File size: 10,537 Bytes
fde38c3 b6f8bcb fde38c3 f0796bd fde38c3 b6f8bcb fde38c3 b6f8bcb fde38c3 b6f8bcb e068114 b6f8bcb 89a3ce8 fde38c3 e068114 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
import os
import re
import json
import torch
import numpy as np
import logging
from typing import Dict, List, Tuple, Optional
from tqdm import tqdm
from pydantic import BaseModel
from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
AutoModelForQuestionAnswering,
pipeline,
LogitsProcessor,
LogitsProcessorList,
PreTrainedModel,
PreTrainedTokenizer
)
from sentence_transformers import SentenceTransformer, CrossEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
from rank_bm25 import BM25Okapi
import PyPDF2
from sklearn.cluster import KMeans
import spacy
import subprocess
import gradio as gr
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s"
)
class ConfidenceCalibrator(LogitsProcessor):
def __init__(self, calibration_factor: float = 0.9):
self.calibration_factor = calibration_factor
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
return scores / self.calibration_factor
class DocumentResult(BaseModel):
content: str
confidence: float
source_page: int
supporting_evidence: List[str]
class OptimalModelSelector:
def __init__(self):
self.qa_models = {
"deberta-v3": ("deepset/deberta-v3-large-squad2", 0.87)
}
self.summarization_models = {
"bart": ("facebook/bart-large-cnn", 0.85)
}
self.current_models = {}
def get_best_model(self, task_type: str) -> Tuple[PreTrainedModel, PreTrainedTokenizer, float]:
model_map = self.qa_models if "qa" in task_type else self.summarization_models
best_model_name, best_score = max(model_map.items(), key=lambda x: x[1][1])
if best_model_name not in self.current_models:
tokenizer = AutoTokenizer.from_pretrained(model_map[best_model_name][0])
model = (AutoModelForQuestionAnswering if "qa" in task_type
else AutoModelForSeq2SeqLM).from_pretrained(model_map[best_model_name][0])
model = model.eval().half().to('cuda' if torch.cuda.is_available() else 'cpu')
self.current_models[best_model_name] = (model, tokenizer)
return *self.current_models[best_model_name], best_score
class PDFAugmentedRetriever:
def __init__(self, document_texts: List[str]):
self.documents = [(i, text) for i, text in enumerate(document_texts)]
self.bm25 = BM25Okapi([text.split() for _, text in self.documents])
self.encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
self.tfidf = TfidfVectorizer(stop_words='english').fit([text for _, text in self.documents])
def retrieve(self, query: str, top_k: int = 5) -> List[Tuple[int, str, float]]:
bm25_scores = self.bm25.get_scores(query.split())
semantic_scores = self.encoder.predict([(query, doc) for _, doc in self.documents])
combined_scores = 0.4 * bm25_scores + 0.6 * np.array(semantic_scores)
top_indices = np.argsort(combined_scores)[-top_k:][::-1]
return [(self.documents[i][0], self.documents[i][1], float(combined_scores[i]))
for i in top_indices]
class DetailedExplainer:
def __init__(self,
explanation_model: str = "google/flan-t5-large",
device: int = 0):
try:
self.nlp = spacy.load("en_core_web_sm")
except OSError:
subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
self.nlp = spacy.load("en_core_web_sm")
self.explainer = pipeline(
"text2text-generation",
model=explanation_model,
tokenizer=explanation_model,
device=device,
max_length=500,
max_new_tokens=800
)
def extract_concepts(self, text: str) -> list:
doc = self.nlp(text)
concepts = set()
for chunk in doc.noun_chunks:
if len(chunk) > 1 and not chunk.root.is_stop:
concepts.add(chunk.text.strip())
for ent in doc.ents:
if ent.label_ in ["PERSON", "ORG", "GPE", "NORP", "EVENT", "WORK_OF_ART"]:
concepts.add(ent.text.strip())
return list(concepts)
def explain_concept(self, concept: str, context: str, min_accuracy: float = 0.50) -> str:
prompt = (
f"The following sentence from a PDF is given \n{context}\n\n\nNow explain the concept '{concept}' mentioned above with at least {int(min_accuracy * 100)}% accuracy."
)
result = self.explainer(
prompt,
do_sample=False
)
return result[0]["generated_text"].strip()
def explain_text(self, text: str, context: str) -> dict:
concepts = self.extract_concepts(text)
explanations = {}
for concept in concepts:
explanations[concept] = self.explain_concept(concept, context)
return {"concepts": concepts, "explanations": explanations}
class AdvancedPDFAnalyzer:
def __init__(self):
self.logger = logging.getLogger("PDFAnalyzer")
self.model_selector = OptimalModelSelector()
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.qa_model, self.qa_tokenizer, _ = self.model_selector.get_best_model("qa")
self.qa_model = self.qa_model.to(self.device)
self.summarizer = pipeline(
"summarization",
model="facebook/bart-large-cnn",
device=0 if torch.cuda.is_available() else -1,
framework="pt"
)
self.logits_processor = LogitsProcessorList([
ConfidenceCalibrator(calibration_factor=0.85)
])
self.detailed_explainer = DetailedExplainer(device=0 if torch.cuda.is_available() else -1)
def extract_text_with_metadata(self, file_path: str) -> List[Dict]:
documents = []
with open(file_path, 'rb') as f:
reader = PyPDF2.PdfReader(f)
for i, page in enumerate(reader.pages):
text = page.extract_text()
if not text or not text.strip():
continue
page_number = i + 1
metadata = {
'source': os.path.basename(file_path),
'page': page_number,
'char_count': len(text),
'word_count': len(text.split()),
}
documents.append({
'content': self._clean_text(text),
'metadata': metadata
})
if not documents:
raise ValueError("No extractable content found in PDF")
return documents
def _clean_text(self, text: str) -> str:
text = re.sub(r'[\x00-\x1F\x7F-\x9F]', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'(\w)-\s+(\w)', r'\1\2', text)
return text.strip()
def answer_question(self, question: str, documents: List[Dict]) -> Dict:
retriever = PDFAugmentedRetriever([doc['content'] for doc in documents])
relevant_contexts = retriever.retrieve(question, top_k=3)
answers = []
for page_idx, context, similarity_score in relevant_contexts:
inputs = self.qa_tokenizer(
question,
context,
add_special_tokens=True,
return_tensors="pt",
max_length=512,
truncation=True
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.qa_model(**inputs)
start_logits = outputs.start_logits
end_logits = outputs.end_logits
logits_processor = LogitsProcessorList([ConfidenceCalibrator()])
start_logits = logits_processor(inputs['input_ids'], start_logits)
end_logits = logits_processor(inputs['input_ids'], end_logits)
start_prob = torch.nn.functional.softmax(start_logits, dim=-1)
end_prob = torch.nn.functional.softmax(end_logits, dim=-1)
max_start_score, max_start_idx = torch.max(start_prob, dim=-1)
max_start_idx_int = max_start_idx.item()
max_end_score, max_end_idx = torch.max(end_prob[0, max_start_idx_int:], dim=-1)
max_end_idx_int = max_end_idx.item() + max_start_idx_int
confidence = float((max_start_score * max_end_score) * 0.9 * similarity_score)
answer_tokens = inputs["input_ids"][0][max_start_idx_int:max_end_idx_int + 1]
answer = self.qa_tokenizer.decode(answer_tokens, skip_special_tokens=True)
explanations_result = self.detailed_explainer.explain_text(answer, context)
answers.append({
"answer": answer,
"confidence": confidence,
"context": context,
"page_number": documents[page_idx]['metadata']['page'],
"explanations": explanations_result
})
if not answers:
return {"answer": "No confident answer found", "confidence": 0.0, "explanations": {}}
best_answer = max(answers, key=lambda x: x['confidence'])
if best_answer['confidence'] < 0.85:
best_answer['answer'] = f"[Low Confidence] {best_answer['answer']}"
return best_answer
analyzer = AdvancedPDFAnalyzer()
documents = analyzer.extract_text_with_metadata("example.pdf")
def ask_question_gradio(question: str):
if not question.strip():
return "Please enter a valid question."
try:
result = analyzer.answer_question(question, documents)
answer = result['answer']
confidence = result['confidence']
explanation = "\n\n".join(
f"🔹 {concept}: {desc}"
for concept, desc in result.get("explanations", {}).get("explanations", {}).items()
)
return f"📌 **Answer**: {answer}\n\n🔒 **Confidence**: {confidence:.2f}\n\n📘 **Explanations**:\n{explanation}"
except Exception as e:
return f"❌ Error: {str(e)}"
demo = gr.Interface(
fn=ask_question_gradio,
inputs=gr.Textbox(label="Ask a question about the PDF"),
outputs=gr.Markdown(label="Answer"),
title="Quandans AI - Ask Questions",
description="Ask a question based on the document loaded in this system."
)
demo.launch()
|