Update app.py

app.py

@@ -1,179 +1,15 @@
-import os
-import datetime
-import requests
-import pytz
-import yaml
-from typing import List
-
-from langchain.text_splitter import CharacterTextSplitter
-from tools.final_answer import FinalAnswerTool
-from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
-from Gradio_UI import GradioUI
-
-# === TOOLS ===
-
-@tool
-def web_search(query: str) -> str:
-    """Allows search through DuckDuckGo.
-    Args:
-        query: what you want to search
-    """
-    search_tool = DuckDuckGoSearchTool()
-    results = search_tool(query)
-    return "\n".join(results)
-
-@tool
-def get_current_time_in_timezone(timezone: str) -> str:
-    """Fetches the current local time in a specified timezone.
-    Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
-    """
-    try:
-        tz = pytz.timezone(timezone)
-        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
-        return f"The current local time in {timezone} is: {local_time}"
-    except Exception as e:
-        return f"Error fetching time for timezone '{timezone}': {str(e)}"
-
-@tool
-def visit_webpage(url: str) -> str:
-    """Fetches raw HTML content of a web page.
-    Args:
-        url: The url of the webpage.
-    """
-    try:
-        response = requests.get(url, timeout=5)
-        return response.text[:5000]  # Limit length
-    except Exception as e:
-        return f"[ERROR fetching {url}]: {str(e)}"
-
-@tool
-def text_splitter(text: str) -> List[str]:
-    """Splits text into chunks using LangChain's CharacterTextSplitter.
-    Args:
-        text: A string of text to split.
-    """
-    splitter = CharacterTextSplitter(chunk_size=450, chunk_overlap=10)
-    return splitter.split_text(text)
-
-# === FINAL ANSWER TOOL ===
-final_answer = FinalAnswerTool()
-
-# === LOAD PROMPT TEMPLATES ===
-with open("prompts.yaml", "r") as stream:
-    prompt_templates = yaml.safe_load(stream)
-
-# === LOAD agent.json CONFIG ===
-with open("agent.json", "r") as f:
-    agent_config = yaml.safe_load(f)
-
-model_config = agent_config["model"]["data"]
-
-# === BUILD MODEL ===
-model = LiteLLMModel(
-    model_id="gemini/gemini-2.0-flash-lite",
-    api_key=os.getenv("GEMINI_API_KEY"),
-    temperature=0.5,
-    max_tokens=1024,
-)
-
-# 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' (!!!)
-
-# model = HfApiModel(
-#     #model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-#     model_id="mistralai/Mistral-7B-Instruct-v0.2",
-#     token=os.getenv("HF_TOKEN"),
-#     max_tokens=2096,
-#     temperature=0.5,
-#     last_input_token_count=0,
-#     last_output_token_count=0,
-# )
-
-
-
-
-
-# === IMPORT TOOL FROM HUB ===
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
-
-# === BUILD AGENT ===
-agent = CodeAgent(
-    model=model,
-    tools=[
-        final_answer,
-        web_search,
-        get_current_time_in_timezone,
-        visit_webpage,
-        text_splitter,
-        image_generation_tool
-    ],
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
-
-# === LAUNCH UI ===
-GradioUI(agent).launch() # new change 6:36 - for evaluation API
-
+# app.py (for first_agent_template)
 import gradio as gr
+from agent import agent  # Import your refactored agent
+from Gradio_UI import GradioUI
 
-# # OLD VERSION
-# def run_agent(question):
-#     try:
-#         result = agent(question)
-#         return [str(result)]  # Must return a list with one string (like ["answer"])
-#     except Exception as e:
-#         return [f"Error: {e}"]
-
-# # NEW VERSION
-# def run_agent(question):
-#     try:
-#         # Get all steps from the agent run
-#         steps = list(agent.run(question, stream=False))
-
-#         # Look through all tool calls to find final_answer
-#         for step in steps:
-#             if hasattr(step, "tool_calls") and step.tool_calls:
-#                 for call in step.tool_calls:
-#                     if call.name == "final_answer":
-#                         answer = call.arguments.get("answer", None)
-#                         if answer:
-#                             return [str(answer)]
-
-#         # Fallback: try tool_output or .final_answer if FinalAnswerStep
-#         for step in reversed(steps):
-#             if hasattr(step, "tool_output") and step.tool_output:
-#                 return [str(step.tool_output)]
-#             if hasattr(step, "final_answer") and step.final_answer:
-#                 return [str(step.final_answer)]
-
-#         return ["null"]
-#     except Exception as e:
-#         return [f"Error: {e}"]
-
-# # NEWER VERSION (should just return the result)
-# import re
-
+# Optional: Uncomment to test agent endpoint separately
 # def run_agent(question):
 #     try:
 #         result = agent(question)
-
-#         # If result is a string and contains "### 1. Task outcome", extract that
-#         if isinstance(result, str):
-#             match = re.search(r"### 1\. Task outcome \(short version\):\s*(.+)", result)
-#             if match:
-#                 return [match.group(1).strip()]  # return just the short answer
-#             return [result.strip()]
-
 #         return [str(result)]
 #     except Exception as e:
 #         return [f"Error: {e}"]
 
-
-demo = gr.Interface(fn=run_agent, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+    GradioUI(agent).launch(debug=True, share=True)