Delete agent.py

agent.py

@@ -1,137 +0,0 @@
-# agent.py
-
-import os
-import requests
-from smolagents import LiteLLMModel, CodeAgent, tool, DuckDuckGoSearchTool, SpeechToTextTool, VisitWebpageTool
-import speech_recognition as sr
-from pydub import AudioSegment
-from PIL import Image
-
-# Ustaw endpoint API (dostosuj jeśli inny)
-api_url = "https://agents-course-unit4-scoring.hf.space"
-
-# ==== Narzędzia własne do podpięcia ====
-
-@tool
-def download_question_file(task_id: str, file_name: str = "", save_dir: str = ".") -> str:
-    """
-    Downloads the file associated with a given task ID and saves it to disk.
-    Args:
-        task_id (str): Unique question/task identifier.
-        file_name (str): Optional file name.
-        save_dir (str): Directory to save.
-    Returns:
-        str: Path to the saved file, or error.
-    """
-    url = f"{api_url}/files/{task_id}"
-    try:
-        resp = requests.get(url, timeout=15)
-        resp.raise_for_status()
-    except requests.exceptions.HTTPError as e:
-        return f"HTTP error: {e.response.status_code}"
-    except Exception as e:
-        return f"Network error: {e}"
-    content_disposition = resp.headers.get("Content-Disposition", "")
-    filename = (
-        content_disposition.split('filename="')[-1].rstrip('"')
-        if "filename=" in content_disposition
-        else file_name if file_name else f"{task_id}.dat"
-    )
-    os.makedirs(save_dir, exist_ok=True)
-    file_path = os.path.join(save_dir, filename)
-    with open(file_path, "wb") as f:
-        f.write(resp.content)
-    return file_path
-
-@tool
-def read_image(image_path: str) -> Image:
-    """
-    Loads image from disk.
-    Args:
-        image_path (str): Path to the image file.
-    Returns:
-        The image.
-    """
-    return Image.open(image_path)
-
-@tool
-def audio_to_text(audio_path: str) -> str:
-    """
-    Converts audio (mp3/wav) to text using Google Speech Recognition.
-    Args:
-        audio_path (str): Path to the audio file.
-    Returns:
-        str: Recognized text.
-    """
-    if audio_path.endswith(".mp3"):
-        source_file = audio_path.replace(".mp3", ".wav")
-        sound = AudioSegment.from_mp3(audio_path)
-        sound.export(source_file, format="wav")
-    else:
-        source_file = audio_path
-    r = sr.Recognizer()
-    audio_file = sr.AudioFile(source_file)
-    with audio_file as source:
-        audio = r.record(source)
-        text = r.recognize_google(audio)
-    return text
-
-@tool
-def extract_text_from_image(image_path: str) -> str:
-    """
-    Extract text from image using pytesseract (OCR).
-    Args:
-        image_path: Path to the image file.
-    Returns:
-        Extracted text or error message.
-    """
-    try:
-        import pytesseract
-        from PIL import Image
-        image = Image.open(image_path)
-        text = pytesseract.image_to_string(image)
-        return text
-    except ImportError:
-        return "Error: pytesseract is not installed."
-    except Exception as e:
-        return f"Error extracting text from image: {str(e)}"
-
-# ==== AGENT ====
-
-class GaiaAgent:
-    def __init__(self, model=None, max_steps=8):
-        # Jeśli model nie został przekazany, inicjalizuj domyślnie na OpenAI GPT-4o (lub inny)
-        if model is None:
-            api_key = os.getenv("OPENAI_API_KEY", "")
-            model = LiteLLMModel(
-                model_id="gpt-4o",  # Zmień na swój model jeśli potrzeba
-                api_key=api_key,
-            )
-        self.gaia_agent = CodeAgent(
-            model=model,
-            tools=[
-                DuckDuckGoSearchTool(),
-                download_question_file,
-                read_image,
-                audio_to_text,
-                extract_text_from_image,
-                VisitWebpageTool(),
-                SpeechToTextTool()
-            ],
-            additional_authorized_imports=["pandas", "numpy", "math", "statistics", "scipy"],
-            max_steps=max_steps
-        )
-        # Możesz dodać tu dodatkową konfigurację promptów jeśli chcesz.
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        if self.gaia_agent:
-            try:
-                answer = self.gaia_agent.run(question)
-                print(f"Agent generated answer: {answer[:50]}..." if len(answer) > 50 else f"Agent generated answer: {answer}")
-                return answer
-            except Exception as e:
-                print(f"Error processing question: {e}")
-                return "An error occurred while processing your question. Please check the agent logs for details."
-        else:
-            return "The agent is not properly initialized. Please check your API keys and configuration."