Update app.py

app.py

@@ -1,47 +1,70 @@
 import os
 import gradio as gr
-from langchain.agents import initialize_agent, Tool
-from langchain.agents.agent_types import AgentType
-from langchain.tools import DuckDuckGoSearchRun
-from langchain.llms import OpenAI
-from langchain.utilities import SerpAPIWrapper
-from langchain_core.tools import Tool
-
-# Set your API key
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Initialize the LLM
-llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key)
-
-# Define tools the agent can use
-search = DuckDuckGoSearchRun()
-
-tools = [
-    Tool(
-        name="DuckDuckGo Search",
-        func=search.run,
-        description="Useful for answering questions about current events or factual information."
+import requests
+import pandas as pd
+from smolagents import Agent
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# A simple agent using Hugging Face hosted inference API
+class HuggingFaceAPIAgent(Agent):
+    def __init__(self, model="google/flan-t5-small"):
+        self.model = model
+        self.api_url = f"https://api-inference.huggingface.co/models/{model}"
+        self.headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}
+
+    def __call__(self, question: str) -> str:
+        print(f"Calling HF inference API for: {question[:60]}")
+        response = requests.post(
+            self.api_url,
+            headers=self.headers,
+            json={"inputs": question},
+            timeout=10
+        )
+        if response.status_code != 200:
+            print(f"API error: {response.status_code} {response.text}")
+            return "API call failed."
+        return response.json()[0]["generated_text"]
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if not profile:
+        return "Please log in first.", None
+
+    username = profile.username
+    agent_code = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main"
+    agent = HuggingFaceAPIAgent()
+
+    questions = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15).json()
+    answers, log = [], []
+
+    for q in questions:
+        try:
+            answer = agent(q["question"])
+        except Exception as e:
+            answer = f"Error: {e}"
+        answers.append({"task_id": q["task_id"], "submitted_answer": answer})
+        log.append({"Task ID": q["task_id"], "Question": q["question"], "Submitted Answer": answer})
+
+    result = requests.post(f"{DEFAULT_API_URL}/submit", json={
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers
+    }).json()
+
+    message = (
+        f"✅ Submission complete!\n"
+        f"Score: {result.get('score')}%\n"
+        f"{result.get('correct_count')}/{result.get('total_attempted')} correct\n"
+        f"Message: {result.get('message')}"
     )
-]
-
-# Initialize the agent
-agent = initialize_agent(
-    tools=tools,
-    llm=llm,
-    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=True
-)
-
-# Gradio interface
-def agent_response(user_input):
-    return agent.run(user_input)
-
-demo = gr.Interface(
-    fn=agent_response,
-    inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),
-    outputs="text",
-    title="🔍 AI Agent with Tools",
-    description="This agent uses a language model with optional web search via DuckDuckGo to answer your queries."
-)
-
-demo.launch()
+    return message, pd.DataFrame(log)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 SmolAgent with Hugging Face Inference API")
+    gr.LoginButton()
+    btn = gr.Button("Run Agent & Submit")
+    status = gr.Textbox(label="Status")
+    results = gr.DataFrame(label="Results")
+    btn.click(fn=run_and_submit_all, outputs=[status, results])
+
+demo.launch()