Create app

app.py

@@ -0,0 +1,336 @@
+import os
+import gradio as gr
+import requests
+import pandas as pd
+import base64
+import ollama
+from typing import TypedDict, Annotated
+from langchain_core.messages import AnyMessage, SystemMessage
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_ollama.chat_models import ChatOllama
+from langchain.tools import Tool
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+
+def get_file_path(task_id: str, question) -> str:
+    """Retrieves reference file path."""
+    if question['task_id'] == task_id:
+        return question['file_path']
+
+
+def get_ref_content(path: str) -> str | object:
+    """Retrieves content from the reference path provided."""
+    with open(path, "rb") as f:
+        file = f.read()
+    return file
+
+
+def search_web(topic: str) -> str:
+    """Retrieves information about the topic."""
+    results = DuckDuckGoSearchRun().invoke(topic)
+    if results:
+        return "\n\n".join([doc.text for doc in results[:2]])
+    else:
+        return "No matching content found."
+
+
+def extract_text_from_image(img_path: str) -> str:
+    """Extracts text from image"""
+    try:
+        # Read image and encode as base64
+        with open(img_path, "rb") as image_file:
+            image_bytes = image_file.read()
+
+        image_base64 = base64.b64encode(image_bytes).decode("utf-8")
+        return image_base64
+    except Exception as e:
+        # A butler should handle errors gracefully
+        error_msg = f"Error extracting text: {str(e)}"
+        print(error_msg)
+        return ""
+
+
+# Initialize the tool
+get_file_path_tool = Tool(
+    name="file_path_retriever",
+    func=get_file_path,
+    description="Retrieves path to the reference file."
+)
+
+get_content_tool = Tool(
+    name="ref_content_retriever",
+    func=get_ref_content,
+    description="Retrieves reference file content."
+)
+
+search_web_tool = Tool(
+    name="search_web_retriever",
+    func=search_web,
+    description="Retrieves online info about a specific topic."
+)
+
+extract_text_tool = Tool(
+    name="extract_text_retriever",
+    func=extract_text_from_image,
+    description="Retrieves text from an image."
+)
+
+# Generate the chat interface, including the tools
+print("Loading LLM...")
+ollama.pull('gemma3:4b')
+chat = ChatOllama(model="gemma3:4b", verbose=True)
+print(f"Model {chat.model} downloaded!")
+
+tools = [get_file_path_tool, get_content_tool, extract_text_tool, search_web_tool]
+chat_with_tools = chat.bind_tools(tools, parallel_tool_calls=False)
+
+
+# Generate the AgentState and Agent graph
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+
+def assistant(state: AgentState):
+    return {
+        "messages": chat.invoke(state["messages"]),
+    }
+
+
+# The graph
+builder = StateGraph(AgentState)
+
+# Define nodes: these do the work
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode([get_file_path_tool, get_content_tool, extract_text_tool, search_web_tool]))
+
+# Define edges: these determine how the control flow moves
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # If the latest message requires a tool, route to tools
+    # Otherwise, provide a direct response
+    tools_condition
+)
+builder.add_edge("tools", "assistant")
+alfred = builder.compile()
+system_prompt = SystemMessage(
+    content="You are a general AI assistant. \
+        I will ask you a question. Report your thoughts shortly, \
+        and finish your answer with the following template: \
+        FINAL ANSWER: [YOUR FINAL ANSWER]. \
+        YOUR FINAL ANSWER should be a number OR as few words as possible \
+        OR a comma separated list of numbers and/or strings. \
+        If you are asked for a number, use only digits in your final answer. \
+        Don't use comma nor brackets to write your number neither use units such as $ or percent sign \
+        unless specified otherwise. \
+        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), \
+        and write the digits in plain text unless specified otherwise. \
+        If you are asked for a comma separated list, apply the above rules \
+        depending of whether the element to be put in the list is a number or a string. \
+        If the question refers to an external content and there is no reference file attached, \
+        perform a web search and retrieve relevant information from the internet. \
+        Make sure that each final answer is preceded with 'FINAL ANSWER:'. "
+)
+
+
+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 ( useful 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 separate 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)