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#!/usr/bin/env python3 | |
""" | |
🚀 GAIA Multi-Agent System - UNIVERSAL MULTIMODAL AI AGENT | |
Enhanced with comprehensive multimodal capabilities for ANY type of question: | |
- 🎥 Video Processing & Analysis | |
- 🎵 Audio Processing & Speech Recognition | |
- 🎨 Image Generation & Advanced Computer Vision | |
- 📊 Data Visualization & Chart Generation | |
- 🎙️ Speech Synthesis & Voice Generation | |
- 🎬 Video Generation & Editing | |
- 🧬 Scientific Computing & Analysis | |
- 📈 Advanced Analytics & Modeling | |
""" | |
import os | |
import sys | |
import re | |
import json | |
import time | |
import random | |
import logging | |
import requests | |
import tempfile | |
import base64 | |
import hashlib | |
import subprocess | |
from typing import Dict, List, Any, Optional, Tuple, Union | |
from dataclasses import dataclass | |
from enum import Enum | |
from urllib.parse import urlparse, urljoin | |
import math | |
import statistics | |
# Core AI and Web Libraries | |
try: | |
from huggingface_hub import InferenceClient | |
HF_AVAILABLE = True | |
except ImportError: | |
HF_AVAILABLE = False | |
print("⚠️ huggingface_hub not available. AI features limited.") | |
try: | |
import openai | |
OPENAI_AVAILABLE = True | |
except ImportError: | |
OPENAI_AVAILABLE = False | |
print("⚠️ OpenAI not available. GPT models unavailable.") | |
# Web Scraping | |
try: | |
from bs4 import BeautifulSoup | |
BS4_AVAILABLE = True | |
except ImportError: | |
BS4_AVAILABLE = False | |
print("⚠️ BeautifulSoup not available. Web scraping limited.") | |
# Image Processing | |
try: | |
from PIL import Image, ImageDraw, ImageFont | |
PIL_AVAILABLE = True | |
except ImportError: | |
PIL_AVAILABLE = False | |
print("⚠️ Pillow not available. Image processing limited.") | |
# Video Processing | |
try: | |
import cv2 | |
CV2_AVAILABLE = True | |
except ImportError: | |
CV2_AVAILABLE = False | |
print("⚠️ OpenCV not available. Video processing unavailable.") | |
# Audio Processing | |
try: | |
import librosa | |
import soundfile as sf | |
AUDIO_AVAILABLE = True | |
except ImportError: | |
AUDIO_AVAILABLE = False | |
print("⚠️ Audio libraries not available. Audio processing unavailable.") | |
# Speech Recognition | |
try: | |
import speech_recognition as sr | |
SPEECH_AVAILABLE = True | |
except ImportError: | |
SPEECH_AVAILABLE = False | |
print("⚠️ Speech recognition not available.") | |
# Text-to-Speech | |
try: | |
import pyttsx3 | |
TTS_AVAILABLE = True | |
except ImportError: | |
TTS_AVAILABLE = False | |
print("⚠️ Text-to-speech not available.") | |
# Data Visualization | |
try: | |
import matplotlib.pyplot as plt | |
import plotly.graph_objects as go | |
import plotly.express as px | |
VIZ_AVAILABLE = True | |
# Optional: seaborn | |
try: | |
import seaborn as sns | |
SEABORN_AVAILABLE = True | |
except ImportError: | |
SEABORN_AVAILABLE = False | |
sns = None | |
except ImportError: | |
VIZ_AVAILABLE = False | |
SEABORN_AVAILABLE = False | |
plt = None | |
go = None | |
px = None | |
sns = None | |
print("⚠️ Visualization libraries not available.") | |
# Scientific Computing | |
try: | |
import numpy as np | |
import pandas as pd | |
import scipy.stats as stats | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.cluster import KMeans | |
SCIENCE_AVAILABLE = True | |
except ImportError: | |
SCIENCE_AVAILABLE = False | |
print("⚠️ Scientific computing libraries not available.") | |
# Configure logging | |
logging.basicConfig(level=logging.INFO) | |
logger = logging.getLogger(__name__) | |
class ToolType(Enum): | |
"""🛠️ Universal tool types for any content type""" | |
# Original tools | |
WEB_SEARCH = "web_search" | |
BROWSE_URL = "browse_url" | |
DOWNLOAD_FILE = "download_file" | |
READ_PDF = "read_pdf" | |
ANALYZE_IMAGE = "analyze_image" | |
CALCULATOR = "calculator" | |
# New multimodal tools | |
PROCESS_VIDEO = "process_video" | |
ANALYZE_AUDIO = "analyze_audio" | |
GENERATE_IMAGE = "generate_image" | |
SYNTHESIZE_SPEECH = "synthesize_speech" | |
CREATE_VISUALIZATION = "create_visualization" | |
ANALYZE_DATA = "analyze_data" | |
GENERATE_VIDEO = "generate_video" | |
EXTRACT_AUDIO = "extract_audio" | |
TRANSCRIBE_SPEECH = "transcribe_speech" | |
DETECT_OBJECTS = "detect_objects" | |
FACE_RECOGNITION = "face_recognition" | |
SCIENTIFIC_COMPUTE = "scientific_compute" | |
class ToolCall: | |
tool: ToolType | |
parameters: Dict[str, Any] | |
class UniversalMultimodalToolkit: | |
"""🌟 Universal toolkit for processing ANY type of content""" | |
def __init__(self, hf_token: str = None, openai_key: str = None): | |
self.hf_token = hf_token | |
self.openai_key = openai_key | |
self.temp_dir = tempfile.mkdtemp() | |
# Initialize specialized clients | |
self._init_multimodal_clients() | |
def _init_multimodal_clients(self): | |
"""Initialize all multimodal AI clients""" | |
self.clients = {} | |
if self.hf_token and HF_AVAILABLE: | |
# Vision models | |
self.clients['vision'] = InferenceClient(model="Salesforce/blip-image-captioning-large", token=self.hf_token) | |
self.clients['image_gen'] = InferenceClient(model="stabilityai/stable-diffusion-xl-base-1.0", token=self.hf_token) | |
self.clients['object_detection'] = InferenceClient(model="facebook/detr-resnet-50", token=self.hf_token) | |
# Audio models - Updated to use provider pattern for speech recognition | |
self.clients['speech_to_text'] = InferenceClient( | |
provider="hf-inference", | |
api_key=self.hf_token, | |
) | |
self.clients['audio_classification'] = InferenceClient(model="facebook/wav2vec2-base-960h", token=self.hf_token) | |
# Text generation for multimodal | |
self.clients['text_gen'] = InferenceClient(model="meta-llama/Meta-Llama-3-8B-Instruct", token=self.hf_token) | |
# === VIDEO PROCESSING === | |
def process_video(self, video_path: str, task: str = "analyze") -> str: | |
"""🎥 Process and analyze video content""" | |
if not CV2_AVAILABLE: | |
return "❌ Video processing unavailable. Install opencv-python." | |
try: | |
logger.info(f"🎥 Processing video: {video_path} | Task: {task}") | |
cap = cv2.VideoCapture(video_path) | |
if not cap.isOpened(): | |
return f"❌ Could not open video: {video_path}" | |
# Get video properties | |
fps = cap.get(cv2.CAP_PROP_FPS) | |
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
duration = frame_count / fps if fps > 0 else 0 | |
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
video_info = f"Video: {width}x{height}, {fps:.1f} FPS, {duration:.1f}s, {frame_count} frames" | |
if task == "extract_frames": | |
# Extract key frames for analysis | |
frames_extracted = [] | |
frame_interval = max(1, frame_count // 10) # Extract 10 frames max | |
for i in range(0, frame_count, frame_interval): | |
cap.set(cv2.CAP_PROP_POS_FRAMES, i) | |
ret, frame = cap.read() | |
if ret: | |
frame_path = os.path.join(self.temp_dir, f"frame_{i}.jpg") | |
cv2.imwrite(frame_path, frame) | |
frames_extracted.append(frame_path) | |
cap.release() | |
# Analyze extracted frames | |
frame_analyses = [] | |
for frame_path in frames_extracted[:3]: # Analyze first 3 frames | |
analysis = self.analyze_image(frame_path, "Describe what you see in this video frame") | |
frame_analyses.append(analysis) | |
return f"{video_info}. Frame analysis: {'; '.join(frame_analyses)}" | |
elif task == "motion_detection": | |
# Simple motion detection | |
ret, frame1 = cap.read() | |
if not ret: | |
cap.release() | |
return f"{video_info}. Motion detection failed." | |
frame1_gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) | |
motion_detected = 0 | |
while True: | |
ret, frame2 = cap.read() | |
if not ret: | |
break | |
frame2_gray = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) | |
diff = cv2.absdiff(frame1_gray, frame2_gray) | |
if cv2.countNonZero(diff) > 5000: # Threshold for motion | |
motion_detected += 1 | |
frame1_gray = frame2_gray | |
cap.release() | |
motion_percentage = (motion_detected / frame_count) * 100 | |
return f"{video_info}. Motion detected in {motion_percentage:.1f}% of frames." | |
else: | |
cap.release() | |
return f"{video_info}. Basic video analysis complete." | |
except Exception as e: | |
logger.error(f"❌ Video processing error: {e}") | |
return f"❌ Video processing failed: {e}" | |
# === AUDIO PROCESSING === | |
def analyze_audio(self, audio_path: str, task: str = "analyze") -> str: | |
"""🎵 Analyze audio content""" | |
if not AUDIO_AVAILABLE: | |
return "❌ Audio processing unavailable. Install librosa and soundfile." | |
try: | |
logger.info(f"🎵 Analyzing audio: {audio_path} | Task: {task}") | |
# Load audio | |
y, sr = librosa.load(audio_path, sr=None) | |
duration = len(y) / sr | |
audio_info = f"Audio: {duration:.1f}s, {sr} Hz, {len(y)} samples" | |
if task == "transcribe": | |
return self.transcribe_speech(audio_path) | |
elif task == "features": | |
# Extract audio features | |
tempo, beats = librosa.beat.beat_track(y=y, sr=sr) | |
spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0] | |
spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)[0] | |
zero_crossing_rate = librosa.feature.zero_crossing_rate(y)[0] | |
features = { | |
"tempo": float(tempo), | |
"avg_spectral_centroid": float(np.mean(spectral_centroids)), | |
"avg_spectral_rolloff": float(np.mean(spectral_rolloff)), | |
"avg_zero_crossing_rate": float(np.mean(zero_crossing_rate)) | |
} | |
return f"{audio_info}. Features: {json.dumps(features, indent=2)}" | |
else: | |
return f"{audio_info}. Basic audio analysis complete." | |
except Exception as e: | |
logger.error(f"❌ Audio analysis error: {e}") | |
return f"❌ Audio analysis failed: {e}" | |
def transcribe_speech(self, audio_path: str) -> str: | |
"""🎙️ Convert speech to text using Whisper via HuggingFace Inference API""" | |
try: | |
logger.info(f"🎙️ Transcribing speech from: {audio_path}") | |
if self.hf_token and HF_AVAILABLE and 'speech_to_text' in self.clients: | |
# Use Whisper via HuggingFace Inference API with provider pattern | |
try: | |
result = self.clients['speech_to_text'].automatic_speech_recognition( | |
audio_path, | |
model="openai/whisper-large-v3" | |
) | |
if isinstance(result, dict) and 'text' in result: | |
transcription = result['text'].strip() | |
elif isinstance(result, str): | |
transcription = result.strip() | |
else: | |
transcription = str(result).strip() | |
if transcription: | |
return f"Transcription: {transcription}" | |
else: | |
return "❌ No transcription available" | |
except Exception as hf_error: | |
logger.warning(f"⚠️ HuggingFace speech recognition failed: {hf_error}") | |
# Fall through to local recognition | |
# Fallback to local speech recognition if available | |
if SPEECH_AVAILABLE: | |
try: | |
r = sr.Recognizer() | |
with sr.AudioFile(audio_path) as source: | |
audio = r.record(source) | |
text = r.recognize_google(audio) | |
return f"Transcription: {text}" | |
except sr.UnknownValueError: | |
return "❌ Could not understand audio" | |
except sr.RequestError as e: | |
return f"❌ Speech recognition error: {e}" | |
else: | |
return "❌ Speech recognition unavailable. Need HuggingFace token or speech_recognition library." | |
except Exception as e: | |
logger.error(f"❌ Transcription error: {e}") | |
return f"❌ Transcription failed: {e}" | |
# === IMAGE GENERATION === | |
def generate_image(self, prompt: str, style: str = "realistic") -> str: | |
"""🎨 Generate images from text descriptions""" | |
try: | |
logger.info(f"🎨 Generating image: {prompt} | Style: {style}") | |
if self.hf_token and 'image_gen' in self.clients: | |
# Use Stable Diffusion via HuggingFace | |
enhanced_prompt = f"{prompt}, {style} style, high quality, detailed" | |
image = self.clients['image_gen'].text_to_image(enhanced_prompt) | |
# Save generated image | |
image_path = os.path.join(self.temp_dir, f"generated_{int(time.time())}.png") | |
image.save(image_path) | |
return f"✅ Image generated and saved to: {image_path}" | |
elif self.openai_key and OPENAI_AVAILABLE: | |
# Use DALL-E via OpenAI | |
client = openai.OpenAI(api_key=self.openai_key) | |
response = client.images.generate( | |
model="dall-e-3", | |
prompt=f"{prompt}, {style} style", | |
size="1024x1024", | |
quality="standard", | |
n=1, | |
) | |
image_url = response.data[0].url | |
# Download and save image | |
img_response = requests.get(image_url) | |
image_path = os.path.join(self.temp_dir, f"dalle_generated_{int(time.time())}.png") | |
with open(image_path, 'wb') as f: | |
f.write(img_response.content) | |
return f"✅ DALL-E image generated and saved to: {image_path}" | |
else: | |
return "❌ Image generation unavailable. Need HuggingFace token or OpenAI key." | |
except Exception as e: | |
logger.error(f"❌ Image generation error: {e}") | |
return f"❌ Image generation failed: {e}" | |
# === SPEECH SYNTHESIS === | |
def synthesize_speech(self, text: str, voice: str = "default") -> str: | |
"""🎙️ Convert text to speech""" | |
try: | |
logger.info(f"🎙️ Synthesizing speech: {text[:50]}... | Voice: {voice}") | |
if TTS_AVAILABLE: | |
engine = pyttsx3.init() | |
# Set voice properties | |
voices = engine.getProperty('voices') | |
if voices and len(voices) > 0: | |
if voice == "female" and len(voices) > 1: | |
engine.setProperty('voice', voices[1].id) | |
else: | |
engine.setProperty('voice', voices[0].id) | |
# Set speech rate and volume | |
engine.setProperty('rate', 150) | |
engine.setProperty('volume', 0.9) | |
# Generate speech file | |
speech_path = os.path.join(self.temp_dir, f"speech_{int(time.time())}.wav") | |
engine.save_to_file(text, speech_path) | |
engine.runAndWait() | |
return f"✅ Speech synthesized and saved to: {speech_path}" | |
else: | |
return "❌ Text-to-speech unavailable. Install pyttsx3." | |
except Exception as e: | |
logger.error(f"❌ Speech synthesis error: {e}") | |
return f"❌ Speech synthesis failed: {e}" | |
# === DATA VISUALIZATION === | |
def create_visualization(self, data: Dict[str, Any], chart_type: str = "bar") -> str: | |
"""📊 Create data visualizations and charts""" | |
try: | |
logger.info(f"📊 Creating {chart_type} chart") | |
if not VIZ_AVAILABLE: | |
return "❌ Visualization unavailable. Install matplotlib, seaborn, and plotly." | |
# Prepare data | |
if isinstance(data, dict) and 'x' in data and 'y' in data: | |
x_data = data['x'] | |
y_data = data['y'] | |
title = data.get('title', 'Data Visualization') | |
else: | |
return "❌ Invalid data format. Need dict with 'x' and 'y' keys." | |
# Create visualization | |
plt.figure(figsize=(10, 6)) | |
if chart_type == "bar": | |
plt.bar(x_data, y_data) | |
elif chart_type == "line": | |
plt.plot(x_data, y_data, marker='o') | |
elif chart_type == "scatter": | |
plt.scatter(x_data, y_data) | |
elif chart_type == "pie": | |
plt.pie(y_data, labels=x_data, autopct='%1.1f%%') | |
else: | |
plt.plot(x_data, y_data) | |
plt.title(title) | |
plt.xlabel(data.get('xlabel', 'X')) | |
plt.ylabel(data.get('ylabel', 'Y')) | |
plt.grid(True, alpha=0.3) | |
# Save chart | |
chart_path = os.path.join(self.temp_dir, f"chart_{int(time.time())}.png") | |
plt.savefig(chart_path, dpi=300, bbox_inches='tight') | |
plt.close() | |
return f"✅ {chart_type.title()} chart created and saved to: {chart_path}" | |
except Exception as e: | |
logger.error(f"❌ Visualization error: {e}") | |
return f"❌ Visualization failed: {e}" | |
# === SCIENTIFIC COMPUTING === | |
def scientific_compute(self, operation: str, data: Dict[str, Any]) -> str: | |
"""🧬 Perform scientific computations and analysis""" | |
try: | |
if not SCIENCE_AVAILABLE: | |
return "❌ Scientific computing unavailable. Install numpy, pandas, scipy, sklearn." | |
logger.info(f"🧬 Scientific computation: {operation}") | |
if operation == "statistics": | |
values = data.get('values', []) | |
if not values: | |
return "❌ No values provided for statistics" | |
result = { | |
"mean": float(np.mean(values)), | |
"median": float(np.median(values)), | |
"std": float(np.std(values)), | |
"min": float(np.min(values)), | |
"max": float(np.max(values)), | |
"variance": float(np.var(values)), | |
"skewness": float(stats.skew(values)), | |
"kurtosis": float(stats.kurtosis(values)) | |
} | |
return f"Statistics: {json.dumps(result, indent=2)}" | |
elif operation == "correlation": | |
x = data.get('x', []) | |
y = data.get('y', []) | |
if not x or not y or len(x) != len(y): | |
return "❌ Need equal length x and y arrays for correlation" | |
correlation = float(np.corrcoef(x, y)[0, 1]) | |
p_value = float(stats.pearsonr(x, y)[1]) | |
return f"Correlation: {correlation:.4f}, P-value: {p_value:.4f}" | |
elif operation == "clustering": | |
data_points = data.get('data', []) | |
n_clusters = data.get('clusters', 3) | |
if not data_points: | |
return "❌ No data points provided for clustering" | |
# Perform K-means clustering | |
scaler = StandardScaler() | |
scaled_data = scaler.fit_transform(data_points) | |
kmeans = KMeans(n_clusters=n_clusters, random_state=42) | |
labels = kmeans.fit_predict(scaled_data) | |
return f"Clustering complete. Labels: {labels.tolist()}" | |
else: | |
return f"❌ Unknown scientific operation: {operation}" | |
except Exception as e: | |
logger.error(f"❌ Scientific computation error: {e}") | |
return f"❌ Scientific computation failed: {e}" | |
# === OBJECT DETECTION === | |
def detect_objects(self, image_path: str) -> str: | |
"""🔍 Detect and identify objects in images""" | |
try: | |
logger.info(f"🔍 Detecting objects in: {image_path}") | |
if self.hf_token and 'object_detection' in self.clients: | |
with open(image_path, 'rb') as img_file: | |
result = self.clients['object_detection'].object_detection(img_file.read()) | |
if result: | |
objects = [] | |
for detection in result: | |
label = detection.get('label', 'unknown') | |
score = detection.get('score', 0) | |
objects.append(f"{label} ({score:.2f})") | |
return f"Objects detected: {', '.join(objects)}" | |
else: | |
return "No objects detected" | |
else: | |
return "❌ Object detection unavailable. Need HuggingFace token." | |
except Exception as e: | |
logger.error(f"❌ Object detection error: {e}") | |
return f"❌ Object detection failed: {e}" | |
# Enhanced existing methods | |
def web_search(self, query: str, num_results: int = 5) -> str: | |
"""🔍 Enhanced web search with comprehensive crawling and browsing""" | |
try: | |
logger.info(f"🔍 Web search: {query}") | |
# Enhanced DuckDuckGo search with better result extraction | |
search_url = f"https://duckduckgo.com/html/?q={requests.utils.quote(query)}" | |
headers = { | |
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' | |
} | |
response = requests.get(search_url, headers=headers, timeout=15) | |
response.raise_for_status() | |
if not BS4_AVAILABLE: | |
return f"⚠️ Search completed but parsing limited. Raw response length: {len(response.text)}" | |
soup = BeautifulSoup(response.text, 'html.parser') | |
results = [] | |
# Enhanced result extraction with multiple patterns | |
result_selectors = [ | |
'div.result', | |
'div[data-result-index]', | |
'article', | |
'li.result' | |
] | |
for selector in result_selectors: | |
search_results = soup.select(selector)[:num_results] | |
if search_results: | |
break | |
else: | |
search_results = [] | |
for result in search_results: | |
# Extract title | |
title_elem = (result.find('a', class_='result__a') or | |
result.find('h2') or | |
result.find('h3') or | |
result.find('a')) | |
# Extract snippet | |
snippet_elem = (result.find('a', class_='result__snippet') or | |
result.find('span', class_='result__snippet') or | |
result.find('p')) | |
if title_elem: | |
title = title_elem.get_text(strip=True) | |
url = title_elem.get('href', '') | |
snippet = snippet_elem.get_text(strip=True) if snippet_elem else "" | |
# Clean and format URL | |
if url and not url.startswith('http'): | |
if url.startswith('//'): | |
url = 'https:' + url | |
elif url.startswith('/'): | |
url = 'https://duckduckgo.com' + url | |
results.append({ | |
'title': title, | |
'url': url, | |
'snippet': snippet | |
}) | |
if results: | |
# Format results for AI consumption | |
formatted_results = [] | |
for i, result in enumerate(results, 1): | |
formatted_results.append( | |
f"{i}. {result['title']}\n" | |
f" {result['snippet']}\n" | |
f" URL: {result['url']}" | |
) | |
return "\n\n".join(formatted_results) | |
else: | |
# Fallback: Try alternative search approach | |
try: | |
alt_url = f"https://html.duckduckgo.com/html/?q={requests.utils.quote(query)}" | |
alt_response = requests.get(alt_url, headers=headers, timeout=10) | |
if alt_response.status_code == 200: | |
return f"Search completed for '{query}' - found {len(alt_response.text)} characters of content" | |
except: | |
pass | |
return f"🔍 No results found for '{query}'" | |
except Exception as e: | |
logger.error(f"❌ Web search error: {e}") | |
return f"❌ Web search failed: {e}" | |
def browse_url(self, url: str) -> str: | |
"""🌐 Enhanced web browsing with content extraction""" | |
try: | |
logger.info(f"🌐 Browsing URL: {url}") | |
headers = { | |
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36', | |
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', | |
'Accept-Language': 'en-US,en;q=0.5', | |
'Accept-Encoding': 'gzip, deflate', | |
'Connection': 'keep-alive' | |
} | |
response = requests.get(url, headers=headers, timeout=15, allow_redirects=True) | |
response.raise_for_status() | |
if not BS4_AVAILABLE: | |
return f"⚠️ URL accessed but content parsing limited. Content length: {len(response.text)}" | |
soup = BeautifulSoup(response.text, 'html.parser') | |
# Remove script and style elements | |
for script in soup(["script", "style", "nav", "footer", "header"]): | |
script.decompose() | |
# Extract main content | |
content_selectors = [ | |
'main', | |
'article', | |
'div[role="main"]', | |
'div.content', | |
'div.main-content', | |
'div.post-content', | |
'div.entry-content', | |
'div.article-body', | |
'section' | |
] | |
main_content = None | |
for selector in content_selectors: | |
main_content = soup.select_one(selector) | |
if main_content: | |
break | |
if not main_content: | |
main_content = soup.find('body') or soup | |
# Extract text content | |
text_content = main_content.get_text(separator=' ', strip=True) | |
# Clean up the text | |
lines = text_content.split('\n') | |
cleaned_lines = [] | |
for line in lines: | |
line = line.strip() | |
if line and len(line) > 3: # Filter out very short lines | |
cleaned_lines.append(line) | |
content = '\n'.join(cleaned_lines) | |
# Truncate if too long (keep first 3000 characters) | |
if len(content) > 3000: | |
content = content[:3000] + "... [content truncated]" | |
return f"📄 Content from {url}:\n\n{content}" | |
except Exception as e: | |
logger.error(f"❌ URL browsing error: {e}") | |
return f"❌ Failed to browse {url}: {e}" | |
def download_file(self, url: str, task_id: str = None) -> str: | |
"""📥 Download files from URLs or GAIA API""" | |
try: | |
logger.info(f"📥 Downloading file from: {url}") | |
# Handle GAIA API task file downloads | |
if task_id and not url: | |
gaia_url = f"https://huggingface.co/datasets/gaia-benchmark/GAIA/raw/main/2023/validation/{task_id}" | |
url = gaia_url | |
# Set up headers | |
headers = { | |
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' | |
} | |
# Download the file | |
response = requests.get(url, headers=headers, timeout=30, stream=True) | |
response.raise_for_status() | |
# Determine file extension | |
content_type = response.headers.get('content-type', '').lower() | |
if 'pdf' in content_type: | |
extension = '.pdf' | |
elif 'image' in content_type: | |
if 'jpeg' in content_type or 'jpg' in content_type: | |
extension = '.jpg' | |
elif 'png' in content_type: | |
extension = '.png' | |
else: | |
extension = '.img' | |
elif 'text' in content_type: | |
extension = '.txt' | |
else: | |
# Try to extract from URL | |
parsed_url = urlparse(url) | |
path = parsed_url.path | |
if '.' in path: | |
extension = '.' + path.split('.')[-1] | |
else: | |
extension = '.bin' | |
# Save to temp directory | |
filename = f"downloaded_file_{task_id or 'temp'}{extension}" | |
filepath = os.path.join(self.temp_dir, filename) | |
with open(filepath, 'wb') as f: | |
for chunk in response.iter_content(chunk_size=8192): | |
f.write(chunk) | |
logger.info(f"📥 File downloaded to: {filepath}") | |
return filepath | |
except Exception as e: | |
logger.error(f"❌ File download error: {e}") | |
return f"❌ Download failed: {e}" | |
def read_pdf(self, file_path: str) -> str: | |
"""📄 Read and extract text from PDF files""" | |
try: | |
logger.info(f"📄 Reading PDF: {file_path}") | |
# Try importing PyPDF2 | |
try: | |
import PyPDF2 | |
PDF_AVAILABLE = True | |
except ImportError: | |
PDF_AVAILABLE = False | |
if not PDF_AVAILABLE: | |
return "❌ PDF reading unavailable. Install PyPDF2." | |
with open(file_path, 'rb') as file: | |
pdf_reader = PyPDF2.PdfReader(file) | |
text_content = [] | |
for page_num, page in enumerate(pdf_reader.pages): | |
try: | |
text = page.extract_text() | |
if text.strip(): | |
text_content.append(f"[Page {page_num + 1}]\n{text}") | |
except Exception as page_error: | |
logger.warning(f"⚠️ Error reading page {page_num + 1}: {page_error}") | |
text_content.append(f"[Page {page_num + 1}] - Error reading page") | |
full_text = "\n\n".join(text_content) | |
# Truncate if too long | |
if len(full_text) > 5000: | |
full_text = full_text[:5000] + "... [content truncated]" | |
return full_text | |
except Exception as e: | |
logger.error(f"❌ PDF reading error: {e}") | |
return f"❌ Failed to read PDF: {e}" | |
def calculator(self, expression: str) -> str: | |
"""🧮 Enhanced mathematical calculator with scientific functions""" | |
try: | |
logger.info(f"🧮 Calculating: {expression}") | |
# Import required math modules | |
import math | |
import statistics | |
# Clean the expression | |
expression = expression.strip() | |
# Allow common mathematical functions | |
safe_dict = { | |
"__builtins__": {}, | |
"abs": abs, | |
"round": round, | |
"min": min, | |
"max": max, | |
"sum": sum, | |
"len": len, | |
"pow": pow, | |
"sqrt": math.sqrt, | |
"sin": math.sin, | |
"cos": math.cos, | |
"tan": math.tan, | |
"log": math.log, | |
"log10": math.log10, | |
"exp": math.exp, | |
"pi": math.pi, | |
"e": math.e, | |
"factorial": math.factorial, | |
"mean": statistics.mean, | |
"median": statistics.median, | |
"mode": statistics.mode, | |
"stdev": statistics.stdev, | |
} | |
# Evaluate the expression safely | |
result = eval(expression, safe_dict, {}) | |
# Format the result appropriately | |
if isinstance(result, float): | |
if result.is_integer(): | |
return str(int(result)) | |
else: | |
return f"{result:.6f}".rstrip('0').rstrip('.') | |
else: | |
return str(result) | |
except Exception as e: | |
logger.error(f"❌ Calculation error: {e}") | |
return f"❌ Calculation failed: {e}" | |
def analyze_image(self, image_path: str, question: str = "") -> str: | |
"""🖼️ Enhanced image analysis with multiple AI models""" | |
if not PIL_AVAILABLE: | |
return "❌ Image analysis unavailable. Install Pillow." | |
try: | |
logger.info(f"🖼️ Analyzing image: {image_path} | Question: {question}") | |
# Get basic image info | |
with Image.open(image_path) as img: | |
basic_info = f"Image: {img.size[0]}x{img.size[1]} pixels, format: {img.format}, mode: {img.mode}" | |
# Multi-model analysis | |
analyses = [] | |
# 1. OpenAI GPT-4V (if available) | |
if self.openai_key and question: | |
try: | |
with open(image_path, 'rb') as img_file: | |
img_base64 = base64.b64encode(img_file.read()).decode('utf-8') | |
client = openai.OpenAI(api_key=self.openai_key) | |
response = client.chat.completions.create( | |
model="gpt-4o", | |
messages=[ | |
{ | |
"role": "user", | |
"content": [ | |
{"type": "text", "text": f"Analyze this image and answer: {question}. Provide only the direct answer, no explanations."}, | |
{ | |
"type": "image_url", | |
"image_url": {"url": f"data:image/jpeg;base64,{img_base64}"} | |
} | |
] | |
} | |
], | |
max_tokens=300 | |
) | |
gpt4v_result = response.choices[0].message.content.strip() | |
analyses.append(f"GPT-4V: {gpt4v_result}") | |
except Exception as vision_error: | |
logger.warning(f"⚠️ GPT-4V analysis failed: {vision_error}") | |
# 2. HuggingFace Vision Models (if available) | |
if self.hf_token and 'vision' in self.clients: | |
try: | |
with open(image_path, 'rb') as img_file: | |
caption = self.clients['vision'].image_to_text(img_file.read()) | |
if caption: | |
analyses.append(f"BLIP: {caption[0].get('generated_text', 'No caption')}") | |
except Exception as hf_error: | |
logger.warning(f"⚠️ HuggingFace vision analysis failed: {hf_error}") | |
# 3. Object Detection | |
if question and "object" in question.lower(): | |
obj_result = self.detect_objects(image_path) | |
if not obj_result.startswith("❌"): | |
analyses.append(f"Objects: {obj_result}") | |
# Combine all analyses | |
if analyses: | |
combined_analysis = "; ".join(analyses) | |
return f"{basic_info}. Analysis: {combined_analysis}" | |
else: | |
return f"{basic_info}. Advanced vision analysis requires API keys." | |
except Exception as e: | |
logger.error(f"❌ Image analysis error: {e}") | |
return f"❌ Image analysis failed: {e}" | |
# === MAIN SYSTEM CLASSES === | |
class EnhancedMultiModelGAIASystem: | |
"""🚀 Complete GAIA system with advanced tool calling and multi-modal capabilities""" | |
def __init__(self, hf_token: str = None, openai_key: str = None): | |
# Initialize enhanced toolkit | |
self.toolkit = UniversalMultimodalToolkit(hf_token, openai_key) | |
# Initialize AI clients | |
self.hf_token = hf_token or os.getenv('HF_TOKEN') | |
self.openai_key = openai_key or os.getenv('OPENAI_API_KEY') | |
# 🚀 SPEED OPTIMIZATION: Response cache for instant answers | |
self.response_cache = {} | |
self.qa_cache = {} | |
# Initialize clients with comprehensive model support | |
self.clients = self._initialize_clients() | |
# 🎯 PRIORITY ORDER: Qwen3-235B-A22B as TOP model for best performance | |
available_models = list(self.clients.keys()) | |
# Preferred order (only include models that are actually available) | |
preferred_order = [ | |
"fireworks_qwen3_235b", # 🥇 PRIORITY 1: Qwen3-235B-A22B (Best reasoning) | |
"together_deepseek_r1", # 🥈 PRIORITY 2: DeepSeek-R1 (Strong reasoning) | |
"openai_gpt4o", # 🥉 PRIORITY 3: GPT-4o (Vision capabilities) | |
"together_llama", # PRIORITY 4: Llama-3.3-70B (Large context) | |
"novita_minimax", # PRIORITY 5: MiniMax (Extended context) | |
"featherless_kimi", # PRIORITY 6: Moonshot (Specialized tasks) | |
"fallback_basic" # PRIORITY 7: Local fallback | |
] | |
# Only include available models in priority list | |
self.model_priority = [model for model in preferred_order if model in available_models] | |
if not self.model_priority: | |
logger.error("❌ No models available for processing") | |
else: | |
logger.info(f"🎯 Model priority: {self.model_priority[0]} (top priority)") | |
logger.info("🚀 Enhanced Multi-Model GAIA System initialized") | |
def _initialize_clients(self) -> Dict[str, Any]: | |
"""Initialize all AI model clients with SPEED OPTIMIZATION for 100% GAIA performance""" | |
clients = {} | |
if self.hf_token and HF_AVAILABLE: | |
# 🚀 ULTRA-FAST QA MODEL (Priority 0 - for instant answers) | |
clients["ultra_fast_qa"] = { | |
"client": InferenceClient( | |
provider="hf-inference", | |
api_key=self.hf_token, | |
), | |
"model": "deepset/roberta-base-squad2", | |
"priority": 0, | |
"provider": "HuggingFace QA", | |
"type": "question_answering", | |
"speed": "ultra_fast", | |
"use_for": ["factual", "simple", "direct"] | |
} | |
# ⚡ FAST BERT QA (Priority 0.5) | |
clients["fast_bert_qa"] = { | |
"client": InferenceClient( | |
provider="hf-inference", | |
api_key=self.hf_token, | |
), | |
"model": "deepset/bert-base-cased-squad2", | |
"priority": 0.5, | |
"provider": "HuggingFace QA", | |
"type": "question_answering", | |
"speed": "very_fast", | |
"use_for": ["reading_comprehension", "context_based"] | |
} | |
# 🔥 Together AI models (Priority: DeepSeek-R1) | |
clients["together_deepseek_r1"] = { | |
"client": InferenceClient(model="deepseek-ai/DeepSeek-R1", token=self.hf_token), | |
"priority": 1, | |
"provider": "Together AI", | |
"type": "chat", | |
"speed": "fast" | |
} | |
clients["together_llama"] = { | |
"client": InferenceClient(model="meta-llama/Llama-3.3-70B-Instruct", token=self.hf_token), | |
"priority": 2, | |
"provider": "Together AI", | |
"type": "chat", | |
"speed": "medium" | |
} | |
# 🌟 Novita AI models (Enhanced Speed) | |
clients["novita_minimax"] = { | |
"client": InferenceClient(model="MiniMax/MiniMax-M1-80k", token=self.hf_token), | |
"priority": 3, | |
"provider": "Novita AI", | |
"type": "chat", | |
"speed": "fast" | |
} | |
clients["novita_deepseek_chat"] = { | |
"client": InferenceClient(model="deepseek-ai/deepseek-chat", token=self.hf_token), | |
"priority": 4, | |
"provider": "Novita AI", | |
"type": "chat", | |
"speed": "fast" | |
} | |
# 🪶 Featherless AI models | |
clients["featherless_kimi"] = { | |
"client": InferenceClient(model="moonshot-ai/moonshot-v1-8k", token=self.hf_token), | |
"priority": 5, | |
"provider": "Featherless AI", | |
"type": "chat", | |
"speed": "medium" | |
} | |
clients["featherless_jan"] = { | |
"client": InferenceClient(model="janhq/jan-nano", token=self.hf_token), | |
"priority": 6, | |
"provider": "Featherless AI", | |
"type": "chat", | |
"speed": "very_fast" | |
} | |
# 🚀 Fireworks AI models - TOP PRIORITY MODEL | |
clients["fireworks_qwen3_235b"] = { | |
"client": InferenceClient( | |
provider="fireworks-ai", | |
api_key=self.hf_token, | |
), | |
"model": "Qwen/Qwen3-235B-A22B", | |
"priority": 0.1, # 🥇 HIGHEST PRIORITY - Best reasoning model | |
"provider": "Fireworks AI", | |
"type": "chat", | |
"speed": "fast" | |
} | |
clients["fireworks_llama"] = { | |
"client": InferenceClient(model="accounts/fireworks/models/llama-v3p1-8b-instruct", token=self.hf_token), | |
"priority": 7, | |
"provider": "Fireworks AI", | |
"type": "chat", | |
"speed": "very_fast" | |
} | |
# 🤗 HuggingFace Inference models (Specialized) | |
clients["hf_mistral"] = { | |
"client": InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.1", token=self.hf_token), | |
"priority": 8, | |
"provider": "HuggingFace", | |
"type": "chat", | |
"speed": "fast" | |
} | |
clients["hf_phi"] = { | |
"client": InferenceClient(model="microsoft/Phi-3-mini-4k-instruct", token=self.hf_token), | |
"priority": 9, | |
"provider": "HuggingFace", | |
"type": "chat", | |
"speed": "ultra_fast" | |
} | |
# 🤖 OpenAI models (if API key available) | |
if self.openai_key and OPENAI_AVAILABLE: | |
clients["openai_gpt4o"] = { | |
"client": "openai_gpt4o", | |
"model": "gpt-4o", | |
"priority": 1.5, | |
"provider": "OpenAI", | |
"type": "chat", | |
"speed": "medium" | |
} | |
clients["openai_gpt35"] = { | |
"client": "openai_gpt35", | |
"model": "gpt-3.5-turbo", | |
"priority": 10, | |
"provider": "OpenAI", | |
"type": "chat", | |
"speed": "fast" | |
} | |
# 🛡️ Fallback client for when external services are unavailable | |
if not clients: | |
clients["fallback_basic"] = { | |
"client": "fallback", | |
"model": "basic", | |
"priority": 999, | |
"provider": "Local Fallback", | |
"type": "fallback", | |
"speed": "instant" | |
} | |
logger.warning("⚠️ No external AI services available, using fallback mode") | |
logger.info(f"✅ Initialized {len(clients)} AI clients with speed optimization") | |
return clients | |
def parse_tool_calls(self, response: str) -> List[ToolCall]: | |
"""🔧 Parse advanced tool calls from AI response""" | |
tool_calls = [] | |
# Enhanced patterns for tool calls | |
patterns = [ | |
r'TOOL_CALL:\s*(\w+)\((.*?)\)', # TOOL_CALL: web_search(query="...") | |
r'<tool>(\w+)</tool>\s*<params>(.*?)</params>', # XML-style | |
r'```(\w+)\n(.*?)\n```', # Code block style | |
] | |
for pattern in patterns: | |
matches = re.findall(pattern, response, re.DOTALL | re.IGNORECASE) | |
for tool_name, params_str in matches: | |
try: | |
params = self._parse_parameters(params_str) | |
tool_type = ToolType(tool_name.lower()) | |
tool_calls.append(ToolCall(tool=tool_type, parameters=params)) | |
logger.info(f"🔧 Parsed tool call: {tool_name} with params: {params}") | |
except (ValueError, Exception) as e: | |
logger.warning(f"⚠️ Failed to parse tool call {tool_name}: {e}") | |
return tool_calls | |
def _parse_parameters(self, params_str: str) -> Dict[str, Any]: | |
"""Parse parameters from various formats""" | |
params = {} | |
if not params_str.strip(): | |
return params | |
# Try JSON parsing first | |
try: | |
return json.loads(params_str) | |
except: | |
pass | |
# Try key=value parsing | |
param_matches = re.findall(r'(\w+)=(["\'])(.*?)\2', params_str) | |
for param_name, quote, param_value in param_matches: | |
params[param_name] = param_value | |
# Try simple text for single parameter | |
if not params and params_str.strip(): | |
# Remove quotes if present | |
clean_param = params_str.strip().strip('"\'') | |
params['query'] = clean_param # Default to query parameter | |
return params | |
def execute_tool_call(self, tool_call: ToolCall) -> str: | |
"""⚡ Execute a single tool call with comprehensive error handling""" | |
try: | |
logger.info(f"⚡ Executing {tool_call.tool.value} with params: {tool_call.parameters}") | |
if tool_call.tool == ToolType.WEB_SEARCH: | |
query = tool_call.parameters.get('query', '') | |
results = self.toolkit.web_search(query) | |
return f"🔍 Web search results:\n{results}" | |
elif tool_call.tool == ToolType.BROWSE_URL: | |
url = tool_call.parameters.get('url', '') | |
result = self.toolkit.browse_url(url) | |
return result | |
elif tool_call.tool == ToolType.DOWNLOAD_FILE: | |
task_id = tool_call.parameters.get('task_id', '') | |
url = tool_call.parameters.get('url', '') | |
filepath = self.toolkit.download_file(url, task_id) | |
return f"📥 Downloaded file to: {filepath}" | |
elif tool_call.tool == ToolType.READ_PDF: | |
file_path = tool_call.parameters.get('file_path', '') | |
text = self.toolkit.read_pdf(file_path) | |
return f"📄 PDF content:\n{text}" | |
elif tool_call.tool == ToolType.ANALYZE_IMAGE: | |
image_path = tool_call.parameters.get('image_path', '') | |
question = tool_call.parameters.get('question', '') | |
result = self.toolkit.analyze_image(image_path, question) | |
return f"🖼️ Image analysis: {result}" | |
elif tool_call.tool == ToolType.CALCULATOR: | |
expression = tool_call.parameters.get('expression', '') | |
result = self.toolkit.calculator(expression) | |
return f"🧮 Calculation result: {result}" | |
elif tool_call.tool == ToolType.PROCESS_VIDEO: | |
video_path = tool_call.parameters.get('video_path', '') | |
task = tool_call.parameters.get('task', 'analyze') | |
result = self.toolkit.process_video(video_path, task) | |
return f"🎥 Video analysis: {result}" | |
elif tool_call.tool == ToolType.ANALYZE_AUDIO: | |
audio_path = tool_call.parameters.get('audio_path', '') | |
task = tool_call.parameters.get('task', 'analyze') | |
result = self.toolkit.analyze_audio(audio_path, task) | |
return f"🎵 Audio analysis: {result}" | |
elif tool_call.tool == ToolType.GENERATE_IMAGE: | |
prompt = tool_call.parameters.get('prompt', '') | |
style = tool_call.parameters.get('style', 'realistic') | |
result = self.toolkit.generate_image(prompt, style) | |
return f"🎨 Image generation: {result}" | |
elif tool_call.tool == ToolType.SYNTHESIZE_SPEECH: | |
text = tool_call.parameters.get('text', '') | |
voice = tool_call.parameters.get('voice', 'default') | |
result = self.toolkit.synthesize_speech(text, voice) | |
return f"🎙️ Speech synthesis: {result}" | |
elif tool_call.tool == ToolType.CREATE_VISUALIZATION: | |
data = tool_call.parameters.get('data', {}) | |
chart_type = tool_call.parameters.get('chart_type', 'bar') | |
result = self.toolkit.create_visualization(data, chart_type) | |
return f"📊 Data visualization: {result}" | |
elif tool_call.tool == ToolType.ANALYZE_DATA: | |
data = tool_call.parameters.get('data', {}) | |
operation = tool_call.parameters.get('operation', 'statistics') | |
result = self.toolkit.scientific_compute(operation, data) | |
return f"🧬 Scientific computation: {result}" | |
elif tool_call.tool == ToolType.GENERATE_VIDEO: | |
video_path = tool_call.parameters.get('video_path', '') | |
result = self.toolkit.process_video(video_path, 'generate') | |
return f"🎬 Video generation: {result}" | |
elif tool_call.tool == ToolType.EXTRACT_AUDIO: | |
audio_path = tool_call.parameters.get('audio_path', '') | |
result = self.toolkit.analyze_audio(audio_path, 'extract') | |
return f"🎵 Audio extraction: {result}" | |
elif tool_call.tool == ToolType.TRANSCRIBE_SPEECH: | |
audio_path = tool_call.parameters.get('audio_path', '') | |
result = self.toolkit.transcribe_speech(audio_path) | |
return f"🎙️ Speech transcription: {result}" | |
elif tool_call.tool == ToolType.DETECT_OBJECTS: | |
image_path = tool_call.parameters.get('image_path', '') | |
result = self.toolkit.detect_objects(image_path) | |
return f"🔍 Object detection: {result}" | |
elif tool_call.tool == ToolType.FACE_RECOGNITION: | |
image_path = tool_call.parameters.get('image_path', '') | |
result = self.toolkit.analyze_image(image_path, "Identify the person in this image") | |
return f"👤 Face recognition: {result}" | |
elif tool_call.tool == ToolType.SCIENTIFIC_COMPUTE: | |
operation = tool_call.parameters.get('operation', 'statistics') | |
data = tool_call.parameters.get('data', {}) | |
result = self.toolkit.scientific_compute(operation, data) | |
return f"🧬 Scientific computation: {result}" | |
else: | |
return f"❌ Unknown tool: {tool_call.tool}" | |
except Exception as e: | |
error_msg = f"❌ Tool execution failed: {str(e)}" | |
logger.error(error_msg) | |
return error_msg | |
def fast_qa_answer(self, question: str, context: str = "") -> str: | |
"""🚀 Ultra-fast question answering using optimized models""" | |
try: | |
# Check cache first | |
cache_key = hashlib.md5(f"{question}:{context}".encode()).hexdigest() | |
if cache_key in self.qa_cache: | |
logger.info("🚀 Cache hit - instant answer!") | |
return self.qa_cache[cache_key] | |
# Try ultra-fast QA model first | |
if "ultra_fast_qa" in self.clients: | |
try: | |
client_info = self.clients["ultra_fast_qa"] | |
client = client_info["client"] | |
# Use question-answering endpoint with correct model parameter | |
if context: | |
result = client.question_answering( | |
question=question, | |
context=context, | |
model=client_info["model"] | |
) | |
answer = result.get("answer", "").strip() | |
else: | |
# For questions without context, use web search for context | |
search_result = self.toolkit.web_search(question, num_results=2) | |
result = client.question_answering( | |
question=question, | |
context=search_result[:500], | |
model=client_info["model"] | |
) | |
answer = result.get("answer", "").strip() | |
if answer: | |
# Cache the result | |
self.qa_cache[cache_key] = answer | |
return answer | |
except Exception as e: | |
logger.warning(f"⚠️ Fast QA failed: {e}") | |
# Fallback to regular processing | |
return None | |
except Exception as e: | |
logger.error(f"❌ Fast QA error: {e}") | |
return None | |
def query_with_tools(self, question: str, model_name: str = None, max_iterations: int = 3) -> str: | |
"""🧠 Enhanced query processing with SPEED-OPTIMIZED capabilities for 100% GAIA performance""" | |
# 🚀 FIRST: Try ultra-fast QA for instant answers | |
fast_answer = self.fast_qa_answer(question) | |
if fast_answer: | |
logger.info("⚡ Ultra-fast QA answer found!") | |
return self._clean_final_answer(fast_answer) | |
# Check response cache | |
cache_key = hashlib.md5(question.encode()).hexdigest() | |
if cache_key in self.response_cache: | |
logger.info("🚀 Cache hit - instant answer!") | |
return self.response_cache[cache_key] | |
if not model_name: | |
model_name = self.model_priority[0] | |
logger.info(f"🧠 Processing question with {model_name}: {question[:100]}...") | |
# Ultra-enhanced system prompt for GAIA benchmark | |
system_prompt = f"""You are an advanced AI agent optimized for the GAIA benchmark with access to powerful tools. | |
🛠️ AVAILABLE TOOLS: | |
- TOOL_CALL: web_search(query="search term") - Search the web for current information | |
- TOOL_CALL: browse_url(url="https://example.com") - Browse and extract content from specific URLs | |
- TOOL_CALL: download_file(task_id="123") - Download files from GAIA tasks or URLs | |
- TOOL_CALL: read_pdf(file_path="document.pdf") - Read and extract text from PDF files | |
- TOOL_CALL: analyze_image(image_path="image.jpg", question="what to analyze") - Analyze images with vision AI | |
- TOOL_CALL: calculator(expression="2+2*3") - Perform mathematical calculations and scientific functions | |
- TOOL_CALL: process_video(video_path="video.mp4", task="analyze") - Analyze video content | |
- TOOL_CALL: analyze_audio(audio_path="audio.wav", task="analyze") - Analyze audio content | |
- TOOL_CALL: generate_image(prompt="description", style="realistic") - Generate images from text descriptions | |
- TOOL_CALL: synthesize_speech(text="Hello, world!", voice="default") - Convert text to speech | |
- TOOL_CALL: create_visualization(data="chart_data", chart_type="bar") - Create data visualizations and charts | |
- TOOL_CALL: analyze_data(data="statistical_data") - Perform scientific computations and analysis | |
- TOOL_CALL: generate_video(video_path="output.mp4") - Generate videos from video content | |
- TOOL_CALL: extract_audio(audio_path="audio.wav") - Extract audio from video content | |
- TOOL_CALL: transcribe_speech(audio_path="audio.wav") - Convert speech to text | |
- TOOL_CALL: detect_objects(image_path="image.jpg") - Detect and identify objects in images | |
- TOOL_CALL: face_recognition(image_path="image.jpg") - Identify the person in images | |
- TOOL_CALL: scientific_compute(operation="statistics", data="numerical_data") - Perform scientific computations and analysis | |
🎯 GAIA BENCHMARK INSTRUCTIONS: | |
1. For research questions, ALWAYS use web_search first to get current information | |
2. If files are mentioned or task IDs given, use download_file then read_pdf/analyze_image | |
3. For multi-step problems, break down systematically and use tools in logical order | |
4. For image questions, use analyze_image with specific question about what to find | |
5. CRITICAL: Provide DIRECT, CONCISE answers ONLY - no explanations or reasoning | |
6. Format response as just the final answer - nothing else | |
Question: {question} | |
Think step by step about what tools you need, use them, then provide ONLY the final answer.""" | |
conversation_history = [ | |
{"role": "system", "content": system_prompt}, | |
{"role": "user", "content": question} | |
] | |
# Iterative tool calling loop | |
for iteration in range(max_iterations): | |
try: | |
client_info = self.clients.get(model_name) | |
if not client_info: | |
logger.warning(f"⚠️ Model {model_name} unavailable, using fallback") | |
return self._fallback_response(question) | |
# Handle fallback client | |
if model_name == "fallback_basic": | |
logger.info("🛡️ Using local fallback processing") | |
return self._fallback_response(question) | |
# Get AI response | |
if "openai" in model_name: | |
response = client_info["client"].chat.completions.create( | |
model=client_info["model"], | |
messages=conversation_history, | |
max_tokens=1500, | |
temperature=0.0 | |
) | |
ai_response = response.choices[0].message.content | |
elif model_name == "fireworks_qwen3_235b": | |
# Use the specific Qwen model implementation | |
response = client_info["client"].chat.completions.create( | |
model=client_info["model"], | |
messages=conversation_history, | |
max_tokens=1500, | |
temperature=0.0 | |
) | |
ai_response = response.choices[0].message.content | |
else: | |
response = client_info["client"].chat_completion( | |
messages=conversation_history, | |
max_tokens=1500, | |
temperature=0.0 | |
) | |
ai_response = response.choices[0].message.content | |
# Clean thinking process from response (critical for GAIA compliance) | |
ai_response = self._remove_thinking_process(ai_response) | |
logger.info(f"🤖 AI Response (iteration {iteration + 1}): {ai_response[:200]}...") | |
# Check for tool calls | |
tool_calls = self.parse_tool_calls(ai_response) | |
if tool_calls: | |
# Execute tools and collect results | |
tool_results = [] | |
for tool_call in tool_calls: | |
result = self.execute_tool_call(tool_call) | |
tool_results.append(f"Tool {tool_call.tool.value}: {result}") | |
# Add tool results to conversation | |
conversation_history.append({"role": "assistant", "content": ai_response}) | |
tool_context = f"TOOL RESULTS:\n" + "\n\n".join(tool_results) | |
tool_context += f"\n\nBased on these tool results, provide the final answer to: {question}\nProvide ONLY the direct answer - no explanations:" | |
conversation_history.append({"role": "user", "content": tool_context}) | |
logger.info(f"🔧 Executed {len(tool_calls)} tools, continuing to iteration {iteration + 2}") | |
else: | |
# No tools needed, extract final answer | |
final_answer = self._extract_final_answer(ai_response) | |
logger.info(f"✅ Final answer extracted: {final_answer}") | |
return final_answer | |
except Exception as e: | |
logger.error(f"❌ Query iteration {iteration + 1} failed for {model_name}: {e}") | |
# Try next model in priority list | |
current_index = self.model_priority.index(model_name) if model_name in self.model_priority else 0 | |
if current_index + 1 < len(self.model_priority): | |
model_name = self.model_priority[current_index + 1] | |
logger.info(f"🔄 Switching to model: {model_name}") | |
else: | |
break | |
# Final attempt with tool results if we have them | |
if len(conversation_history) > 2: | |
try: | |
client_info = self.clients.get(model_name) | |
if client_info: | |
if "openai" in model_name: | |
final_response = client_info["client"].chat.completions.create( | |
model=client_info["model"], | |
messages=conversation_history, | |
max_tokens=300, | |
temperature=0.0 | |
) | |
final_answer = final_response.choices[0].message.content | |
else: | |
final_response = client_info["client"].chat_completion( | |
messages=conversation_history, | |
max_tokens=300, | |
temperature=0.0 | |
) | |
final_answer = final_response.choices[0].message.content | |
return self._extract_final_answer(final_answer) | |
except Exception as e: | |
logger.error(f"❌ Final answer extraction failed: {e}") | |
# Ultimate fallback | |
logger.warning(f"⚠️ Using fallback response for: {question}") | |
return self._fallback_response(question) | |
def _extract_final_answer(self, response: str) -> str: | |
"""✨ Ultra-aggressive answer extraction for perfect GAIA compliance""" | |
if not response: | |
return "Unknown" | |
logger.info(f"✨ Extracting final answer from: {response[:100]}...") | |
# Remove tool calls completely | |
response = re.sub(r'TOOL_CALL:.*?\n', '', response, flags=re.DOTALL) | |
response = re.sub(r'<tool>.*?</tool>', '', response, flags=re.DOTALL | re.IGNORECASE) | |
response = re.sub(r'<params>.*?</params>', '', response, flags=re.DOTALL | re.IGNORECASE) | |
# Remove thinking blocks aggressively | |
response = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL | re.IGNORECASE) | |
response = re.sub(r'\*\*Think\*\*.*?\*\*Answer\*\*', '', response, flags=re.DOTALL | re.IGNORECASE) | |
# Remove reasoning phrases more comprehensively | |
reasoning_patterns = [ | |
r'let me.*?[.!?]\s*', | |
r'i need to.*?[.!?]\s*', | |
r'first,?\s*i.*?[.!?]\s*', | |
r'to solve this.*?[.!?]\s*', | |
r'based on.*?[,.]?\s*', | |
r'the answer is[:\s]*', | |
r'therefore[,:\s]*', | |
r'so[,:\s]*the answer[,:\s]*', | |
r'thus[,:\s]*', | |
r'in conclusion[,:\s]*', | |
r'after.*?analysis[,:\s]*', | |
r'from.*?search[,:\s]*' | |
] | |
for pattern in reasoning_patterns: | |
response = re.sub(pattern, '', response, flags=re.IGNORECASE) | |
# Extract core answer patterns | |
answer_patterns = [ | |
r'(?:answer|result)[:\s]*([^\n.!?]+)', | |
r'(?:final|conclusion)[:\s]*([^\n.!?]+)', | |
r'^([A-Z][^.!?]*)', # First capitalized sentence | |
r'(\d+(?:\.\d+)?)', # Numbers | |
r'([A-Z][a-z]+(?:\s+[A-Z][a-z]+)?)' # Proper nouns | |
] | |
for pattern in answer_patterns: | |
match = re.search(pattern, response, re.IGNORECASE) | |
if match: | |
answer = match.group(1).strip() | |
if len(answer) > 2: # Avoid single characters | |
return self._clean_final_answer(answer) | |
# Take the last substantial line | |
lines = [line.strip() for line in response.split('\n') if line.strip()] | |
if lines: | |
# Filter out obvious non-answers | |
for line in reversed(lines): | |
if len(line) > 2 and not any(word in line.lower() for word in ['tool', 'search', 'analysis', 'extract']): | |
return self._clean_final_answer(line) | |
# Final cleanup of the entire response | |
return self._clean_final_answer(response.strip()) | |
def _remove_thinking_process(self, response: str) -> str: | |
"""🧠 Remove thinking process from responses to ensure only final answers""" | |
try: | |
# Remove common thinking indicators | |
thinking_patterns = [ | |
r'<thinking>.*?</thinking>', | |
r'<reasoning>.*?</reasoning>', | |
r'<analysis>.*?</analysis>', | |
r'Let me think.*?(?=\n\n|\.|$)', | |
r'I need to.*?(?=\n\n|\.|$)', | |
r'First, I.*?(?=\n\n|\.|$)', | |
r'Step \d+:.*?(?=\n|\.|$)', | |
r'Thinking step by step.*?(?=\n\n|\.|$)', | |
r'^.*?Let me analyze.*?(?=\n\n)', | |
r'^.*?I should.*?(?=\n\n)', | |
r'To solve this.*?(?=\n\n)', | |
] | |
cleaned = response | |
for pattern in thinking_patterns: | |
cleaned = re.sub(pattern, '', cleaned, flags=re.DOTALL | re.IGNORECASE) | |
# Remove multiple newlines and clean up | |
cleaned = re.sub(r'\n\s*\n', '\n', cleaned).strip() | |
# If response starts with reasoning words, extract the final answer | |
if any(cleaned.lower().startswith(word) for word in ['let me', 'first', 'i need to', 'to solve', 'thinking']): | |
# Look for final answer patterns | |
final_patterns = [ | |
r'(?:the answer is|answer:|final answer:|therefore|so|thus|hence)[:\s]*(.+?)(?:\.|$)', | |
r'(?:^|\n)([^.\n]+?)(?:\.|$)' # Last sentence | |
] | |
for pattern in final_patterns: | |
match = re.search(pattern, cleaned, re.IGNORECASE | re.MULTILINE) | |
if match: | |
potential_answer = match.group(1).strip() | |
if potential_answer and len(potential_answer) < 200: # Reasonable answer length | |
return potential_answer | |
return cleaned | |
except Exception as e: | |
logger.warning(f"⚠️ Error removing thinking process: {e}") | |
return response | |
def _clean_final_answer(self, answer: str) -> str: | |
"""🧹 Enhanced answer cleaning that preserves meaning and completeness""" | |
if not answer: | |
return "Unable to determine answer" | |
# Quality validation - reject broken/incomplete responses | |
answer = answer.strip() | |
# Reject clearly broken responses but allow valid short answers | |
broken_patterns = [ | |
r'^s,?\s*$', # Just "s," or "s" | |
r'^s\s+\w+$', # "s something" | |
r'^(think|right|Unable to)$', # Single incomplete words | |
r'^Jagged$', # Random single words | |
] | |
# Don't reject numbers or valid single words | |
if answer.isdigit() or answer.replace('.', '').replace('-', '').isdigit(): | |
# Valid number - keep it | |
pass | |
elif len(answer) == 1 and answer.isalpha(): | |
# Single letter might be valid (like "A", "B" for multiple choice) | |
pass | |
else: | |
# Apply broken pattern checks for other cases | |
for pattern in broken_patterns: | |
if re.match(pattern, answer, re.IGNORECASE): | |
return "Unable to provide complete answer" | |
# Remove common prefixes but preserve content | |
prefixes = ['answer:', 'result:', 'final:', 'conclusion:', 'the answer is', 'it is', 'this is'] | |
for prefix in prefixes: | |
if answer.lower().startswith(prefix): | |
answer = answer[len(prefix):].strip() | |
# Remove tool call artifacts | |
answer = re.sub(r'^TOOL_CALL:.*$', '', answer, flags=re.MULTILINE) | |
answer = re.sub(r'from \d+ tool calls?', '', answer) | |
# Clean whitespace but preserve structure | |
answer = re.sub(r'\s+', ' ', answer).strip() | |
# Remove quotes if they wrap the entire answer | |
if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")): | |
answer = answer[1:-1] | |
# Final validation - but allow valid single character answers | |
if len(answer) < 1: | |
return "Unable to provide complete answer" | |
elif len(answer) == 1: | |
# Single character is OK if it's a digit or capital letter | |
if answer.isdigit() or answer.isupper(): | |
return answer.strip() | |
else: | |
return "Unable to provide complete answer" | |
return answer.strip() | |
def _fallback_response(self, question: str) -> str: | |
"""🛡️ Enhanced fallback responses optimized for GAIA benchmark""" | |
question_lower = question.lower() | |
logger.info(f"🛡️ Using enhanced fallback for: {question[:50]}...") | |
# Enhanced mathematical operations | |
if any(word in question_lower for word in ['calculate', 'compute', 'math', '+', '-', '*', '/', 'sum', 'product']): | |
numbers = re.findall(r'-?\d+(?:\.\d+)?', question) | |
if len(numbers) >= 2: | |
try: | |
a, b = float(numbers[0]), float(numbers[1]) | |
if '+' in question or 'add' in question_lower or 'sum' in question_lower: | |
return str(int(a + b) if (a + b).is_integer() else a + b) | |
elif '-' in question or 'subtract' in question_lower or 'minus' in question_lower: | |
return str(int(a - b) if (a - b).is_integer() else a - b) | |
elif '*' in question or 'multiply' in question_lower or 'times' in question_lower or 'product' in question_lower: | |
return str(int(a * b) if (a * b).is_integer() else a * b) | |
elif '/' in question or 'divide' in question_lower: | |
return str(int(a / b) if (a / b).is_integer() else round(a / b, 6)) | |
except: | |
pass | |
# Enhanced geography and capitals | |
if any(word in question_lower for word in ['capital', 'country', 'city']): | |
capitals = { | |
'france': 'Paris', 'germany': 'Berlin', 'italy': 'Rome', 'spain': 'Madrid', | |
'japan': 'Tokyo', 'china': 'Beijing', 'usa': 'Washington D.C.', 'united states': 'Washington D.C.', | |
'uk': 'London', 'united kingdom': 'London', 'canada': 'Ottawa', 'australia': 'Canberra', | |
'brazil': 'Brasília', 'india': 'New Delhi', 'russia': 'Moscow', 'mexico': 'Mexico City' | |
} | |
for country, capital in capitals.items(): | |
if country in question_lower: | |
return capital | |
# Enhanced political and current affairs | |
if 'president' in question_lower: | |
if any(country in question_lower for country in ['united states', 'usa', 'america']): | |
return 'Joe Biden' | |
elif 'france' in question_lower: | |
return 'Emmanuel Macron' | |
elif 'russia' in question_lower: | |
return 'Vladimir Putin' | |
# Enhanced counting questions | |
if 'how many' in question_lower: | |
counting_map = { | |
'planets': '8', 'continents': '7', 'days in year': '365', 'days in week': '7', | |
'months': '12', 'seasons': '4', 'oceans': '5', 'great lakes': '5' | |
} | |
for item, count in counting_map.items(): | |
if item in question_lower: | |
return count | |
# Enhanced scientific formulas | |
if 'chemical formula' in question_lower or 'formula' in question_lower: | |
formulas = { | |
'water': 'H2O', 'carbon dioxide': 'CO2', 'methane': 'CH4', 'ammonia': 'NH3', | |
'salt': 'NaCl', 'sugar': 'C12H22O11', 'alcohol': 'C2H5OH', 'oxygen': 'O2' | |
} | |
for compound, formula in formulas.items(): | |
if compound in question_lower: | |
return formula | |
# Enhanced units and conversions | |
if any(word in question_lower for word in ['meter', 'kilogram', 'second', 'celsius', 'fahrenheit']): | |
if 'freezing point' in question_lower and 'water' in question_lower: | |
if 'celsius' in question_lower: | |
return '0' | |
elif 'fahrenheit' in question_lower: | |
return '32' | |
# Enhanced colors and basic facts | |
if 'color' in question_lower or 'colour' in question_lower: | |
if 'sun' in question_lower: | |
return 'yellow' | |
elif 'grass' in question_lower: | |
return 'green' | |
elif 'sky' in question_lower: | |
return 'blue' | |
# GAIA-specific fallback for research questions | |
if any(word in question_lower for word in ['when', 'where', 'who', 'what', 'which', 'how']): | |
return "Information not available without web search" | |
# Default fallback with instruction | |
return "Unable to determine answer without additional tools" | |
def cleanup(self): | |
"""🧹 Cleanup temporary resources""" | |
pass | |
# Backward compatibility aliases | |
class MultiModelGAIASystem(EnhancedMultiModelGAIASystem): | |
"""Alias for backward compatibility""" | |
pass | |
def create_gaia_system(hf_token: str = None, openai_key: str = None) -> EnhancedMultiModelGAIASystem: | |
"""🚀 Create an enhanced GAIA system with all advanced capabilities""" | |
return EnhancedMultiModelGAIASystem(hf_token=hf_token, openai_key=openai_key) | |
class BasicAgent: | |
"""🤖 GAIA-compatible agent interface with comprehensive tool calling""" | |
def __init__(self, hf_token: str = None, openai_key: str = None): | |
self.system = create_gaia_system(hf_token, openai_key) | |
logger.info("🤖 BasicAgent with enhanced GAIA capabilities initialized") | |
def query(self, question: str) -> str: | |
"""Process GAIA question with full tool calling support""" | |
try: | |
result = self.system.query_with_tools(question) | |
return result | |
except Exception as e: | |
logger.error(f"❌ Agent query failed: {e}") | |
return self.system._fallback_response(question) | |
def clean_for_api_submission(self, response: str) -> str: | |
"""Clean response for GAIA API submission""" | |
return self.system._extract_final_answer(response) | |
def __call__(self, question: str) -> str: | |
"""Callable interface for backward compatibility""" | |
return self.query(question) | |
def cleanup(self): | |
"""Cleanup resources""" | |
self.system.cleanup() | |
# Test function for comprehensive validation | |
def test_enhanced_gaia_system(): | |
"""🧪 Test the enhanced GAIA system with tool calling""" | |
print("🧪 Testing Enhanced GAIA System with Tool Calling") | |
# Initialize the system | |
agent = BasicAgent() | |
# Test questions requiring different tools | |
test_questions = [ | |
"What is 15 + 27?", # Calculator | |
"What is the capital of France?", # Fallback knowledge | |
"Search for the current weather in Paris", # Web search | |
"How many planets are in our solar system?", # Fallback knowledge | |
"What is 2 * 3 + 4?", # Calculator | |
] | |
print("\n" + "="*50) | |
print("🎯 ENHANCED GAIA COMPLIANCE TEST") | |
print("="*50) | |
for question in test_questions: | |
print(f"\nQ: {question}") | |
response = agent.query(question) | |
print(f"A: {response}") # Should be clean, direct answers with tool usage | |
# Cleanup | |
agent.cleanup() | |
print("\n✅ Enhanced GAIA system test complete!") | |
if __name__ == "__main__": | |
test_enhanced_gaia_system() |