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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForMultipleChoice, AutoModelForQuestionAnswering
import json
import collections # 如果您的 postprocess_qa_predictions 需要
# 假設 utils_qa.py 在同一目錄下 (或者您需要將其函數複製過來或確保可導入)
# from utils_qa import postprocess_qa_predictions # 您可能需要完整路徑或將其放入 requirements.txt
# --- 模型和分詞器加載 ---
# 建議從 Hugging Face Hub 加載您已經上傳的模型
# 這樣您的 Space 就不需要包含模型文件本身,保持輕量
TOKENIZER_PATH = "bert-base-chinese" # 或者您上傳的分詞器路徑
SELECTOR_MODEL_PATH = "TheWeeeed/chinese-paragraph-selector" # 替換為您上傳的段落選擇模型 ID
QA_MODEL_PATH = "TheWeeeed/chinese-extractive-qa" # 替換為您上傳的答案抽取模型 ID
try:
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
selector_model = AutoModelForMultipleChoice.from_pretrained(SELECTOR_MODEL_PATH)
qa_model = AutoModelForQuestionAnswering.from_pretrained(QA_MODEL_PATH)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
selector_model.to(device)
selector_model.eval()
qa_model.to(device)
qa_model.eval()
models_loaded_successfully = True
print(f"模型和分詞器加載成功,使用設備: {device}")
except Exception as e:
models_loaded_successfully = False
error_message = f"加載模型或分詞器時出錯: {e}"
print(error_message)
# 在 Gradio 界面中,我們可以顯示這個錯誤信息
# --- 從您的 inference_pipeline.py 中提取並調整以下函數 ---
def select_relevant_paragraph_gradio(question_text, candidate_paragraph_texts_str, model, tokenizer, device, max_seq_len):
# candidate_paragraph_texts_str 是一個由換行符分隔的字符串
candidate_paragraph_texts = [p.strip() for p in candidate_paragraph_texts_str.split('\n') if p.strip()]
if not candidate_paragraph_texts:
return "請至少提供一個候選段落。", -1
model.eval()
inputs_mc = []
for p_text in candidate_paragraph_texts:
inputs_mc.append(
tokenizer(
question_text, p_text, add_special_tokens=True, max_length=max_seq_len,
padding="max_length", truncation=True, return_tensors="pt"
)
)
input_ids = torch.stack([inp["input_ids"].squeeze(0) for inp in inputs_mc]).unsqueeze(0).to(device)
attention_mask = torch.stack([inp["attention_mask"].squeeze(0) for inp in inputs_mc]).unsqueeze(0).to(device)
token_type_ids = None
if "token_type_ids" in inputs_mc[0]:
token_type_ids = torch.stack([inp["token_type_ids"].squeeze(0) for inp in inputs_mc]).unsqueeze(0).to(device)
with torch.no_grad():
if token_type_ids is not None:
outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
else:
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
predicted_index = torch.argmax(outputs.logits, dim=1).item()
if predicted_index < len(candidate_paragraph_texts):
return candidate_paragraph_texts[predicted_index], predicted_index
else:
return "段落選擇索引錯誤。", -1
def prepare_features_for_qa_inference_gradio(question_id, question_text, selected_context, tokenizer, max_seq_len, doc_stride):
# 這個函數需要從您的 inference_pipeline.py 中提取並適當修改
# 它需要返回一個可以被 QA 模型使用的 Dataset 或 features 列表
# 簡化版:
from datasets import Dataset # 需要在 requirements.txt 中
qa_example_for_processing = {"id": [question_id], "question": [question_text], "context": [selected_context]}
temp_dataset = Dataset.from_dict(qa_example_for_processing)
pad_on_right = tokenizer.padding_side == "right"
qa_features = temp_dataset.map(
lambda examples: prepare_features_for_qa_inference( # 這是您 inference_pipeline.py 中的函數
examples, tokenizer, pad_on_right, max_seq_len, doc_stride
),
batched=True,
remove_columns=temp_dataset.column_names
)
return qa_features # 返回 Dataset 對象
# 您 inference_pipeline.py 中的 prepare_features_for_qa_inference 函數需要被複製到這裡
# 或者確保它可以被導入
def prepare_features_for_qa_inference(examples, tokenizer, pad_on_right, max_seq_len, doc_stride):
examples["question"] = [q.lstrip() if isinstance(q, str) else "" for q in examples["question"]]
questions = examples["question" if pad_on_right else "context"]
contexts = examples["context" if pad_on_right else "question"]
# Ensure questions and contexts are lists of strings, handle None by converting to empty string
questions = [q if isinstance(q, str) else "" for q in questions]
contexts = [c if isinstance(c, str) else "" for c in contexts]
tokenized_output = tokenizer(
questions,
contexts,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_len,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length", # This ensures all primary outputs are lists of numbers of fixed length
)
# The tokenizer with padding="max_length" should already produce lists of integers
# for input_ids, attention_mask, token_type_ids.
# The main risk of 'None' would be if the input strings were so problematic
# that the tokenizer failed internally in a way not producing standard padded output.
# However, standard tokenizers are quite robust with empty strings when padding is enabled.
# Let's directly create the structure we need for the output Dataset.
# `tokenized_output` is a BatchEncoding (dict-like).
# If `return_overflowing_tokens=True` and N features are generated from one example,
# then `tokenized_output['input_ids']` is a list of N lists.
processed_features = []
num_generated_features = len(tokenized_output["input_ids"]) # Number of features due to overflow
# `sample_mapping` maps each generated feature back to its original example index in the input `examples`
sample_mapping = tokenized_output.pop("overflow_to_sample_mapping", list(range(len(examples["id"]))))
for i in range(num_generated_features):
feature = {}
original_example_index = sample_mapping[i] # Index of the original example this feature came from
# These should always be lists of integers due to padding="max_length"
feature["input_ids"] = tokenized_output["input_ids"][i]
if "attention_mask" in tokenized_output:
feature["attention_mask"] = tokenized_output["attention_mask"][i]
if "token_type_ids" in tokenized_output:
feature["token_type_ids"] = tokenized_output["token_type_ids"][i]
# These might not be strictly needed by the model's forward pass but are used by postprocessing
feature["example_id"] = examples["id"][original_example_index]
current_offset_mapping = tokenized_output["offset_mapping"][i]
sequence_ids = tokenized_output.sequence_ids(i) # Pass the index of the feature
context_idx_in_pair = 1 if pad_on_right else 0
feature["offset_mapping"] = [
offset if sequence_ids[k] == context_idx_in_pair else None
for k, offset in enumerate(current_offset_mapping)
]
processed_features.append(feature)
# The .map function expects a dictionary where keys are column names
# and values are lists of features for those columns.
# Since we are processing one original example at a time (batched=True on a Dataset of 1 row),
# and this one example can produce multiple features, `processed_features` is a list of dicts.
# We need to return a dictionary of lists.
if not processed_features: # Should not happen if tokenizer works, but as a safeguard
# Return structure with empty lists to match expected features by .map()
# This case indicates an issue with tokenizing the input example.
logger.error(f"No features generated for example ID {examples['id'][0]}. Input q: {examples['question'][0]}, c: {examples['context'][0]}")
return {
"input_ids": [], "token_type_ids": [], "attention_mask": [],
"offset_mapping": [], "example_id": []
}
# Transpose the list of feature dictionaries into a dictionary of feature lists
# This is what the .map(batched=True) function expects as a return value
final_batch = {}
for key in processed_features[0].keys():
final_batch[key] = [feature[key] for feature in processed_features]
for key_to_check in ["input_ids", "attention_mask", "token_type_ids"]:
if key_to_check in final_batch:
for i, lst in enumerate(final_batch[key_to_check]):
if lst is None:
raise ValueError(f"在 prepare_features_for_qa_inference 中,{key_to_check} 的第 {i} 個特徵列表為 None!")
if any(x is None for x in lst):
raise ValueError(f"在 prepare_features_for_qa_inference 中,{key_to_check} 的第 {i} 個特徵列表內部包含 None!內容: {lst[:20]}")
return final_batch
# postprocess_qa_predictions 函數也需要從 utils_qa.py 複製或導入
# from utils_qa import postprocess_qa_predictions # 確保 utils_qa.py 在 Space 的環境中可用
# --- Gradio 界面函數 ---
def two_stage_qa(question, candidate_paragraphs_str, max_seq_len_mc=512, max_seq_len_qa=384, doc_stride_qa=128, n_best_size=20, max_answer_length=100):
if not models_loaded_successfully:
return f"錯誤: {error_message}", "N/A", "N/A"
if not question.strip() or not candidate_paragraphs_str.strip():
return "錯誤: 問題和候選段落不能為空。", "N/A", "N/A"
# 階段一
selected_paragraph, selected_idx = select_relevant_paragraph_gradio(
question, candidate_paragraphs_str, selector_model, tokenizer, device, max_seq_len_mc
)
if selected_idx == -1: # 段落選擇出錯
return f"段落選擇出錯: {selected_paragraph}", "N/A", selected_paragraph
# 階段二
# 準備 QA 特徵
qa_features_dataset = prepare_features_for_qa_inference_gradio(
"temp_id", question, selected_paragraph, tokenizer, max_seq_len_qa, doc_stride_qa
)
if len(qa_features_dataset) == 0:
return "錯誤: 無法為選定段落生成QA特徵 (可能段落太短或內容問題)。", f"選中的段落 (索引 {selected_idx}):\n{selected_paragraph}", "N/A"
# 創建 DataLoader
from transformers import default_data_collator # 需要導入
qa_dataloader = DataLoader(
qa_features_dataset, collate_fn=default_data_collator, batch_size=8 # batch_size可以小一些
)
all_start_logits = []
all_end_logits = []
for batch in qa_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs_qa = qa_model(**batch)
all_start_logits.append(outputs_qa.start_logits.cpu().numpy())
all_end_logits.append(outputs_qa.end_logits.cpu().numpy())
if not all_start_logits:
return "錯誤: QA模型沒有產生logits。", f"選中的段落 (索引 {selected_idx}):\n{selected_paragraph}", "N/A"
start_logits_np = np.concatenate(all_start_logits, axis=0)
end_logits_np = np.concatenate(all_end_logits, axis=0)
# 為了 postprocess_qa_predictions,我們需要原始的 example 數據
# 它期望一個包含 "answers" 字段的 Dataset
def add_empty_answers(example):
example["answers"] = {"text": [], "answer_start": []}
return example
# temp_dataset 用於 postprocessing
original_example_for_postproc = {"id": ["temp_id"], "question": [question], "context": [selected_paragraph]}
original_dataset_for_postproc = Dataset.from_dict(original_example_for_postproc).map(add_empty_answers)
# 後處理
# 確保 postprocess_qa_predictions 可用
predictions_dict = postprocess_qa_predictions(
examples=original_dataset_for_postproc, # 原始的、包含 context 和空 answers 的 Dataset
features=qa_features_dataset, # 包含 offset_mapping 和 example_id 的 Dataset
predictions=(start_logits_np, end_logits_np),
version_2_with_negative=False,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
null_score_diff_threshold=0.0,
output_dir=None,
prefix="gradio_predict",
is_world_process_zero=True
)
final_answer = predictions_dict.get("temp_id", "未能提取答案。")
return final_answer, f"選中的段落 (索引 {selected_idx}):\n{selected_paragraph}", predictions_dict
# --- 創建 Gradio 界面 ---
# 定義預設的問題和段落內容
DEFAULT_QUESTION = "世界最高峰是什麼?"
DEFAULT_PARAGRAPHS = (
"珠穆朗瑪峰是喜馬拉雅山脈的主峰,位於中國與尼泊爾邊界上,是世界海拔最高的山峰。\n"
"喬戈里峰,又稱K2,是喀喇崑崙山脈的主峰,海拔8611米,是世界第二高峰,位於中國與巴基斯坦邊界。\n"
"干城章嘉峰位於喜馬拉雅山脈中段尼泊爾和印度邊界線上,海拔8586米,為世界第三高峰。\n"
"洛子峰,海拔8516米,為世界第四高峰,位於珠穆朗瑪峰以南約3公里處,同屬喜馬拉雅山脈。"
)
iface = gr.Interface(
fn=two_stage_qa, # 您的兩階段問答處理函數
inputs=[
gr.Textbox(
lines=2,
placeholder="輸入您的問題...",
label="問題 (Question)",
value=DEFAULT_QUESTION # <--- 為問題設置預設值
),
gr.Textbox(
lines=10,
placeholder="在此處輸入候選段落,每段一行...",
label="候選段落 (Candidate Paragraphs - One per line)",
value=DEFAULT_PARAGRAPHS # <--- 為段落設置預設值
)
],
outputs=[
gr.Textbox(label="預測答案 (Predicted Answer)"),
gr.Textbox(label="選中的相關段落 (Selected Relevant Paragraph)"),
gr.JSON(label="原始預測字典 (Raw Predictions Dict - for debugging)")
],
title="兩階段中文抽取式問答系統",
description="輸入一個問題和多個候選段落(每行一個段落)。系統會先選擇最相關的段落,然後從中抽取答案。",
allow_flagging="never" # 或者您希望的標記設置
)
if __name__ == "__main__":
if models_loaded_successfully: # 確保模型已加載才啟動
iface.launch()
else:
print(f"Gradio 應用無法啟動,因為模型加載失敗: {error_message if 'error_message' in locals() else '未知錯誤'}")
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