Update app.py

app.py

@@ -1,45 +1,224 @@
+import os
 import gradio as gr
-from langchain.agents import AgentExecutor, Tool
-from langchain.tools import DuckDuckGoSearchRun
-from langchain.llms import HuggingFaceHub
 import requests
+import pandas as pd
+from smolagents import (
+    CodeAgent,
+    OpenAIServerModel,
+    GoogleSearchTool,
+)
+from tools import read_image, transcribe_audio, run_video, search_wikipedia, read_code
 
-# 1. Инициализация инструментов
-search = DuckDuckGoSearchRun()
-tools = [
-    Tool(
-        name="Search",
-        func=search.run,
-        description="Для поиска фактов в интернете"
-    )
-]
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+model_id = "gpt-4o-mini"
 
-# 2. Выбор модели (бесплатной)
-llm = HuggingFaceHub(
-    repo_id="mistralai/Mistral-7B-Instruct-v0.1",
-    huggingfacehub_api_token="ваш_API_токен"  # Замените на токен из https://huggingface.co/settings/tokens
-)
 
-# 3. Создание агента
-agent = AgentExecutor.from_llm_and_tools(llm=llm, tools=tools)
+class BasicAgent:
+    def __init__(self, model_id=model_id):
+        model = OpenAIServerModel(model_id=model_id, temperature=0.1)
+        google_search = GoogleSearchTool()
+        self.agent = CodeAgent(
+            model=model,
+            tools=[
+                read_image,
+                transcribe_audio,
+                read_code,
+                run_video,
+                search_wikipedia,
+                google_search,
+            ],
+            additional_authorized_imports=["numpy", "pandas"],
+            max_steps=20,
+        )
+        add_sys_prompt = f"""\n\nIf a file_url is available or an url is given in question statement, then request and use the content to answer the question. \
+        If a code file, such as .py file, is given, do not attempt to execute it but rather open it as a text file and analyze the content. \
+        When a tabluar file, such as csv, tsv, xlsx, is given, read it using pandas. 
+        
+        Make sure you provide the answer in accordance with the instruction provided in the question. Do not return the result of tool as a final_answer. 
+        Do Not add any additional information, explanation, unnecessary words or symbols. The answer is likely as simple as one word."""
+        self.agent.prompt_templates["system_prompt"] += add_sys_prompt
+
+    def __call__(self, question: str) -> str:
+        answer = self.agent.run(question)
+        return 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.
+    """
+    space_id = os.getenv("SPACE_ID")
+
+    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"
+
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    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
+
+    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")
+        file_name = item.get("file_name")
+        if file_name:
+            file_url = f"{DEFAULT_API_URL}/files/{task_id}"
+        else:
+            file_url = "No URL provided"
+        extension = file_name.split(".")[-1]
+
+        question_text += f"\n\nfile_url : {file_url} \nfile_extension : {extension}"
+        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)
+
+    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)
 
-# 4. Функция для обработки вопросов GAIA
-def answer_question(question):
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = agent.run({"input": question})
-        return response
+        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:
-        return f"Ошибка: {str(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
+
 
-# 5. Интерфейс Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("## GAIA Agent Evaluation")
-    
-    with gr.Row():
-        question = gr.Textbox(label="Вопрос GAIA", lines=3)
-        answer = gr.Textbox(label="Ответ агента", lines=3)
-    
-    submit_btn = gr.Button("Отправить ответ")
-    submit_btn.click(answer_question, inputs=question, outputs=answer)
-
-demo.launch()
+    gr.Markdown("# Evaluation")
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(
+        label="Run Status / Submission Result", lines=5, interactive=False
+    )
+    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)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    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(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)