Update app.py

app.py

@@ -3,8 +3,8 @@ import gradio as gr
 import requests
 import pandas as pd
 from datetime import datetime
-from smolagents import Tool, ToolCallingAgent
-from smolagents.models import InferenceClientModel
+from transformers import pipeline, Tool
+from transformers.agents import Agent
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -13,8 +13,11 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 class CalculatorTool(Tool):
     name = "calculator"
     description = "Performs mathematical calculations"
+    inputs = {"expression": {"type": "text"}}
+    outputs = {"result": {"type": "text"}}
+    output_type = "text"  # Added required attribute
     
-    def use(self, expression: str) -> str:
+    def __call__(self, expression: str) -> str:
         try:
             return str(eval(expression))
         except:
@@ -23,26 +26,27 @@ class CalculatorTool(Tool):
 class TimeTool(Tool):
     name = "current_time"
     description = "Gets current UTC time"
+    inputs = {}
+    outputs = {"time": {"type": "text"}}
+    output_type = "text"  # Added required attribute
     
-    def use(self) -> str:
+    def __call__(self) -> str:
         return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
 
 # --- Enhanced Agent ---
-class LocalAgent:
+class HFLocalAgent:
     def __init__(self):
-        print("Initializing local agent with smolagents...")
+        print("Initializing local Hugging Face agent...")
         self.tools = {
             "calculator": CalculatorTool(),
             "time": TimeTool()
         }
         
-        # Using a local model through smolagents
-        self.agent = ToolCallingAgent(
-            tools=list(self.tools.values()),
-            model=InferenceClientModel(
-                model_id="HuggingFaceH4/zephyr-7b-beta",
-                api_base="https://api-inference.huggingface.co/models"
-            )
+        # Load local model (small but efficient)
+        self.llm = pipeline(
+            "text-generation",
+            model="HuggingFaceH4/zephyr-7b-beta",
+            device="cpu"  # Change to "cuda" if GPU available
         )
         
     def __call__(self, question: str) -> str:
@@ -51,17 +55,22 @@ class LocalAgent:
         
         # Math questions
         if any(word in question_lower for word in ["calculate", "what is", "how much is", "+", "-", "*", "/"]):
-            return self.tools["calculator"].use(question.replace("?", ""))
+            return self.tools["calculator"](question.replace("?", ""))
         
         # Time questions
         if any(word in question_lower for word in ["time", "current time"]):
-            return self.tools["time"].use()
+            return self.tools["time"]()
         
-        # Fallback to agent with tools
+        # Fallback to local LLM
         try:
-            return self.agent.run(question)
+            response = self.llm(
+                f"Answer concisely: {question}",
+                max_new_tokens=100,
+                temperature=0.7
+            )
+            return response[0]['generated_text'].split(":")[-1].strip()
         except Exception as e:
-            print(f"Agent error: {e}")
+            print(f"LLM error: {e}")
             return "I couldn't process this question."
 
 # --- Evaluation Runner ---
@@ -73,7 +82,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     api_url = os.getenv("API_URL", DEFAULT_API_URL)
     
     try:
-        agent = LocalAgent()
+        agent = HFLocalAgent()
         response = requests.get(f"{api_url}/questions", timeout=15)
         response.raise_for_status()
         questions = response.json()
@@ -119,10 +128,10 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         return f"Evaluation failed: {str(e)}", pd.DataFrame(results if 'results' in locals() else [])
 
 # --- Gradio Interface ---
-with gr.Blocks(title="Local Agent Evaluator") as app:
+with gr.Blocks(title="Local HF Agent Evaluator") as app:
     gr.Markdown("""
-    ## Local Agent Evaluation
-    Uses smolagents with local Hugging Face models
+    ## Local Hugging Face Agent Evaluation
+    Uses completely free/local models - no API keys required
     """)
     
     gr.LoginButton()