Update app.py

app.py

@@ -5,12 +5,8 @@ import inspect
 import pandas as pd
 from dotenv import load_dotenv
 from typing import List
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_experimental.tools import PythonREPLTool
-from langchain_community.vectorstores import FAISS
-from langchain_core.documents import Document
-from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace, HuggingFaceEmbeddings
-from langchain_core.messages import HumanMessage
+import agent
+
 
 
 
@@ -36,7 +32,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-    HF_KEY = os.getenv("HF_TOKEN")
+    
 
     if profile:
         username= f"{profile.username}"
@@ -52,161 +48,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
       
-     llm = ChatHuggingFace(
-     llm=HuggingFaceEndpoint(
-        repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-        huggingfacehub_api_token=HF_KEY,
-        task="text-generation",
-       )
-     )
+     agent=agent.run();
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
 
-
-# -----------------------------
-# Tools
-# -----------------------------
-search = DuckDuckGoSearchRun()
-python_tool = PythonREPLTool()
-
-TOOLS = {
-    "search": search.run,
-    "python": python_tool.run,
-    "llm": lambda x: llm.invoke([HumanMessage(content=x)]).content,
-    "summarize": lambda text: llm.invoke([HumanMessage(content=f"Summarize the following:\n{text}")]).content
-}
-
-# -----------------------------
-# Memory (Vector DB)
-# -----------------------------
-embeddings = HuggingFaceEmbeddings()
-# Initialize FAISS with a dummy document to prevent IndexError when trying to determine embedding dimension
-vectorstore = FAISS.from_documents([Document(page_content="initialization_document_for_dimension_inference")], embeddings)
-
-def store_memory(text: str):
-    vectorstore.add_documents([Document(page_content=text)])
-
-def retrieve_memory(query: str):
-    docs = vectorstore.similarity_search(query, k=3)
-    return "\n".join([d.page_content for d in docs])
-
-# -----------------------------
-# Planner
-# -----------------------------
-def plan(goal, history):
-    prompt = f"""
-You are an autonomous agent.
-
-Goal: {goal}
-
-Previous steps:
-{history}
-
-Decide the NEXT action:
-- search(query)
-- python(code)
-- llm(prompt)
-- summarize(text)
-- finish(answer)
-
-Respond ONLY in one line.
-"""
-    return llm.invoke([HumanMessage(content=prompt)]).content.strip()
-
-# -----------------------------
-# Executor
-# -----------------------------
-def execute(action: str):
-    try:
-        if action.startswith("search("):
-            query = action[len("search("):-1]
-            return TOOLS["search"](query)
-
-        elif action.startswith("python("):
-            code = action[len("python("):-1]
-            return TOOLS["python"](code)
-
-        elif action.startswith("llm("):
-            prompt = action[len("llm("):-1]
-            return TOOLS["llm"](prompt)
-
-        elif action.startswith("summarize("):
-            text_to_summarize = action[len("summarize("):-1]
-            return TOOLS["summarize"](text_to_summarize)
-
-        elif action.startswith("finish("):
-            return action[len("finish("):-1]
-
-        else:
-            return "Invalid action"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# -----------------------------
-# Critic (loop control)
-# -----------------------------
-def critic(goal, last_result):
-    prompt = f"""
-Goal: {goal}
-
-Latest result:
-{last_result}
-
-Is the goal achieved? Answer YES or NO.
-"""
-    return "YES" in llm.invoke([HumanMessage(content=prompt)]).content.upper()
-
-# -----------------------------
-# Autonomous Loop
-# -----------------------------
-def autonomous_agent(goal: str, max_steps=15):
-
-    history = ""
-    print(f"\n🎯 Goal: {goal}\n")
-
-    for step in range(max_steps):
-        print(f"--- Step {step+1} ---")
-
-        # Retrieve memory
-        memory_context = retrieve_memory(goal)
-
-        action = plan(goal, history + "\nMemory:\n" + memory_context)
-        print(f"🧠 Plan: {action}")
-
-        result = execute(action)
-        print(f"⚙️ Result: {result[:300]}...\n")
-
-        # Store memory
-        store_memory(f"Action: {action}\nResult: {result}")
-
-        history += f"\nStep {step+1}: {action} → {result}"
-
-        # Finish condition
-        if action.startswith("finish("):
-            print("✅ Finished by agent")
-            return result
-
-        # Critic check
-        if critic(goal, result):
-            print("✅ Critic determined goal achieved")
-            return result
-
-    return "❌ Max steps reached without completion"
-
-# -----------------------------
-# Run
-# -----------------------------
-if __name__ == "__main__":
-    while True:
-        goal = input("\nEnter goal (or 'exit'): ")
-        if goal == "exit":
-            break
-
-        result = autonomous_agent(goal)
-        print(f"\n🤖 Final Output:\n{result}\n")
-    
     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)