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| import torch | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForSeq2SeqLM, | |
| pipeline, | |
| AutoProcessor, | |
| AutoModelForSpeechSeq2Seq | |
| ) | |
| from ddgs import DDGS | |
| import pandas as pd | |
| import os | |
| SYSTEM_PROMPT = """ | |
| You are a helpful AI assistant. Think step by step to solve the problem. If the question requires reasoning, perform it. If it refers to a search or file, use the result provided. At the end, return ONLY the final answer string. No explanations. | |
| """ | |
| class GaiaAgent: | |
| def __init__(self, model_id="google/flan-t5-base"): | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| self.model = AutoModelForSeq2SeqLM.from_pretrained(model_id) | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.model.to(self.device) | |
| # Whisper via HF | |
| self.asr_model_id = "openai/whisper-small" | |
| self.asr_processor = AutoProcessor.from_pretrained(self.asr_model_id) | |
| self.asr_model = AutoModelForSpeechSeq2Seq.from_pretrained(self.asr_model_id).to(self.device) | |
| self.pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=self.asr_model, | |
| tokenizer=self.asr_processor.tokenizer, | |
| feature_extractor=self.asr_processor.feature_extractor, | |
| return_timestamps=False, | |
| device=0 if torch.cuda.is_available() else -1 | |
| ) | |
| def search(self, query: str) -> str: | |
| try: | |
| with DDGS() as ddgs: | |
| results = list(ddgs.text(query, safesearch="off")) | |
| if results: | |
| return results[0]['body'] | |
| except Exception: | |
| return "" | |
| return "" | |
| def transcribe_audio(self, file_path: str) -> str: | |
| try: | |
| result = self.pipe(file_path) | |
| return result['text'] | |
| except Exception: | |
| return "" | |
| def handle_excel(self, file_path: str) -> str: | |
| try: | |
| df = pd.read_excel(file_path) | |
| df.columns = [col.lower() for col in df.columns] | |
| if 'category' in df.columns and 'sales' in df.columns: | |
| food_sales = df[df['category'].str.lower() != 'drink']['sales'].sum() | |
| return f"{food_sales:.2f}" | |
| except Exception: | |
| return "" | |
| return "" | |
| def __call__(self, question: str, files: dict = None) -> tuple[str, str]: | |
| try: | |
| context = "" | |
| if files: | |
| for filename, filepath in files.items(): | |
| if filename.endswith(".mp3") or filename.endswith(".wav"): | |
| context = self.transcribe_audio(filepath) | |
| break | |
| elif filename.endswith(".xlsx"): | |
| excel_result = self.handle_excel(filepath) | |
| return excel_result.strip(), excel_result.strip() | |
| elif "http" in question.lower() or "wikipedia" in question.lower(): | |
| context = self.search(question) | |
| prompt = f"{SYSTEM_PROMPT}\n\n{context}\n\nQuestion: {question.strip()}" | |
| inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True).to(self.device) | |
| outputs = self.model.generate( | |
| **inputs, | |
| max_new_tokens=128, | |
| do_sample=False, | |
| pad_token_id=self.tokenizer.pad_token_id | |
| ) | |
| output_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| final = output_text.strip() | |
| return final, final | |
| except Exception as e: | |
| return "ERROR", f"Agent failed: {e}" | |