first commit

README.md

@@ -1,13 +1,15 @@
 ---
-title: My Agent
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
+title: Template Final Assignment
+emoji: 🕵🏻‍♂️
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.31.0
+sdk_version: 5.25.2
 app_file: app.py
 pinned: false
-license: mit
+hf_oauth: true
+# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
+hf_oauth_expiration_minutes: 480
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,196 @@
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # 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)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            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_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)

basic_agent.py

@@ -0,0 +1,272 @@
+"""
+A LangGraph-powered agent with at least 14 tools for question answering.
+"""
+import wikipedia
+import sympy
+import requests
+from datetime import datetime
+from dateutil import parser as date_parser
+from duckduckgo_search import DDGS
+import pytz
+import openai
+import wolframalpha
+import pandas as pd
+from bs4 import BeautifulSoup
+import httpx
+from langgraph.graph import StateGraph, END
+
+# 1. Wikipedia Search Tool
+def wikipedia_search(query):
+    try:
+        return wikipedia.summary(query, sentences=2)
+    except Exception as e:
+        return f"Wikipedia error: {e}"
+
+# 2. Math Evaluation Tool
+def math_eval(expr):
+    try:
+        return str(sympy.sympify(expr).evalf())
+    except Exception as e:
+        return f"Math error: {e}"
+
+# 3. DuckDuckGo Web Search Tool
+def ddg_search(query):
+    try:
+        results = list(DDGS().text(query, max_results=1))
+        return results[0]['body'] if results else 'No results.'
+    except Exception as e:
+        return f"DDG error: {e}"
+
+# 4. Current Date Tool
+def get_current_date():
+    return datetime.now().strftime('%Y-%m-%d')
+
+# 5. Current Time Tool
+def get_current_time():
+    return datetime.now().strftime('%H:%M:%S')
+
+# 6. Timezone Conversion Tool
+def convert_timezone(dt_str, from_tz, to_tz):
+    try:
+        dt = date_parser.parse(dt_str)
+        from_zone = pytz.timezone(from_tz)
+        to_zone = pytz.timezone(to_tz)
+        dt = from_zone.localize(dt)
+        return dt.astimezone(to_zone).strftime('%Y-%m-%d %H:%M:%S')
+    except Exception as e:
+        return f"Timezone error: {e}"
+
+# 7. OpenAI GPT-3.5 Completion Tool (requires OPENAI_API_KEY env var)
+def openai_completion(prompt):
+    try:
+        openai.api_key = os.getenv('OPENAI_API_KEY')
+        resp = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}])
+        return resp.choices[0].message.content.strip()
+    except Exception as e:
+        return f"OpenAI error: {e}"
+
+# 8. WolframAlpha Short Answer Tool (requires WOLFRAMALPHA_APPID env var)
+def wolfram_query(query):
+    try:
+        appid = os.getenv('WOLFRAMALPHA_APPID')
+        client = wolframalpha.Client(appid)
+        res = client.query(query)
+        return next(res.results).text
+    except Exception as e:
+        return f"WolframAlpha error: {e}"
+
+# 9. Weather API Tool (using python-weather-api)
+def get_weather(city):
+    try:
+        import python_weather
+        import asyncio
+        async def getweather():
+            async with python_weather.Client(unit=python_weather.METRIC) as client:
+                weather = await client.get(city)
+                return f"{weather.current.temperature}°C, {weather.current.sky_text}"
+        return asyncio.run(getweather())
+    except Exception as e:
+        return f"Weather error: {e}"
+
+# 10. Pandas DataFrame Tool
+def describe_dataframe(csv_url):
+    try:
+        df = pd.read_csv(csv_url)
+        return str(df.describe())
+    except Exception as e:
+        return f"Pandas error: {e}"
+
+# 11. BeautifulSoup HTML Title Extractor
+def extract_title(url):
+    try:
+        resp = requests.get(url, timeout=10)
+        soup = BeautifulSoup(resp.text, 'lxml')
+        return soup.title.string.strip() if soup.title else 'No title found.'
+    except Exception as e:
+        return f"Soup error: {e}"
+
+# 12. HTTPX GET Tool
+def httpx_get(url):
+    try:
+        resp = httpx.get(url, timeout=10)
+        return resp.text[:500]
+    except Exception as e:
+        return f"HTTPX error: {e}"
+
+# 13. Currency Conversion Tool (using exchangerate.host)
+def convert_currency(amount, from_cur, to_cur):
+    try:
+        url = f"https://api.exchangerate.host/convert?from={from_cur}&to={to_cur}&amount={amount}"
+        resp = requests.get(url)
+        data = resp.json()
+        return f"{amount} {from_cur} = {data['result']} {to_cur}"
+    except Exception as e:
+        return f"Currency error: {e}"
+
+# 14. IP Geolocation Tool
+def ip_geolocate(ip):
+    try:
+        resp = requests.get(f"https://ipinfo.io/{ip}/json")
+        data = resp.json()
+        return f"{data.get('city', '?')}, {data.get('region', '?')}, {data.get('country', '?')}"
+    except Exception as e:
+        return f"IP error: {e}"
+
+# --- LangGraph Agent Setup ---
+from langgraph.agent import Tool, Agent
+
+tools = [
+    Tool("wikipedia_search", wikipedia_search, description="Search Wikipedia for a summary."),
+    Tool("math_eval", math_eval, description="Evaluate a math expression."),
+    Tool("ddg_search", ddg_search, description="DuckDuckGo web search."),
+    Tool("get_current_date", get_current_date, description="Get the current date."),
+    Tool("get_current_time", get_current_time, description="Get the current time."),
+    Tool("convert_timezone", convert_timezone, description="Convert time between timezones."),
+    Tool("openai_completion", openai_completion, description="Get a completion from OpenAI GPT-3.5."),
+    Tool("wolfram_query", wolfram_query, description="Query WolframAlpha for a short answer."),
+    Tool("get_weather", get_weather, description="Get current weather for a city."),
+    Tool("describe_dataframe", describe_dataframe, description="Describe a CSV file using pandas."),
+    Tool("extract_title", extract_title, description="Extract the title from a webpage."),
+    Tool("httpx_get", httpx_get, description="Fetch a webpage using HTTPX."),
+    Tool("convert_currency", convert_currency, description="Convert currency using exchangerate.host."),
+    Tool("ip_geolocate", ip_geolocate, description="Get geolocation info for an IP address."),
+]
+
+class LangGraphAgent:
+    def __init__(self):
+        self.agent = Agent(tools=tools)
+    def __call__(self, question: str) -> str:
+        """Use LangGraph agent to answer the question."""
+        try:
+            return self.agent.run(question)
+        except Exception as e:
+            return f"LangGraphAgent error: {e}"
+
+# Define a simple state for demonstration (can be extended for more complex workflows)
+class AgentState(dict):
+    pass
+
+def build_langgraph():
+    # Create a graph
+    graph = StateGraph(AgentState)
+
+    # Add each tool as a node
+    for tool in tools:
+        def make_tool_node(tool_func):
+            def node(state: AgentState):
+                question = state.get('question', '')
+                try:
+                    result = tool_func(question)
+                except Exception as e:
+                    result = f"Tool error: {e}"
+                state['result'] = result
+                return state
+            return node
+        graph.add_node(tool.name, make_tool_node(tool.func))
+
+    # For demonstration, connect all nodes to END (in practice, you may want to chain or branch)
+    for tool in tools:
+        graph.add_edge(tool.name, END)
+
+    # Set entry point (first tool)
+    graph.set_entry_point(tools[0].name)
+
+    return graph
+
+# --- GAIA-style Multi-step Agent ---
+import re
+
+class GAIAAgent:
+    """
+    A simple GAIA-style agent that can plan and chain multiple tools for multi-step reasoning.
+    This is a minimal demonstration and can be extended for more advanced planning.
+    """
+    def __init__(self):
+        self.tool_map = {t.name: t.func for t in tools}
+
+    def plan(self, question: str):
+        """
+        Very simple planner: looks for keywords to select tools and chain them.
+        Returns a list of (tool_name, tool_input) tuples.
+        """
+        steps = []
+        q = question.lower()
+        # Example: If question asks for weather in a city and then convert time
+        if 'weather' in q and 'time' in q and 'convert' in q:
+            # e.g., "What is the weather in Paris and convert the time to Tokyo timezone?"
+            city = re.findall(r'weather in ([a-zA-Z ]+)', q)
+            city = city[0].strip() if city else 'London'
+            steps.append(('get_weather', city))
+            # Assume user wants to convert current time from city to Tokyo
+            steps.append(('convert_timezone', [datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'Europe/Paris', 'Asia/Tokyo']))
+        elif 'weather' in q:
+            city = re.findall(r'weather in ([a-zA-Z ]+)', q)
+            city = city[0].strip() if city else 'London'
+            steps.append(('get_weather', city))
+        elif 'currency' in q or 'convert' in q and 'usd' in q and 'eur' in q:
+            amount = re.findall(r'(\d+)', q)
+            amount = amount[0] if amount else '1'
+            steps.append(('convert_currency', [amount, 'USD', 'EUR']))
+        elif 'wikipedia' in q or 'who is' in q or 'what is' in q:
+            topic = re.findall(r'(?:wikipedia|who is|what is) ([a-zA-Z0-9 ]+)', q)
+            topic = topic[0].strip() if topic else question
+            steps.append(('wikipedia_search', topic))
+        elif 'math' in q or any(op in q for op in ['+', '-', '*', '/']):
+            expr = re.findall(r'([\d\s\+\-\*/\.]+)', q)
+            expr = expr[0] if expr else question
+            steps.append(('math_eval', expr))
+        else:
+            # Default: try DuckDuckGo search
+            steps.append(('ddg_search', question))
+        return steps
+
+    def __call__(self, question: str) -> str:
+        """
+        Execute the planned steps, chaining outputs if needed.
+        """
+        steps = self.plan(question)
+        last_output = None
+        for tool_name, tool_input in steps:
+            func = self.tool_map.get(tool_name)
+            if not func:
+                last_output = f"Tool {tool_name} not found."
+                break
+            # If previous output is needed as input, use it
+            if isinstance(tool_input, list):
+                # Replace any placeholder with last_output
+                tool_input = [last_output if x == '__PREV__' else x for x in tool_input]
+                try:
+                    last_output = func(*tool_input)
+                except Exception as e:
+                    last_output = f"Error in {tool_name}: {e}"
+            else:
+                try:
+                    last_output = func(tool_input)
+                except Exception as e:
+                    last_output = f"Error in {tool_name}: {e}"
+        return last_output
+
+# Example usage:
+# graph = build_langgraph()
+# result = graph.run({'question': 'What is the capital of France?'})
+# print(result['result'])

requirements.txt

@@ -0,0 +1,16 @@
+gradio[oauth]
+requests
+langgraph
+langchain
+wikipedia
+python-dateutil
+duckduckgo-search
+openai
+wolframalpha
+pytz
+python-weather-api
+sympy
+pandas
+beautifulsoup4
+lxml
+httpx