Update app.py

app.py

@@ -1,64 +1,263 @@
+import requests
 import os
 import gradio as gr
-import requests
 import inspect
 import pandas as pd
-from agent import GaiaAgent
 import time
-from smolagents import LiteLLMModel
-import json
+import re
 
-model = LiteLLMModel(model_id="gemini/gemini-2.0-flash", api_key=os.getenv("GEMINI_API_KEY"))
-MODEL_RPM_LIMIT = 15
+# === TOOLS SECTION (scalone z helper.py) ===
 
-RERUN = False
+from langchain_experimental.utilities import PythonREPL
+from langchain.tools import Tool
 
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+# 1. Python REPL Tool
+python_repl = PythonREPL()
+repl_tool = Tool(
+    name="python_repl",
+    description="""
+    A Python REPL (Read-Eval-Print Loop) for executing Python code.
+    Use this tool for:
+    - Performing accurate calculations (arithmetic, complex math).
+    - Manipulating and analyzing data (e.g., lists, numbers).
+    - Executing small, self-contained Python scripts.
+    Input MUST be valid Python code, and all outputs must be printed.
+    """,
+    func=python_repl.run,
+)
 
-# --- 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
+# 2. File Saver Tool
+def download_and_save_file(args: dict) -> str:
+    """
+    Downloads a file from a given URL and saves it to a specified local filename.
+    Input: JSON string with 'url' and 'local_filename' keys.
+    Example: {"url": "https://example.com/data.xlsx", "local_filename": "data.xlsx"}
+    """
+    try:
+        if isinstance(args, str):
+            import json
+            args = json.loads(args)
+        url = args.get("url")
+        local_filename = args.get("local_filename")
+        if not url or not local_filename:
+            return "Error: Both 'url' and 'local_filename' must be provided."
+        response = requests.get(url, stream=True, timeout=30)
+        response.raise_for_status()
+        os.makedirs(os.path.dirname(local_filename) or '.', exist_ok=True)
+        with open(local_filename, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        return f"File downloaded successfully to {local_filename}"
+    except Exception as e:
+        return f"An unexpected error occurred: {e}"
+
+file_saver_tool = Tool(
+    name="file_saver",
+    description="Downloads a file from a URL and saves it to a specified local filename. Input: JSON with 'url' and 'local_filename'.",
+    func=download_and_save_file,
+)
 
-def load_json_list(filename):
+# 3. Audio Transcriber Tool
+import speech_recognition as sr
+from pydub import AudioSegment
+
+def transcribe_audio_from_path(local_audio_path: str, language: str = "en-US") -> str:
     """
-    Loads a list of JSON objects from a file.
-    Args:
-        filename (str): The name of the file to load from.
-    Returns:
-        list: The loaded list of dictionaries, or an empty list if an error occurs.
+    Transcribes audio content from a local file path to text.
+    Only local file paths. Converts to WAV if needed.
     """
+    r = sr.Recognizer()
+    temp_wav_path = "temp_audio_to_transcribe.wav"
+    transcribed_text = ""
     try:
-        with open(filename, 'r', encoding='utf-8') as f:
-            data = json.load(f)
-        print(f"Data successfully loaded from '{filename}'")
-        return data
-    except FileNotFoundError:
-        print(f"Error: File '{filename}' not found.")
-        return []
-    except json.JSONDecodeError as e:
-        print(f"Error decoding JSON from file '{filename}': {e}")
-        return []
-    except IOError as e:
-        print(f"Error reading data from file: {e}")
-        return []
+        if local_audio_path.startswith("http://") or local_audio_path.startswith("https://"):
+            return "Error: This tool only accepts local file paths, not URLs. Please use 'file_saver' first."
+        if not os.path.exists(local_audio_path):
+            return f"Error: Local audio file not found at '{local_audio_path}'."
+        audio = AudioSegment.from_file(local_audio_path)
+        audio.export(temp_wav_path, format="wav")
+        with sr.AudioFile(temp_wav_path) as source:
+            audio_listened = r.record(source)
+            try:
+                transcribed_text = r.recognize_google(audio_listened, language=language)
+            except sr.UnknownValueError:
+                return "Could not understand audio (speech not clear or too short)."
+            except sr.RequestError as e:
+                return f"Could not request results from Google Speech Recognition service; {e}"
+    except Exception as e:
+        return f"An unexpected error occurred during audio processing or transcription: {e}"
+    finally:
+        if os.path.exists(temp_wav_path):
+            os.remove(temp_wav_path)
+    return transcribed_text.strip()
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+audio_transcriber_tool = Tool(
+    name="audio_transcriber_tool",
+    description=(
+        "Transcribes audio content from a **local file path** to a text transcript. "
+        "Use for extracting spoken information from audio recordings downloaded using 'file_saver'."
+    ),
+    func=transcribe_audio_from_path,
+)
+
+# 4. Gemini Multimodal Tool (for images)
+import base64
+from langchain.tools import Tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import HumanMessage
+
+def analyze_image_with_gemini(args: dict) -> str:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Analyzes an image using Gemini Multimodal LLM to answer a given question.
+    Input: JSON with 'image_path' and 'question'.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    try:
+        if isinstance(args, str):
+            import json
+            args = json.loads(args)
+        image_path = args.get("image_path")
+        question = args.get("question")
+        if not image_path or not question:
+            return "Error: Both 'image_path' and 'question' must be provided."
+        if not os.path.exists(image_path):
+            return f"Error: Local image file not found at '{image_path}'."
+        google_api_key = os.getenv("GOOGLE_API_KEY")
+        if not google_api_key:
+            return "Error: GOOGLE_API_KEY not found in environment variables for multimodal tool."
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            google_api_key=google_api_key,
+            temperature=0.0
+        )
+        with open(image_path, "rb") as f:
+            image_bytes = f.read()
+            image_base64 = base64.b64encode(image_bytes).decode('utf-8')
+        message = HumanMessage(
+            content=[
+                {"type": "text", "text": question},
+                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
+            ]
+        )
+        response = llm.invoke([message])
+        return response.content
+    except Exception as e:
+        return f"Error in gemini_multimodal_tool: {e}"
+
+gemini_multimodal_tool = Tool(
+    name="gemini_multimodal_tool",
+    description="Analyze an image with Gemini LLM. Input: JSON with 'image_path' and 'question'.",
+    func=analyze_image_with_gemini,
+)
+
+# 5. Wikipedia Search Tool
+from langchain_community.document_loaders import WikipediaLoader
+
+def wiki_search(query: str) -> str:
+    """Search Wikipedia for a query and return up to 2 results."""
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata.get("source", "")}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return formatted_search_docs
+
+wikipedia_search_tool2 = Tool(
+    name="wikipedia_search_tool2",
+    description="Search Wikipedia for a query and return up to 2 results.",
+    func=wiki_search,
+)
+
+# ========== END TOOLS SECTION ==========
+
+# --- AGENT SECTION ---
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.memory import ConversationSummaryMemory
+from langchain.prompts import PromptTemplate
+from langchain.agents import AgentExecutor, create_react_agent
+from typing import List, Optional
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# Set up LLM (Google Gemini - requires GOOGLE_API_KEY env variable)
+google_api_key = os.getenv("GOOGLE_API_KEY")
+if not google_api_key:
+    raise RuntimeError("GOOGLE_API_KEY not found in environment. Please set it.")
+
+gemini_model = "gemini-2.0-flash"
+llm_client = ChatGoogleGenerativeAI(
+    model=gemini_model,
+    google_api_key=google_api_key,
+    temperature=0,
+)
+summary_memory = ConversationSummaryMemory(llm=llm_client, memory_key="chat_history")
+
+# Prompt
+prompt = PromptTemplate(
+    input_variables=["input", "agent_scratchpad", "chat_history", "tool_names"],
+    template="""
+You are a smart and helpful AI Agent/Assistant that excels at fact-based reasoning. You are allowed and encouraged to use one or more tools as needed to answer complex questions and perform tasks.
+Your FINAL ANSWER must be one of these formats and ONLY the answer itself (no intro phrases):
+- A number (e.g., '26', '1977', '519')
+- As few words as possible (e.g., 'Paris', 'down', 'LUX')
+- A comma-separated list of numbers and/or strings (e.g., '10,20,30', 'apple,banana,orange')
+---
+Previous conversation history:
+{chat_history}
+New input: {input}
+---
+{agent_scratchpad}
+"""
+)
+
+tools = [repl_tool, file_saver_tool, audio_transcriber_tool, gemini_multimodal_tool, wikipedia_search_tool2]
 
+summary_llm = ChatGoogleGenerativeAI(
+    model=gemini_model,
+    google_api_key=google_api_key,
+    temperature=0,
+    streaming=True
+)
+
+summary_react_agent = create_react_agent(
+    llm=summary_llm,
+    tools=tools,
+    prompt=prompt
+)
+
+class BasicAgent:
+    def __init__(
+        self,
+        agent,
+        tools: List,
+        verbose: bool = False,
+        handle_parsing_errors: bool = True,
+        max_iterations: int = 9,
+        memory: Optional[ConversationSummaryMemory] = None
+    ) -> None:
+        self.agent = agent
+        self.tools = tools
+        self.verbose = verbose
+        self.handle_parsing_errors = handle_parsing_errors
+        self.max_iterations = max_iterations
+        self.memory = memory
+        self.agent_obj = AgentExecutor(
+            agent=self.agent,
+            tools=self.tools,
+            verbose=self.verbose,
+            handle_parsing_errors=self.handle_parsing_errors,
+            max_iterations=self.max_iterations,
+            memory=self.memory
+        )
+    def __call__(self, question: str) -> str:
+        result = self.agent_obj.invoke(
+            {"input": question},
+            config={"configurable": {"session_id": "test-session"}},
+        )
+        return result['output']
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
     if profile:
         username= f"{profile.username}"
         print(f"User logged in: {username}")
@@ -70,18 +269,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     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()
-        agent = GaiaAgent(model, MODEL_RPM_LIMIT )
-    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 = BasicAgent(summary_react_agent, tools, True, True, 30, summary_memory)
     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)
@@ -91,53 +282,34 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
              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
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
     results_log = []
     answers_payload = []
-    if RERUN:
-        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(item.get("file_name") != None):
-                question_text = question_text + f" To answer use the file provided, download it with the tool provided with the task_id {task_id} and the file name {file_name}."
-
-            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}"})
-            time.sleep(60)
-
-        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)
-    else:
-        answers_payload = load_json_list("answers_payload.json")
+    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")
+        full_question_for_agent = question_text
+        if file_name:
+            attachment_url = f"{api_url}/files/{task_id}"
+            full_question_for_agent += f"\n\nAttachment '{file_name}' available at EXACT URL: {attachment_url}"
+            print(f"Running agent on task {task_id}: {full_question_for_agent}",flush=True)
+        try:
+            submitted_answer = agent(full_question_for_agent)
+            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})
+            time.sleep(5)  # for demo, zmień na 60 przy real eval!
+        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}"})
 
-    # 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)
+    if not answers_payload:
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 5. Submit
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -150,38 +322,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             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.")
+        cleaned_final_status = re.sub(r'[^\x20-\x7E\n\r\t]+', '', final_status)
+        cleaned_final_status = cleaned_final_status.strip()
         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
+        return cleaned_final_status, 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(
@@ -193,18 +342,13 @@ with gr.Blocks() as demo:
         ---
         **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.
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution.
         """
     )
-
     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]
@@ -212,24 +356,19 @@ with gr.Blocks() as demo:
 
 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
-
+    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 repo URLs if SPACE_ID is found
+    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)