_from_step(step_log)
-
-    # Process final answer : Use a more comprehensive media output
-    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={"image": final_answer.to_string(), "type": "file"},
-        )  # Send as Gradio-compatible file object:
-    elif isinstance(final_answer, AgentAudio):
-        yield gr.ChatMessage(
-            role="assistant",
-            content={"audio": final_answer.to_string(), "type": "file"},
-        )  # Send as Gradio-compatible file object
-    else:
-        yield gr.ChatMessage(
-            role="assistant", content=f"**Final answer:** {str(final_answer)}"
-        )
-
-
-"""Main application for the OpenDeepResearch Gradio interface."""
-
-import mimetypes
-import os
-import re
-import shutil
-from typing import Optional
-
-from dotenv import load_dotenv
-from huggingface_hub import login
-import gradio as gr
-
-from scripts.text_inspector_tool import TextInspectorTool
-from scripts.text_web_browser import (
-    ArchiveSearchTool,
-    FinderTool,
-    FindNextTool,
-    PageDownTool,
-    PageUpTool,
-    SimpleTextBrowser,
-    VisitTool,
-)
-from scripts.visual_qa import visualizer
-from scripts.text_cleaner_tool import TextCleanerTool
-
-from smolagents import (
-    CodeAgent,
-    HfApiModel,
-    LiteLLMModel,
-    OpenAIServerModel,
-    TransformersModel,
-    GoogleSearchTool,
-    Tool,
-)
-from smolagents.agent_types import AgentText, AgentImage, AgentAudio
-from smolagents.gradio_ui import pull_messages_from_step, handle_agent_output_types
-
-
-# Constants and configurations
-AUTHORIZED_IMPORTS = [
-    "requests",
-    "zipfile",
-    "pandas",
-    "numpy",
-    "sympy",
-    "json",
-    "bs4",
-    "pubchempy",
-    "xml",
-    "yahoo_finance",
-    "Bio",
-    "sklearn",
-    "scipy",
-    "pydub",
-    "PIL",
-    "chess",
-    "PyPDF2",
-    "pptx",
-    "torch",
-    "datetime",
-    "fractions",
-    "csv",
-    "clean-text",
-]
-
-USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"
-BROWSER_CONFIG = {
-    "viewport_size": 1024 * 5,
-    "downloads_folder": "downloads_folder",
-    "request_kwargs": {
-        "headers": {"User-Agent": USER_AGENT},
-        "timeout": 300,
-    },
-    "serpapi_key": os.getenv("SERPAPI_API_KEY"),
-}
-
-CUSTOM_ROLE_CONVERSIONS = {"tool-call": "assistant", "tool-response": "user"}
-
-
-ALLOWED_FILE_TYPES = [
-    "application/pdf",
-    "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-    "text/plain",
-    "text/markdown",  # Added Markdown support
-    "application/json",  # Added JSON support
-    "image/png",
-    "image/webp",
-    "image/jpeg",  # Added JPEG support
-    "image/gif",  # Added GIF support
-    "video/mp4",
-    "audio/mpeg",  # Added MP3 support
-    "audio/wav",  # Added WAV support
-    "audio/ogg",  # Added OGG support
-]
-
-
-def setup_environment():
-    """Initialize environment variables and authentication."""
-    load_dotenv(override=True)
-    hf_token = os.getenv("HF_TOKEN")
-    if hf_token:  # Check if token is actually set
-        login(hf_token)
-        print("HF_TOKEN (last 10 characters):", hf_token[-10:])
-    else:
-        print("HF_TOKEN not found in environment variables.")
-
-
-class ModelManager:
-    """Manages model loading and initialization."""
-
-    @staticmethod
-    def load_model(chosen_inference: str, model_id: str, key_manager=None):
-        """Load the specified model with appropriate configuration."""
-        try:
-            if chosen_inference == "hf_api":
-                return HfApiModel(model_id=model_id)
-
-            if chosen_inference == "hf_api_provider":
-                return HfApiModel(provider="together")
-
-            if chosen_inference == "litellm":
-                return LiteLLMModel(model_id=model_id)
-
-            if chosen_inference == "openai":
-                if not key_manager:
-                    raise ValueError("Key manager required for OpenAI model")
-                return OpenAIServerModel(
-                    model_id=model_id, api_key=key_manager.get_key("openai_api_key")
-                )
-
-            if chosen_inference == "transformers":
-                return TransformersModel(
-                    model_id="HuggingFaceTB/SmolLM2-1.7B-Instruct",
-                    device_map="auto",
-                    max_new_tokens=1000,
-                )
-
-            raise ValueError(f"Invalid inference type: {chosen_inference}")
-        except Exception as e:
-            print(f"✗ Couldn't load model: {e}")
-            raise
-
-
-class ToolRegistry:
-    """Manages tool initialization and organization."""
-
-    @staticmethod
-    def load_web_tools(model, browser, text_limit=20000):
-        """Initialize and return web-related tools."""
-        return [
-            GoogleSearchTool(provider="serper"),
-            VisitTool(browser),
-            PageUpTool(browser),
-            PageDownTool(browser),
-            FinderTool(browser),
-            FindNextTool(browser),
-            ArchiveSearchTool(browser),
-            TextInspectorTool(model, text_limit),
-        ]
-
-    @staticmethod
-    def load_image_generation_tools():
-        """Initialize and return image generation tools."""
-        try:
-            return Tool.from_space(
-                space_id="xkerser/FLUX.1-dev",
-                name="image_generator",
-                description="Generates high-quality AgentImage with text prompt (77 token limit).",
-            )
-        except Exception as e:
-            print(f"✗ Couldn't initialize image generation tool: {e}")
-            raise
-
-    @staticmethod
-    def load_clean_text_tool():
-        """Initialize and return image generation tools."""
-        try:
-            return TextCleanerTool
-        except Exception as e:
-            print(f"✗ Couldn't initialize clean text tool: {e}")
-            raise
-
-
 def create_agent():
     """Creates a fresh agent instance with properly configured tools."""
     # Initialize model
@@ -880,4 +622,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-