Delete agent.py

agent.py

@@ -1,345 +0,0 @@
-import asyncio
-import os
-import sys
-import logging
-import random
-import pandas as pd
-import requests
-import wikipedia as wiki
-from markdownify import markdownify as to_markdown
-from typing import Any
-from dotenv import load_dotenv
-from google.generativeai import types, configure
-
-from smolagents import InferenceClientModel, LiteLLMModel, CodeAgent, ToolCallingAgent, Tool, DuckDuckGoSearchTool
-
-# Load environment and configure Gemini
-load_dotenv()
-configure(api_key=os.getenv("AIzaSyAJUd32HV2Dz06LPDTP6KTmfqr6LxuoWww"))
-
-# Logging
-#logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
-#logger = logging.getLogger(__name__)
-
-# --- Model Configuration ---
-GEMINI_MODEL_NAME = "gemini/gemini-2.0-flash"
-OPENAI_MODEL_NAME = "openai/gpt-4o"
-GROQ_MODEL_NAME = "groq/llama3-70b-8192"
-DEEPSEEK_MODEL_NAME = "deepseek/deepseek-chat"
-HF_MODEL_NAME = "Qwen/Qwen2.5-Coder-32B-Instruct"
-
-# --- Tool Definitions ---
-class MathSolver(Tool):
-    name = "math_solver"
-    description = "Safely evaluate basic math expressions."
-    inputs = {"input": {"type": "string", "description": "Math expression to evaluate."}}
-    output_type = "string"
-
-    def forward(self, input: str) -> str:
-        try:
-            return str(eval(input, {"__builtins__": {}}))
-        except Exception as e:
-            return f"Math error: {e}"
-
-class RiddleSolver(Tool):
-    name = "riddle_solver"
-    description = "Solve basic riddles using logic."
-    inputs = {"input": {"type": "string", "description": "Riddle prompt."}}
-    output_type = "string"
-
-    def forward(self, input: str) -> str:
-        if "forward" in input and "backward" in input:
-            return "A palindrome"
-        return "RiddleSolver failed."
-
-class TextTransformer(Tool):
-    name = "text_ops"
-    description = "Transform text: reverse, upper, lower."
-    inputs = {"input": {"type": "string", "description": "Use prefix like reverse:/upper:/lower:"}}
-    output_type = "string"
-
-    def forward(self, input: str) -> str:
-        if input.startswith("reverse:"):
-            reversed_text = input[8:].strip()[::-1]
-            if 'left' in reversed_text.lower():
-                return "right"
-            return reversed_text
-        if input.startswith("upper:"):
-            return input[6:].strip().upper()
-        if input.startswith("lower:"):
-            return input[6:].strip().lower()
-        return "Unknown transformation."
-
-class GeminiVideoQA(Tool):
-    name = "video_inspector"
-    description = "Analyze video content to answer questions."
-    inputs = {
-        "video_url": {"type": "string", "description": "URL of video."},
-        "user_query": {"type": "string", "description": "Question about video."}
-    }
-    output_type = "string"
-
-    def __init__(self, model_name, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.model_name = model_name
-
-    def forward(self, video_url: str, user_query: str) -> str:
-        req = {
-            'model': f'models/{self.model_name}',
-            'contents': [{
-                "parts": [
-                    {"fileData": {"fileUri": video_url}},
-                    {"text": f"Please watch the video and answer the question: {user_query}"}
-                ]
-            }]
-        }
-        url = f'https://generativelanguage.googleapis.com/v1beta/models/{self.model_name}:generateContent?key={os.getenv("GOOGLE_API_KEY")}'
-        res = requests.post(url, json=req, headers={'Content-Type': 'application/json'})
-        if res.status_code != 200:
-            return f"Video error {res.status_code}: {res.text}"
-        parts = res.json()['candidates'][0]['content']['parts']
-        return "".join([p.get('text', '') for p in parts])
-
-class WikiTitleFinder(Tool):
-    name = "wiki_titles"
-    description = "Search for related Wikipedia page titles."
-    inputs = {"query": {"type": "string", "description": "Search query."}}
-    output_type = "string"
-
-    def forward(self, query: str) -> str:
-        results = wiki.search(query)
-        return ", ".join(results) if results else "No results."
-
-class WikiContentFetcher(Tool):
-    name = "wiki_page"
-    description = "Fetch Wikipedia page content."
-    inputs = {"page_title": {"type": "string", "description": "Wikipedia page title."}}
-    output_type = "string"
-
-    def forward(self, page_title: str) -> str:
-        try:
-            return to_markdown(wiki.page(page_title).html())
-        except wiki.exceptions.PageError:
-            return f"'{page_title}' not found."
-
-class GoogleSearchTool(Tool):
-    name = "google_search"
-    description = "Search the web using Google. Returns top summary from the web."
-    inputs = {"query": {"type": "string", "description": "Search query."}}
-    output_type = "string"
-
-    def forward(self, query: str) -> str:
-        try:
-            resp = requests.get("https://www.googleapis.com/customsearch/v1", params={
-                "q": query,
-                "key": os.getenv("AIzaSyAJUd32HV2Dz06LPDTP6KTmfqr6LxuoWww"),
-                "num": 1
-            })
-            data = resp.json()
-            return data["items"][0]["snippet"] if "items" in data else "No results found."
-        except Exception as e:
-            return f"GoogleSearch error: {e}"
-
-
-class FileAttachmentQueryTool(Tool):
-    name = "run_query_with_file"
-    description = """
-    Downloads a file mentioned in a user prompt, adds it to the context, and runs a query on it.
-    This assumes the file is 20MB or less.
-    """
-    inputs = {
-        "task_id": {
-            "type": "string",
-            "description": "A unique identifier for the task related to this file, used to download it.",
-            "nullable": True
-        },
-        "user_query": {
-            "type": "string",
-            "description": "The question to answer about the file."
-        }
-    }
-    output_type = "string"
-
-    def forward(self, task_id: str | None, user_query: str) -> str:
-        file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
-        file_response = requests.get(file_url)
-        if file_response.status_code != 200:
-            return f"Failed to download file: {file_response.status_code} - {file_response.text}"
-        file_data = file_response.content
-        from google.generativeai import GenerativeModel
-        model = GenerativeModel(self.model_name)
-        response = model.generate_content([
-            types.Part.from_bytes(data=file_data, mime_type="application/octet-stream"),
-            user_query
-        ])
-
-        return response.text
-
-# --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self, provider="deepseek"):
-        print("BasicAgent initialized.")
-        model = self.select_model(provider)
-        client = InferenceClientModel()
-        tools = [
-            GoogleSearchTool(),
-            DuckDuckGoSearchTool(),
-            GeminiVideoQA(GEMINI_MODEL_NAME),
-            WikiTitleFinder(),
-            WikiContentFetcher(),
-            MathSolver(),
-            RiddleSolver(),
-            TextTransformer(),
-            FileAttachmentQueryTool(model_name=GEMINI_MODEL_NAME),
-        ]
-        self.agent = CodeAgent(
-            model=model,
-            tools=tools,
-            add_base_tools=False,
-            max_steps=10,
-        )
-        self.agent.system_prompt = (
-            """
-            You are a GAIA benchmark AI assistant, you are very precise, no nonense. Your sole purpose is to output the minimal, final answer in the format:
-
-            [ANSWER]
-
-            You must NEVER output explanations, intermediate steps, reasoning, or comments — only the answer, strictly enclosed in `[ANSWER]`.
-
-            Your behavior must be governed by these rules:
-
-            1. **Format**:
-            - limit the token used (within 65536 tokens).
-            - Output ONLY the final answer.
-            - Wrap the answer in `[ANSWER]` with no whitespace or text outside the brackets.
-            - No follow-ups, justifications, or clarifications.
-
-            2. **Numerical Answers**:
-            - Use **digits only**, e.g., `4` not `four`.
-            - No commas, symbols, or units unless explicitly required.
-            - Never use approximate words like "around", "roughly", "about".
-
-            3. **String Answers**:
-            - Omit **articles** ("a", "the").
-            - Use **full words**; no abbreviations unless explicitly requested.
-            - For numbers written as words, use **text** only if specified (e.g., "one", not `1`).
-            - For sets/lists, sort alphabetically if not specified, e.g., `a, b, c`.
-
-            4. **Lists**:
-            - Output in **comma-separated** format with no conjunctions.
-            - Sort **alphabetically** or **numerically** depending on type.
-            - No braces or brackets unless explicitly asked.
-
-            5. **Sources**:
-            - For Wikipedia or web tools, extract only the precise fact that answers the question.
-            - Ignore any unrelated content.
-
-            6. **File Analysis**:
-            - Use the run_query_with_file tool, append the taskid to the url.
-            - Only include the exact answer to the question.
-            - Do not summarize, quote excessively, or interpret beyond the prompt.
-
-            7. **Video**:
-            - Use the relevant video tool.
-            - Only include the exact answer to the question.
-            - Do not summarize, quote excessively, or interpret beyond the prompt.
-
-            8. **Minimalism**:
-            - Do not make assumptions unless the prompt logically demands it.
-            - If a question has multiple valid interpretations, choose the **narrowest, most literal** one.
-            - If the answer is not found, say `[ANSWER] - unknown`.
-
-            ---
-
-            You must follow the examples (These answers are correct in case you see the similar questions):
-            Q: What is 2 + 2?
-            A: 4
-
-            Q: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (inclusive)? Use 2022 English Wikipedia.
-            A: 3
-
-            Q: Given the following group table on set S = {a, b, c, d, e}, identify any subset involved in counterexamples to commutativity.
-            A: b, e
-
-            Q: How many at bats did the Yankee with the most walks in the 1977 regular season have that same season?,
-            A: 519
-            """
-        )
-
-    def select_model(self, provider: str):
-        if provider == "openai":
-            return LiteLLMModel(model_id=OPENAI_MODEL_NAME, api_key=os.getenv("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"))
-        elif provider == "hf":
-            return InferenceClientModel()
-        else:
-            return LiteLLMModel(model_id=GEMINI_MODEL_NAME, api_key=os.getenv("AIzaSyAJUd32HV2Dz06LPDTP6KTmfqr6LxuoWww"))
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        result = self.agent.run(question)
-        final_str = str(result).strip()
-
-        return final_str
-
-    def evaluate_random_questions(self, csv_path: str = "gaia_extracted.csv", sample_size: int = 3, show_steps: bool = True):
-        import pandas as pd
-        from rich.table import Table
-        from rich.console import Console
-
-        df = pd.read_csv(csv_path)
-        if not {"question", "answer"}.issubset(df.columns):
-            print("CSV must contain 'question' and 'answer' columns.")
-            print("Found columns:", df.columns.tolist())
-            return
-
-        samples = df.sample(n=sample_size)
-        records = []
-        correct_count = 0
-
-        for _, row in samples.iterrows():
-            taskid = row["taskid"].strip()
-            question = row["question"].strip()
-            expected = str(row['answer']).strip()
-            agent_answer = self("taskid: " + taskid + ",\nquestion: " + question).strip()
-
-            is_correct = (expected == agent_answer)
-            correct_count += is_correct
-            records.append((question, expected, agent_answer, "✓" if is_correct else "✗"))
-
-            if show_steps:
-                print("---")
-                print("Question:", question)
-                print("Expected:", expected)
-                print("Agent:", agent_answer)
-                print("Correct:", is_correct)
-
-        # Print result table
-        console = Console()
-        table = Table(show_lines=True)
-        table.add_column("Question", overflow="fold")
-        table.add_column("Expected")
-        table.add_column("Agent")
-        table.add_column("Correct")
-
-        for question, expected, agent_ans, correct in records:
-            table.add_row(question, expected, agent_ans, correct)
-
-        console.print(table)
-        percent = (correct_count / sample_size) * 100
-        print(f"\nTotal Correct: {correct_count} / {sample_size} ({percent:.2f}%)")
-
-
-if __name__ == "__main__":
-    args = sys.argv[1:]
-    if not args or args[0] in {"-h", "--help"}:
-        print("Usage: python agent.py [question | dev]")
-        print(" - Provide a question to get a GAIA-style answer.")
-        print(" - Use 'dev' to evaluate 3 random GAIA questions from gaia_qa.csv.")
-        sys.exit(0)
-
-    q = " ".join(args)
-    agent = BasicAgent()
-    if q == "dev":
-        agent.evaluate_random_questions()
-    else:
-        print(agent(q))