app5.py

import math
from groq import Groq
#from typing import Optional, Tuple
import smolagents
#from smolagents import tool
#import smolagents[litellm]
import os
import re
import requests
import gradio as gr
from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.agents import Tool, AgentExecutor, initialize_agent
from langchain.agents import AgentType
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import SystemMessage
from langchain.memory import ConversationBufferWindowMemory
from youtube_transcript_api import YouTubeTranscriptApi
import pytesseract
import cv2
import pandas as pd
from langchain.tools import tool
from huggingface_hub import InferenceClient # Explicitly import InferenceClient

import json
import inspect
from typing import List, Dict, Optional, Callable, Any
import cv2
import pytesseract
import pandas as pd
from langchain_community.utilities import WikipediaAPIWrapper, DuckDuckGoSearchAPIWrapper
from youtube_transcript_api import YouTubeTranscriptApi
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma


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"
client = Groq(api_key=os.getenv("GROQ_API_KEY"))
'''
# --- 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
'''



'''
class CodeAgent:
    def __init__(
        self,
        client,
        model: str = "llama-3.3-70b-versatile",
        system_prompt: str = "",
        tools: Optional[List[Dict]] = None,
        tool_functions: Optional[Dict[str, Callable]] = None
    ):
        self.client = client
        self.model = model
        self.conversation_history = [
            {
                "role": "system", 
                "content": f"{system_prompt}\n\nIMPORTANT: Always respond with complete, natural "
                          "language answers. Never show raw function calls to the user."
            }
        ]
        self.tools = tools or []
        self.tool_functions = tool_functions or {}

    def add_tool(self, tool_definition: Dict, tool_function: Callable) -> None:
        self.tools.append(tool_definition)
        self.tool_functions[tool_definition["function"]["name"]] = tool_function

    def add_langchain_tools(self, tools: List[Callable]) -> None:
        for tool_func in tools:
            tool_def = self._convert_tool_to_definition(tool_func)
            self.add_tool(tool_def, tool_func)

    def _convert_tool_to_definition(self, tool_func: Callable) -> Dict:
        docstring = inspect.getdoc(tool_func) or ""
        description = docstring.split("\n")[0] if docstring else tool_func.__name__
        
        sig = inspect.signature(tool_func)
        parameters = {"type": "object", "properties": {}, "required": []}
        
        for name, param in sig.parameters.items():
            if name == "self":
                continue
                
            param_info = {"type": "string", "description": ""}
            for line in docstring.split("\n")[1:]:
                line = line.strip()
                if line.startswith(f"{name}:"):
                    param_info["description"] = line[len(f"{name}:"):].strip()
                    break
            
            parameters["properties"][name] = param_info
            parameters["required"].append(name)
        
        return {
            "type": "function",
            "function": {
                "name": tool_func.__name__,
                "description": description,
                "parameters": parameters
            }
        }

    def get_response(self, user_message: str) -> str:
        self.add_message("user", user_message)

        while True:
            response = self.client.chat.completions.create(
                messages=self.conversation_history,
                model=self.model,
                tools=self.tools if self.tools else None
            )
            
            message = response.choices[0].message

            if not hasattr(message, 'tool_calls') or not message.tool_calls:
                self.add_message("assistant", message.content)
                return message.content

            for tool_call in message.tool_calls:
                self._process_tool_call(tool_call)

            self._add_tool_call_message(message)

    def _process_tool_call(self, tool_call: Any) -> None:
        tool_name = tool_call.function.name
        try:
            arguments = json.loads(tool_call.function.arguments)
            tool_result = self._execute_tool(tool_name, arguments)

            # ✅ Correct format for OpenAI/Groq API
            self.conversation_history.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": str(tool_result)
            })

        except Exception as e:
            self.conversation_history.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": f"Error: {str(e)}"
            })

    def _add_tool_call_message(self, message: Any) -> None:
        self.conversation_history.append({
            "role": "assistant",
            "content": None,
            "tool_calls": [{
                "id": tc.id,
                "function": {
                    "name": tc.function.name,
                    "arguments": tc.function.arguments
                },
                "type": "function"
            } for tc in message.tool_calls]
        })

    def _execute_tool(self, tool_name: str, arguments: Dict) -> Any:
        if tool_name not in self.tool_functions:
            raise ValueError(f"Tool not found: {tool_name}")
        return self.tool_functions[tool_name](**arguments)

    def add_message(self, role: str, content: str) -> None:
        self.conversation_history.append({"role": role, "content": content})

    def get_conversation_history(self) -> List[Dict]:
        return self.conversation_history

    def clear_history(self, keep_system_prompt: bool = True) -> None:
        if keep_system_prompt and self.conversation_history:
            system_msg = self.conversation_history[0] if self.conversation_history[0]["role"] == "system" else None
            self.conversation_history = [system_msg] if system_msg else []
        else:
            self.conversation_history = []

'''

#import json
#from groq import Groq
#from langchain.agents.tools import Tool

class MyAgent:
    def __init__(self):
        self.client = Groq()
        self.model =  "llama3-70b-8192"  #  "llama-3.3-70b-versatile" Keeping your original model
        self.conversation_history = []
        self.tools = self._define_tools()
        self._add_system_prompt()

    def _add_system_prompt(self):
        self.conversation_history.append({
            "role": "system",
            "content": (
                "You are a helpful assistant tasked with answering questions using a set of tools when needed. "
                "Always reason step-by-step internally, but only return the final answer to the user without any explanation. "
                "If a tool is needed, respond in JSON format like:\n"
                "{\n"
                "  \"tool\": \"tool_name\",\n"
                "  \"args\": {\"arg1\": \"value1\", ...}\n"
                "}\n"
                "Once the tool returns a result, respond with the final answer only, following these rules:\n"
                "- Use a number, a short string, or a comma-separated list of numbers/strings.\n"
                "- Do not include commas in numbers (e.g., use 1000 not 1,000).\n"
                "- Do not include units like \"$\" or \"%\" unless explicitly asked.\n"
                "- For strings, avoid articles (e.g., no 'the', 'a') and avoid abbreviations (e.g., use 'New York City' not 'NYC').\n"
                "- Write digits plainly (e.g., '2025' not 'two thousand twenty-five').\n"
                "Do not prepend any labels to the final answer."
            )
        })

    def _define_tools(self):
        def web_search(query: str) -> str:
            # Replace with actual implementation
            return f"[WEB RESULT for: {query}]"

        def calculator(expression: str) -> str:
            try:
                return str(eval(expression))
            except Exception as e:
                return f"Error: {e}"

        return [
            Tool.from_function(
                name="web_search",
                func=web_search,
                description="Search the web for current information."
            ),
            Tool.from_function(
                name="calculator",
                func=calculator,
                description="Evaluate a math expression."
            ),
        ]

    def _execute_tool(self, tool_name, args):
        for tool in self.tools:
            if tool.name == tool_name:
                return tool.func(**args)
        return f"Tool '{tool_name}' not found."

    def add_message(self, role, content):
        self.conversation_history.append({"role": role, "content": content})

    def get_response(self, user_message: str) -> str:
        self.add_message("user", user_message)

        response = self.client.chat.completions.create(
            model=self.model,
            messages=self.conversation_history
        )

        message = response.choices[0].message
        assistant_reply = message.content.strip()
        self.add_message("assistant", assistant_reply)

        # Try to detect and parse JSON tool call
        if assistant_reply.startswith("{") and "tool" in assistant_reply:
            try:
                tool_call = json.loads(assistant_reply)
                tool_name = tool_call.get("tool")
                args = tool_call.get("args", {})
                result = self._execute_tool(tool_name, args)
                self.add_message("tool", result)
                return result
            except Exception as e:
                error_msg = f"Error executing tool: {e}"
                self.add_message("tool", error_msg)
                return error_msg

        return assistant_reply
'''
# ===== Tool Implementations =====
def wikipedia_search(query: str) -> str:
    """Search Wikipedia and return summary."""
    try:
        return WikipediaAPIWrapper().run(query)
    except Exception as e:
        return f"Wikipedia error: {str(e)}"

def web_search(query: str) -> str:
    """Search the web using DuckDuckGo."""
    try:
        return DuckDuckGoSearchAPIWrapper().run(query)
    except Exception as e:
        return f"Search error: {str(e)}"

def youtube_transcript(url: str) -> str:
    """Extract transcript from a YouTube video URL."""
    try:
        video_id = url.split("v=")[-1].split("&")[0]
        transcript = YouTubeTranscriptApi.get_transcript(video_id)
        return "\n".join([x["text"] for x in transcript])
    except Exception as e:
        return f"YouTube error: {str(e)}"

def image_ocr(path: str) -> str:
    """Extract text from an image file."""
    try:
        img = cv2.imread(path)
        if img is None:
            return "Error: Could not read image file"
        return pytesseract.image_to_string(img)
    except Exception as e:
        return f"OCR error: {str(e)}"

def read_excel(path: str) -> str:
    """Read contents of an Excel (.xlsx) file."""
    try:
        df = pd.read_excel(path)
        return df.head(100).to_string()  # Limit output size
    except Exception as e:
        return f"Excel error: {str(e)}"

def math_calc(expression: str) -> str:
    """Evaluate a math expression safely."""
    try:
        allowed_chars = set('0123456789+-*/.() ')
        if not all(c in allowed_chars for c in expression):
            return "Error: Invalid characters in expression"
        return str(eval(expression, {"__builtins__": None}, {}))
    except Exception as e:
        return f"Math error: {str(e)}"
'''
'''
# === Example Usage ===
if __name__ == "__main__":
    from groq import Groq
    import os

    # Initialize client
    #client = Groq(api_key=userdata.get('GROQ_API_KEY'))
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    # Create agent
    agent = CodeAgent(client=client,model="llama-3.3-70b-versatile",system_prompt="You are a helpful AI assistant with access to tools.")

    # Add tools
    tools = [wikipedia_search,web_search,youtube_transcript,math_calc]
    agent.add_langchain_tools(tools)

    # Example queries
    print(agent.get_response("What is machine learning?"))
    print(agent.get_response("Calculate 2 + 2 * 3"))
    #print(agent.get_response("Search for latest AI news"))
'''

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:
        
        # Create agent
        #agent = CodeAgent(client=client,model="llama-3.3-70b-versatile",system_prompt="You are a helpful AI assistant with access to tools.")
        agent = MyAgent()
        # Add tools
        #tools = [wikipedia_search,web_search,youtube_transcript,math_calc]
        #agent.add_langchain_tools(tools)
        #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.get_response(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)
'''prompt = """
You are a helpful assistant tasked with answering questions using a set of tools through tool calls in JSON format.
For each question, think step-by-step. If a tool is required, call it using a properly structured tool_call in JSON format including 'tool_call_id'.

After using tools, return the final answer in the following format:
- A single number, or
- A single word or phrase, or
- A comma-separated list of numbers and/or strings.

Formatting guidelines for answers:
- Do not include units such as $, %, kg, etc., unless explicitly asked.
- Do not use commas in numbers (write 1000000, not 1,000,000).
- Do not use abbreviations (e.g., use 'los angeles' instead of 'la').
- Do not use articles (e.g., use 'banana' instead of 'a banana').
- Numbers must be in digits.
- Strings must be lowercase and space-separated if needed.

Your output must directly start with the answer — do not prepend anything like 'Answer:', 'The answer is:', or similar.

Example tool call structure (when using a tool):
{
  "tool_call_id": "example-id",
  "function": {
    "name": "web_search",
    "arguments": {
      "query": "current population of india"
    }
  }
}
"""'''