Update app.py

app.py

@@ -248,7 +248,6 @@
 
 # 
 
-##################
 import os
 import io
 import requests
@@ -261,8 +260,8 @@ import operator
 # --- LangChain & LangGraph Imports ---
 from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage, AIMessage, SystemMessage
 from langchain_core.tools import tool
-# <<<--- CHANGE 1: Import Google Gemini instead of OpenAI --->>>
-from langchain_google_genai import ChatGoogleGenerativeAI
+# <<<--- CHANGE: Import the HuggingFaceEndpoint for open-source models --->>>
+from langchain_huggingface import HuggingFaceEndpoint
 from langgraph.graph import StateGraph, END
 from langgraph.prebuilt import ToolNode
 from tavily import TavilyClient
@@ -273,7 +272,7 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 FILES_DIR = "./files"
 os.makedirs(FILES_DIR, exist_ok=True)
 
-# --- System Prompt (Unchanged, it's strong) ---
+# --- System Prompt (Unchanged, it's strong and model-agnostic) ---
 AGENT_SYSTEM_PROMPT = """You are a world-class AI agent, specialized in solving complex problems from the GAIA benchmark.
 Your task is to analyze the user's question, think step-by-step, and use the provided tools to find the correct answer.
 CRITICAL INSTRUCTIONS:
@@ -292,7 +291,7 @@ Think, use your tools, and then provide ONLY the final, precise answer.
 
 #
 # ================================================================================================
-#  ✅ 1. AGENT'S TOOLS (Unchanged)
+#  ✅ 1. DEFINE THE AGENT'S TOOLS (Unchanged)
 # ================================================================================================
 #
 tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
@@ -314,7 +313,6 @@ def read_file(url: str) -> str:
     """
     Downloads a file from a given URL, saves it locally, and returns its content.
     It can handle both plain text files and PDF files.
-    Use this tool when a question provides a URL to a file that needs to be read.
     """
     print(f"--- Calling Read File Tool with URL: {url} ---")
     try:
@@ -363,7 +361,7 @@ def python_interpreter(code: str) -> str:
 
 #
 # ================================================================================================
-#  ✅ 2. CONFIGURE AND BUILD THE AGENT GRAPH (NOW WITH GEMINI 1.5 PRO)
+#  ✅ 2. CONFIGURE AND BUILD THE AGENT GRAPH (WITH HUGGING FACE)
 # ================================================================================================
 #
 class AgentState(TypedDict):
@@ -373,24 +371,37 @@ def build_agent_graph():
     """Builds the LangGraph agent."""
     tools = [tavily_search, read_file, python_interpreter]
     
-    # <<<--- CHANGE 2: Instantiate the Gemini Model --->>>
-    # It will use the GOOGLE_API_KEY from your secrets.
-    # Note: `convert_system_message_to_human=True` is a useful flag for compatibility,
-    # ensuring our powerful system prompt is always understood correctly by the Gemini model.
-    llm = ChatGoogleGenerativeAI(
-        model="gemini-1.5-pro-latest",
-        temperature=0,
-        convert_system_message_to_human=True
+    # <<<--- CHANGE: Instantiate the Hugging Face Model Endpoint --->>>
+    # This uses the recommended Command R+ model for its excellent tool-use capabilities.
+    # It will automatically use the HUGGINGFACEHUB_API_TOKEN secret.
+    repo_id = "CohereForAI/c4ai-command-r-plus"
+    llm = HuggingFaceEndpoint(
+        repo_id=repo_id,
+        max_new_tokens=1024,
+        temperature=0, # Keep temperature low for fact-based tasks
+        huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN")
     )
     
     llm_with_tools = llm.bind_tools(tools)
 
     def call_model(state: AgentState) -> dict:
+        """Helper function to prepare messages and call the model."""
         messages = state['messages']
-        response = llm_with_tools.invoke(messages)
+        # The HuggingFaceEndpoint doesn't support a separate SystemMessage.
+        # We'll format the system prompt and the latest human message together.
+        if isinstance(messages[0], SystemMessage):
+            # Start with the system message content
+            formatted_messages = [HumanMessage(content=messages[0].content + "\n\nHere is the user's question:\n" + messages[-1].content)]
+            # Add any previous tool outputs
+            formatted_messages.extend(messages[1:-1])
+        else:
+            formatted_messages = messages
+
+        response = llm_with_tools.invoke(formatted_messages)
         return {"messages": [response]}
 
     def should_continue(state: AgentState) -> str:
+        """Determines whether to continue the loop or end."""
         return "action" if state['messages'][-1].tool_calls else "end"
 
     tool_node = ToolNode(tools)
@@ -404,13 +415,13 @@ def build_agent_graph():
 
 #
 # ================================================================================================
-#  ✅ 3. AGENT CLASS AND EVALUATION LOGIC (Unchanged)
+#  ✅ 3. AGENT CLASS AND EVALUATION LOGIC
 # ================================================================================================
 #
 class GaiaAgent:
     def __init__(self):
-        # <<<--- CHANGE 3: Update the print statement for clarity --->>>
-        print("GaiaAgent initialized. Building fresh Gemini 1.5 Pro graph...")
+        # <<<--- CHANGE: Update print statement for new model --->>>
+        print("GaiaAgent initialized. Building fresh Command R+ agent graph...")
         self.agent_app = build_agent_graph()
 
     def __call__(self, question: str) -> str:
@@ -422,8 +433,7 @@ class GaiaAgent:
             ]
         }
         final_state = None
-        # We increase the recursion limit slightly, as some models might take an extra step for reasoning.
-        for i, step in enumerate(self.agent_app.stream(initial_input, {"recursion_limit": 20})):
+        for i, step in enumerate(self.agent_app.stream(initial_input, {"recursion_limit": 15})):
             if i == 0: print("--- Starting Agentic Loop ---")
             final_state = step
 
@@ -432,7 +442,6 @@ class GaiaAgent:
         print(f"\n--- Agent finished. Final Answer: {final_answer} ---\n")
         return final_answer
 
-# (The rest of the file remains exactly the same)
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
     if not profile: return "Please Login to Hugging Face with the button.", None
@@ -483,7 +492,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
             f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_a.get('total_attempted', '?')} correct)\n"
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
         results_df = pd.DataFrame(results_log)
@@ -495,14 +504,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 # --- Gradio Interface ---
 with gr.Blocks() as demo:
-    # <<<--- CHANGE 4: Update the title in the UI --->>>
-    gr.Markdown("# GAIA Agent Final Assessment (V6 - Gemini 1.5 Pro)")
+    # <<<--- CHANGE: Update UI titles and descriptions for the new model --->>>
+    gr.Markdown("# GAIA Agent Final Assessment (Open Source: Command R+)")
     gr.Markdown(
         """
-        **Instructor's Note:** This version has been upgraded to use Google's `gemini-1.5-pro-latest` model.
-        1.  Ensure `GOOGLE_API_KEY` and `TAVILY_API_KEY` are set in your Space secrets.
-        2.  Ensure `requirements.txt` is updated with `langchain-google-genai`.
-        3.  Let's see how Gemini performs! Good luck.
+        **Instructor's Note:** This version runs a top-tier open-source model from the Hugging Face Hub: **`CohereForAI/c4ai-command-r-plus`**.
+        This model is state-of-the-art for agentic tool use.
+        1.  Ensure you have a **`HUGGINGFACEHUB_API_TOKEN`** and a **`TAVILY_API_KEY`** set in your Space secrets.
+        2.  Ensure your `requirements.txt` includes `langchain-huggingface`.
+        3.  Good luck! Let's see how this powerful open model performs.
         """
     )
     gr.LoginButton()