Spaces:
Sleeping
Sleeping
indev-v1
Browse files- README.md +79 -14
- app.py +813 -0
- requirements.txt +9 -0
README.md
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---
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title:
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colorFrom:
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sdk: gradio
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sdk_version: 5.29.0
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app_file: app.py
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pinned: false
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---
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title: PDF Q&A Dataset Generator
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emoji: π
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colorFrom: blue
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colorTo: indigo
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sdk: gradio
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sdk_version: 5.29.0
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app_file: app.py
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pinned: false
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---
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# PDF Q&A Dataset Generator
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A Gradio application that generates Q&A datasets from PDF documents using instruction-tuned language models.
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## Features
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- **PDF Processing**: Automatically extract and chunk text from uploaded PDFs
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- **Q&A Generation**: Create questions, answers, tags, and difficulty levels
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- **Multiple Models**: Choose from various instruction-tuned models
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- **Customization**: Configure number of questions, tags, and difficulty settings
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- **Multiple Output Formats**: Export datasets as JSON, CSV, or Excel
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## How It Works
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This application:
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1. Extracts text from uploaded PDFs
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2. Splits the content into manageable chunks to maintain context
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3. Uses instruction-tuned language models to generate Q&A pairs with tags
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4. Combines these into a comprehensive dataset ready for use
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## Use Cases
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- Creating educational resources and assessment materials
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- Generating training data for Q&A systems
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- Building flashcard datasets for studying
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- Developing content for educational applications
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- Preparing comprehension testing materials
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## Getting Started
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### Local Installation
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```bash
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git clone https://github.com/your-username/pdf-qa-generator.git
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cd pdf-qa-generator
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pip install -r requirements.txt
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python app.py
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```
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### Using on Hugging Face Spaces
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1. Duplicate this Space to your account
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2. Upload your PDFs
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3. Configure your settings
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4. Generate your Q&A dataset
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### Enabling GPU on Hugging Face Spaces
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To enable GPU acceleration on Hugging Face Spaces:
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1. Uncomment the `# import spaces` line at the top of app.py
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2. Uncomment the `# @spaces.GPU` decorator above the `process_pdf_generate_qa` function
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3. Save and redeploy your Space with GPU hardware selected
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## Models
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The app includes a selection of instruction-tuned language models:
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- `databricks/dolly-v2-3b` (default)
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- `databricks/dolly-v2-7b`
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- `EleutherAI/gpt-neo-1.3B`
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- `EleutherAI/gpt-neo-2.7B`
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- `tiiuae/falcon-7b-instruct`
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## License
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MIT
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app.py
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import os
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import json
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import pandas as pd
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import gradio as gr
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import spaces
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import csv
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import yaml
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from typing import List, Dict, Any
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import random
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from pypdf import PdfReader
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import re
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import tempfile
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from huggingface_hub import HfApi
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# Configuration
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DEFAULT_MODEL = "databricks/dolly-v2-3b" # Smaller, more suitable for Spaces
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" # Try to use CUDA if available
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MAX_NEW_TOKENS = 512
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TEMPERATURE = 0.7
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HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # Get token from environment variables
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MAX_RAM_GB = 45 # Set maximum RAM usage to 45GB (below the 70GB limit)
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# Create offload folder for model memory management
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os.makedirs("offload_folder", exist_ok=True)
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# Setup RAM monitoring
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def get_process_memory_usage():
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"""Get the current memory usage of this process in GB"""
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31 |
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import psutil
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process = psutil.Process(os.getpid())
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return process.memory_info().rss / (1024 * 1024 * 1024) # Convert to GB
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35 |
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class PdfExtractor:
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36 |
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"""Extract text content from PDF files"""
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37 |
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38 |
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@staticmethod
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39 |
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def extract_text_from_pdf(pdf_file):
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40 |
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"""Extract text from a PDF file"""
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41 |
+
try:
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42 |
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reader = PdfReader(pdf_file)
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43 |
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text = ""
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44 |
+
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45 |
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for page in reader.pages:
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46 |
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text += page.extract_text() + "\n"
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47 |
+
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48 |
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return text
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49 |
+
except Exception as e:
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50 |
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print(f"Error extracting text from PDF: {e}")
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51 |
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return None
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52 |
+
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53 |
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@staticmethod
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54 |
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def clean_text(text):
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55 |
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"""Clean and preprocess extracted text"""
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56 |
+
if not text:
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57 |
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return ""
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58 |
+
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59 |
+
# Replace multiple newlines with single newline
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60 |
+
text = re.sub(r'\n+', '\n', text)
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61 |
+
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62 |
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# Replace multiple spaces with single space
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63 |
+
text = re.sub(r'\s+', ' ', text)
|
64 |
+
|
65 |
+
return text.strip()
|
66 |
+
|
67 |
+
@staticmethod
|
68 |
+
def chunk_text(text, max_chunk_size=1000, overlap=100):
|
69 |
+
"""Split text into chunks of specified size with overlap"""
|
70 |
+
if not text:
|
71 |
+
return []
|
72 |
+
|
73 |
+
chunks = []
|
74 |
+
start = 0
|
75 |
+
text_length = len(text)
|
76 |
+
|
77 |
+
while start < text_length:
|
78 |
+
end = min(start + max_chunk_size, text_length)
|
79 |
+
|
80 |
+
# If we're not at the end, try to break at a sentence or paragraph
|
81 |
+
if end < text_length:
|
82 |
+
# Look for sentence breaks (period, question mark, exclamation mark followed by space)
|
83 |
+
sentence_break = max(
|
84 |
+
text.rfind('. ', start, end),
|
85 |
+
text.rfind('? ', start, end),
|
86 |
+
text.rfind('! ', start, end),
|
87 |
+
text.rfind('\n', start, end)
|
88 |
+
)
|
89 |
+
|
90 |
+
if sentence_break > start + max_chunk_size // 2:
|
91 |
+
end = sentence_break + 1
|
92 |
+
|
93 |
+
chunks.append(text[start:end].strip())
|
94 |
+
start = end - overlap # Create overlap with previous chunk
|
95 |
+
|
96 |
+
return chunks
|
97 |
+
|
98 |
+
class SyntheticDataGenerator:
|
99 |
+
def __init__(self, model_name=DEFAULT_MODEL):
|
100 |
+
self.model_name = model_name
|
101 |
+
self.model = None
|
102 |
+
self.tokenizer = None
|
103 |
+
self.fallback_models = [
|
104 |
+
"databricks/dolly-v2-3b", # Smallest, most reliable model as primary fallback
|
105 |
+
"EleutherAI/gpt-neo-1.3B", # Second fallback option
|
106 |
+
]
|
107 |
+
# Don't try to load the model in init - we'll load it when needed
|
108 |
+
# This prevents initialization errors from blocking the app startup
|
109 |
+
|
110 |
+
def load_model(self):
|
111 |
+
"""Load the specified model or fall back to a smaller model if loading fails"""
|
112 |
+
# Clear CUDA cache if using GPU to prevent memory fragmentation
|
113 |
+
if torch.cuda.is_available():
|
114 |
+
torch.cuda.empty_cache()
|
115 |
+
# Try initializing CUDA explicitly to catch early errors
|
116 |
+
try:
|
117 |
+
torch.tensor([1.0], device="cuda")
|
118 |
+
except Exception as e:
|
119 |
+
print(f"CUDA initialization error: {e}")
|
120 |
+
|
121 |
+
models_to_try = [self.model_name]
|
122 |
+
|
123 |
+
# Add fallback models only if the requested model isn't already in the fallback list
|
124 |
+
if self.model_name not in self.fallback_models:
|
125 |
+
models_to_try.extend(self.fallback_models)
|
126 |
+
|
127 |
+
for model_name in models_to_try:
|
128 |
+
try:
|
129 |
+
print(f"Loading model {model_name} on {DEVICE}...")
|
130 |
+
|
131 |
+
# Add token for authentication if available
|
132 |
+
tokenizer_kwargs = {}
|
133 |
+
model_kwargs = {
|
134 |
+
"torch_dtype": torch.float16 if torch.cuda.is_available() else torch.float32,
|
135 |
+
"device_map": "auto" if torch.cuda.is_available() else None,
|
136 |
+
"low_cpu_mem_usage": True, # Added to reduce memory usage on CPU
|
137 |
+
"offload_folder": "offload_folder" # Add offload folder for large models
|
138 |
+
}
|
139 |
+
|
140 |
+
# Handle potential CUDA out-of-memory errors with smaller settings
|
141 |
+
if torch.cuda.is_available():
|
142 |
+
try:
|
143 |
+
# Check available GPU memory
|
144 |
+
free_memory, total_memory = torch.cuda.mem_get_info()
|
145 |
+
free_memory_gb = free_memory / (1024**3)
|
146 |
+
total_memory_gb = total_memory / (1024**3)
|
147 |
+
print(f"GPU memory: {free_memory_gb:.2f}GB free out of {total_memory_gb:.2f}GB total")
|
148 |
+
|
149 |
+
# If we're running low on memory (this is common in Spaces), use more aggressive memory saving
|
150 |
+
model_kwargs["max_memory"] = {0: f"{max(free_memory_gb*0.8, 0.5)}GB", "cpu": "8GB"}
|
151 |
+
|
152 |
+
# For smaller GPUs or when memory is constrained, use more aggressive offloading
|
153 |
+
if free_memory_gb < 4.0: # Less than 4GB free
|
154 |
+
print("Low GPU memory detected. Using CPU offloading...")
|
155 |
+
# More conservative memory map to prevent ZeroGPU errors
|
156 |
+
model_kwargs["device_map"] = "auto"
|
157 |
+
model_kwargs["offload_state_dict"] = True # More aggressive offloading
|
158 |
+
except Exception as memory_check_error:
|
159 |
+
print(f"Error checking GPU memory: {memory_check_error}")
|
160 |
+
# Continue with default settings but with safeguards
|
161 |
+
model_kwargs["device_map"] = "auto" # Let the library decide the best mapping
|
162 |
+
|
163 |
+
# Add token for authentication if available and model is gated
|
164 |
+
if HF_TOKEN:
|
165 |
+
tokenizer_kwargs["token"] = HF_TOKEN
|
166 |
+
model_kwargs["token"] = HF_TOKEN
|
167 |
+
print("Using Hugging Face token for authentication")
|
168 |
+
|
169 |
+
# Load tokenizer with safeguards against ZeroGPU issues
|
170 |
+
try:
|
171 |
+
self.tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
|
172 |
+
except Exception as tokenizer_error:
|
173 |
+
print(f"Error loading tokenizer: {tokenizer_error}")
|
174 |
+
# Try loading with additional safety settings
|
175 |
+
tokenizer_kwargs["local_files_only"] = False
|
176 |
+
tokenizer_kwargs["revision"] = "main"
|
177 |
+
self.tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
|
178 |
+
|
179 |
+
# Load the model with ZeroGPU error prevention
|
180 |
+
try:
|
181 |
+
self.model = AutoModelForCausalLM.from_pretrained(
|
182 |
+
model_name,
|
183 |
+
**model_kwargs
|
184 |
+
)
|
185 |
+
except RuntimeError as e:
|
186 |
+
if "CUDA" in str(e) or "GPU" in str(e) or "out of memory" in str(e):
|
187 |
+
print(f"CUDA error loading model: {e}")
|
188 |
+
# Fall back to CPU if GPU fails
|
189 |
+
print("Falling back to CPU for model loading")
|
190 |
+
model_kwargs["device_map"] = {"": "cpu"}
|
191 |
+
model_kwargs["torch_dtype"] = torch.float32
|
192 |
+
self.model = AutoModelForCausalLM.from_pretrained(
|
193 |
+
model_name,
|
194 |
+
**model_kwargs
|
195 |
+
)
|
196 |
+
else:
|
197 |
+
raise
|
198 |
+
|
199 |
+
# Ensure model is on the right device if not using device_map="auto"
|
200 |
+
if not torch.cuda.is_available():
|
201 |
+
self.model = self.model.to(DEVICE)
|
202 |
+
|
203 |
+
# If we loaded a fallback model instead of the requested one, update the model_name
|
204 |
+
if model_name != self.model_name:
|
205 |
+
print(f"Successfully loaded fallback model {model_name} instead of {self.model_name}")
|
206 |
+
self.model_name = model_name
|
207 |
+
else:
|
208 |
+
print(f"Model loaded successfully on {DEVICE}")
|
209 |
+
|
210 |
+
return True
|
211 |
+
except Exception as e:
|
212 |
+
print(f"Error loading model {model_name}: {e}")
|
213 |
+
self.model = None
|
214 |
+
self.tokenizer = None
|
215 |
+
# Continue to the next model in the list
|
216 |
+
|
217 |
+
# If we get here, all models failed
|
218 |
+
print("All models failed to load")
|
219 |
+
return False
|
220 |
+
|
221 |
+
def generate_qa_prompt(self, context, num_questions=3, include_tags=True, difficulty_levels=True):
|
222 |
+
"""Generate a prompt for creating Q&A pairs from context."""
|
223 |
+
tag_instruction = ""
|
224 |
+
if include_tags:
|
225 |
+
tag_instruction = "Add 1-3 tags for each question that categorize the topic or subject matter."
|
226 |
+
|
227 |
+
difficulty_instruction = ""
|
228 |
+
if difficulty_levels:
|
229 |
+
difficulty_instruction = "For each question, assign a difficulty level (easy, medium, or hard)."
|
230 |
+
|
231 |
+
prompt = f"""Task: Based on the following text, generate {num_questions} question and answer pairs that would be useful for comprehension testing or knowledge assessment.
|
232 |
+
|
233 |
+
CONTEXT:
|
234 |
+
{context}
|
235 |
+
|
236 |
+
For each question:
|
237 |
+
1. Write a clear, specific question about the information in the text
|
238 |
+
2. Provide the correct answer to the question, citing relevant details from the text
|
239 |
+
3. {tag_instruction}
|
240 |
+
4. {difficulty_instruction}
|
241 |
+
|
242 |
+
Format each Q&A pair as a JSON object with the following structure:
|
243 |
+
{{
|
244 |
+
"question": "The question text",
|
245 |
+
"answer": "The answer text",
|
246 |
+
"tags": ["tag1", "tag2"],
|
247 |
+
"difficulty": "easy/medium/hard"
|
248 |
+
}}
|
249 |
+
|
250 |
+
Return all Q&A pairs in a JSON array.
|
251 |
+
"""
|
252 |
+
return prompt
|
253 |
+
|
254 |
+
def generate_data(self, prompt, num_samples=1):
|
255 |
+
"""Generate synthetic data using the loaded model."""
|
256 |
+
if not self.model or not self.tokenizer:
|
257 |
+
return ["Error: Model not loaded properly. Please try again with a different model."]
|
258 |
+
|
259 |
+
outputs = []
|
260 |
+
for sample_idx in range(num_samples):
|
261 |
+
try:
|
262 |
+
# Clear CUDA cache before generating to free up memory
|
263 |
+
if torch.cuda.is_available():
|
264 |
+
torch.cuda.empty_cache()
|
265 |
+
|
266 |
+
# ZeroGPU errors often occur in generate() calls
|
267 |
+
# To mitigate this, try multiple approaches in sequence
|
268 |
+
inputs = self.tokenizer(prompt, return_tensors="pt").to(DEVICE)
|
269 |
+
|
270 |
+
try:
|
271 |
+
# First try: Standard generation with conservative settings
|
272 |
+
with torch.no_grad():
|
273 |
+
output = self.model.generate(
|
274 |
+
**inputs,
|
275 |
+
max_new_tokens=MAX_NEW_TOKENS,
|
276 |
+
temperature=TEMPERATURE,
|
277 |
+
do_sample=True,
|
278 |
+
pad_token_id=self.tokenizer.eos_token_id,
|
279 |
+
num_beams=1, # Use greedy decoding instead of beam search
|
280 |
+
early_stopping=True,
|
281 |
+
no_repeat_ngram_size=3 # Prevent repetition
|
282 |
+
)
|
283 |
+
|
284 |
+
decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
|
285 |
+
except (RuntimeError, Exception) as e:
|
286 |
+
if "CUDA" in str(e) or "GPU" in str(e) or "ZeroGPU" in str(e):
|
287 |
+
print(f"GPU error during generation: {e}")
|
288 |
+
print("Falling back to CPU generation...")
|
289 |
+
|
290 |
+
# Move everything to CPU
|
291 |
+
inputs = {k: v.to('cpu') for k, v in inputs.items()}
|
292 |
+
|
293 |
+
# Create CPU copy of the model if we were using GPU
|
294 |
+
if torch.cuda.is_available():
|
295 |
+
# Temporarily move model to CPU for this generation
|
296 |
+
model_cpu = self.model.to('cpu')
|
297 |
+
|
298 |
+
with torch.no_grad():
|
299 |
+
output = model_cpu.generate(
|
300 |
+
**inputs,
|
301 |
+
max_new_tokens=MAX_NEW_TOKENS,
|
302 |
+
temperature=TEMPERATURE,
|
303 |
+
do_sample=True,
|
304 |
+
pad_token_id=self.tokenizer.eos_token_id,
|
305 |
+
num_return_sequences=1,
|
306 |
+
max_length=MAX_NEW_TOKENS + inputs['input_ids'].shape[1]
|
307 |
+
)
|
308 |
+
decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
|
309 |
+
|
310 |
+
# Move model back to CUDA for future calls
|
311 |
+
self.model = self.model.to(DEVICE)
|
312 |
+
else:
|
313 |
+
# Already on CPU, try with reduced parameters
|
314 |
+
with torch.no_grad():
|
315 |
+
output = self.model.generate(
|
316 |
+
**inputs,
|
317 |
+
max_new_tokens=min(256, MAX_NEW_TOKENS), # Reduce token count
|
318 |
+
temperature=0.5, # Lower temperature
|
319 |
+
do_sample=False, # No sampling
|
320 |
+
num_return_sequences=1,
|
321 |
+
pad_token_id=self.tokenizer.eos_token_id
|
322 |
+
)
|
323 |
+
decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
|
324 |
+
else:
|
325 |
+
# Re-raise non-CUDA errors
|
326 |
+
raise
|
327 |
+
|
328 |
+
# Extract only the generated part (remove prompt)
|
329 |
+
prompt_text = self.tokenizer.decode(inputs.input_ids[0], skip_special_tokens=True)
|
330 |
+
generated_text = decoded_output[len(prompt_text):].strip()
|
331 |
+
outputs.append(generated_text)
|
332 |
+
|
333 |
+
# Clear CUDA cache between samples
|
334 |
+
if torch.cuda.is_available():
|
335 |
+
torch.cuda.empty_cache()
|
336 |
+
|
337 |
+
except Exception as e:
|
338 |
+
error_msg = f"Error generating sample {sample_idx+1}: {str(e)}"
|
339 |
+
print(error_msg)
|
340 |
+
outputs.append(f"Error: {error_msg}")
|
341 |
+
|
342 |
+
return outputs
|
343 |
+
|
344 |
+
def parse_json_data(self, generated_text):
|
345 |
+
"""Extract and parse JSON from generated text."""
|
346 |
+
try:
|
347 |
+
# Find JSON-like content (between [ and ])
|
348 |
+
start_idx = generated_text.find('[')
|
349 |
+
end_idx = generated_text.rfind(']') + 1
|
350 |
+
|
351 |
+
if start_idx >= 0 and end_idx > start_idx:
|
352 |
+
json_str = generated_text[start_idx:end_idx]
|
353 |
+
return json.loads(json_str)
|
354 |
+
|
355 |
+
# Try to find single object format
|
356 |
+
start_idx = generated_text.find('{')
|
357 |
+
end_idx = generated_text.rfind('}') + 1
|
358 |
+
|
359 |
+
if start_idx >= 0 and end_idx > start_idx:
|
360 |
+
json_str = generated_text[start_idx:end_idx]
|
361 |
+
return json.loads(json_str)
|
362 |
+
|
363 |
+
print(f"Could not find JSON content in: {generated_text}")
|
364 |
+
return None
|
365 |
+
except json.JSONDecodeError as e:
|
366 |
+
print(f"JSON parse error: {e}")
|
367 |
+
print(f"Problematic text: {generated_text}")
|
368 |
+
|
369 |
+
# Try to find and fix common JSON formatting errors
|
370 |
+
try:
|
371 |
+
# Replace single quotes with double quotes
|
372 |
+
json_str = generated_text[start_idx:end_idx].replace("'", "\"")
|
373 |
+
return json.loads(json_str)
|
374 |
+
except:
|
375 |
+
pass
|
376 |
+
|
377 |
+
# If still failing, try to extract individual JSON objects
|
378 |
+
try:
|
379 |
+
pattern = r'\{[^{}]*\}'
|
380 |
+
matches = re.findall(pattern, generated_text)
|
381 |
+
if matches:
|
382 |
+
results = []
|
383 |
+
for match in matches:
|
384 |
+
try:
|
385 |
+
# Replace single quotes with double quotes
|
386 |
+
fixed_match = match.replace("'", "\"")
|
387 |
+
obj = json.loads(fixed_match)
|
388 |
+
results.append(obj)
|
389 |
+
except:
|
390 |
+
continue
|
391 |
+
if results:
|
392 |
+
return results
|
393 |
+
except:
|
394 |
+
pass
|
395 |
+
|
396 |
+
return None
|
397 |
+
|
398 |
+
def generate_qa_from_pdf_chunk(self, chunk, num_questions=3, include_tags=True, difficulty_levels=True):
|
399 |
+
"""Generate Q&A pairs from a PDF text chunk."""
|
400 |
+
if not self.model or not self.tokenizer:
|
401 |
+
return [], "Error: Model not loaded properly. Please try again with a different model."
|
402 |
+
|
403 |
+
if not chunk or len(chunk.strip()) < 100: # Skip very small chunks
|
404 |
+
return [], "Chunk too small to generate meaningful Q&A pairs."
|
405 |
+
|
406 |
+
prompt = self.generate_qa_prompt(chunk, num_questions, include_tags, difficulty_levels)
|
407 |
+
raw_outputs = self.generate_data(prompt, num_samples=1)
|
408 |
+
raw_output = raw_outputs[0]
|
409 |
+
|
410 |
+
parsed_data = self.parse_json_data(raw_output)
|
411 |
+
|
412 |
+
# Ensure parsed data is a list
|
413 |
+
if parsed_data and isinstance(parsed_data, dict):
|
414 |
+
parsed_data = [parsed_data]
|
415 |
+
|
416 |
+
# Return both the parsed data and raw output for debugging
|
417 |
+
return parsed_data, raw_output
|
418 |
+
|
419 |
+
def format_data_preview(data):
|
420 |
+
"""Format the data for preview in the UI."""
|
421 |
+
if isinstance(data, list):
|
422 |
+
if len(data) > 0 and isinstance(data[0], dict):
|
423 |
+
# Convert list of dicts to DataFrame for better display
|
424 |
+
return pd.DataFrame(data).to_string()
|
425 |
+
else:
|
426 |
+
return json.dumps(data, indent=2)
|
427 |
+
elif isinstance(data, dict):
|
428 |
+
return json.dumps(data, indent=2)
|
429 |
+
else:
|
430 |
+
return str(data)
|
431 |
+
|
432 |
+
def save_data(data, format, filename_prefix):
|
433 |
+
"""Save data to a file in the specified format."""
|
434 |
+
os.makedirs("synthetic_data", exist_ok=True)
|
435 |
+
timestamp = pd.Timestamp.now().strftime("%Y%m%d_%H%M%S")
|
436 |
+
filename = f"synthetic_data/{filename_prefix}_{timestamp}"
|
437 |
+
|
438 |
+
if isinstance(data, list) and len(data) > 0 and isinstance(data[0], dict):
|
439 |
+
df = pd.DataFrame(data)
|
440 |
+
|
441 |
+
if format.lower() == "csv":
|
442 |
+
full_filename = f"{filename}.csv"
|
443 |
+
df.to_csv(full_filename, index=False)
|
444 |
+
elif format.lower() == "json":
|
445 |
+
full_filename = f"{filename}.json"
|
446 |
+
with open(full_filename, "w") as f:
|
447 |
+
json.dump(data, f, indent=2)
|
448 |
+
elif format.lower() == "excel":
|
449 |
+
full_filename = f"{filename}.xlsx"
|
450 |
+
df.to_excel(full_filename, index=False)
|
451 |
+
else:
|
452 |
+
full_filename = f"{filename}.txt"
|
453 |
+
with open(full_filename, "w") as f:
|
454 |
+
f.write(str(data))
|
455 |
+
else:
|
456 |
+
full_filename = f"{filename}.{format.lower()}"
|
457 |
+
with open(full_filename, "w") as f:
|
458 |
+
if format.lower() == "json":
|
459 |
+
json.dump(data, f, indent=2)
|
460 |
+
else:
|
461 |
+
f.write(str(data))
|
462 |
+
|
463 |
+
return full_filename
|
464 |
+
|
465 |
+
def load_models():
|
466 |
+
"""Return a list of available models."""
|
467 |
+
return [
|
468 |
+
"databricks/dolly-v2-3b",
|
469 |
+
"databricks/dolly-v2-7b",
|
470 |
+
"EleutherAI/gpt-neo-1.3B",
|
471 |
+
"EleutherAI/gpt-neo-2.7B",
|
472 |
+
"tiiuae/falcon-7b-instruct"
|
473 |
+
]
|
474 |
+
|
475 |
+
@spaces.GPU
|
476 |
+
def process_pdf_generate_qa(pdf_file, model_name, num_questions_per_chunk, include_tags, include_difficulty, output_file_format, progress=None):
|
477 |
+
"""Process a PDF file and generate Q&A pairs from its content."""
|
478 |
+
if pdf_file is None:
|
479 |
+
return None, "Error: No PDF file uploaded", "", "No file provided"
|
480 |
+
|
481 |
+
try:
|
482 |
+
# Check RAM usage at start
|
483 |
+
current_ram_usage = get_process_memory_usage()
|
484 |
+
print(f"Starting RAM usage: {current_ram_usage:.2f}GB")
|
485 |
+
|
486 |
+
# Clear CUDA cache before starting
|
487 |
+
if torch.cuda.is_available():
|
488 |
+
torch.cuda.empty_cache()
|
489 |
+
|
490 |
+
# Initialize extractor and generator
|
491 |
+
extractor = PdfExtractor()
|
492 |
+
generator = SyntheticDataGenerator(model_name)
|
493 |
+
|
494 |
+
# Wrap model loading in try-except to handle errors
|
495 |
+
try:
|
496 |
+
load_success = generator.load_model()
|
497 |
+
if not load_success:
|
498 |
+
return None, "Error: Failed to load the model. Please try again with a different model.", "", "Model loading failed"
|
499 |
+
except Exception as e:
|
500 |
+
if "ZeroGPU" in str(e) or "GPU task aborted" in str(e) or "CUDA" in str(e):
|
501 |
+
print(f"GPU error during model loading: {e}. Trying with a smaller model...")
|
502 |
+
# If we get a ZeroGPU error, immediately try the smallest model
|
503 |
+
generator.model_name = "EleutherAI/gpt-neo-1.3B" # Use smallest model as emergency fallback
|
504 |
+
load_success = generator.load_model()
|
505 |
+
if not load_success:
|
506 |
+
return None, "Error: Failed to load any model even after fallback. Please try again later.", "", "Model loading failed"
|
507 |
+
else:
|
508 |
+
# Re-raise other errors
|
509 |
+
raise
|
510 |
+
|
511 |
+
# Check RAM usage after model loading
|
512 |
+
ram_after_model = get_process_memory_usage()
|
513 |
+
print(f"RAM usage after model loading: {ram_after_model:.2f}GB")
|
514 |
+
|
515 |
+
# Save PDF temporarily if it's a file object
|
516 |
+
if hasattr(pdf_file, 'name'):
|
517 |
+
# It's already a file path
|
518 |
+
pdf_path = pdf_file.name
|
519 |
+
else:
|
520 |
+
# Create a temporary file
|
521 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp:
|
522 |
+
tmp.write(pdf_file)
|
523 |
+
pdf_path = tmp.name
|
524 |
+
|
525 |
+
# Extract text from PDF
|
526 |
+
pdf_text = extractor.extract_text_from_pdf(pdf_path)
|
527 |
+
|
528 |
+
if not pdf_text:
|
529 |
+
return None, "Failed to extract text from PDF", "", "No data generated"
|
530 |
+
|
531 |
+
# Clean and chunk the text - reduce chunk size to use less memory
|
532 |
+
cleaned_text = extractor.clean_text(pdf_text)
|
533 |
+
chunks = extractor.chunk_text(cleaned_text, max_chunk_size=400, overlap=30)
|
534 |
+
|
535 |
+
# Check RAM after PDF processing
|
536 |
+
ram_after_pdf = get_process_memory_usage()
|
537 |
+
print(f"RAM usage after PDF processing: {ram_after_pdf:.2f}GB, found {len(chunks)} chunks")
|
538 |
+
|
539 |
+
# If we're approaching the RAM limit already, reduce batch size
|
540 |
+
batch_size = 3 # Default
|
541 |
+
if ram_after_pdf > MAX_RAM_GB * 0.7: # If already using 70% of our limit
|
542 |
+
batch_size = 1 # Process one chunk at a time
|
543 |
+
print(f"High RAM usage detected ({ram_after_pdf:.2f}GB), reducing batch size to 1")
|
544 |
+
elif ram_after_pdf > MAX_RAM_GB * 0.5: # If using 50% of our limit
|
545 |
+
batch_size = 2 # Process two chunks at a time
|
546 |
+
print(f"Moderate RAM usage detected ({ram_after_pdf:.2f}GB), reducing batch size to 2")
|
547 |
+
|
548 |
+
# Generate Q&A pairs for each chunk
|
549 |
+
all_qa_pairs = []
|
550 |
+
all_raw_outputs = []
|
551 |
+
|
552 |
+
total_chunks = len(chunks)
|
553 |
+
|
554 |
+
# Process chunks in smaller batches to avoid memory buildup
|
555 |
+
for i in range(0, total_chunks, batch_size):
|
556 |
+
# Get the current batch of chunks
|
557 |
+
batch_chunks = chunks[i:min(i+batch_size, total_chunks)]
|
558 |
+
|
559 |
+
# Process each chunk in the batch
|
560 |
+
for j, chunk in enumerate(batch_chunks):
|
561 |
+
chunk_index = i + j
|
562 |
+
|
563 |
+
if progress is not None:
|
564 |
+
progress(chunk_index / total_chunks, f"Processing chunk {chunk_index+1}/{total_chunks}")
|
565 |
+
|
566 |
+
# Check if we're approaching RAM limit
|
567 |
+
current_ram = get_process_memory_usage()
|
568 |
+
if current_ram > MAX_RAM_GB * 0.9: # Over 90% of our limit
|
569 |
+
print(f"WARNING: High RAM usage detected: {current_ram:.2f}GB - force releasing memory")
|
570 |
+
import gc
|
571 |
+
gc.collect() # Force garbage collection
|
572 |
+
if torch.cuda.is_available():
|
573 |
+
torch.cuda.empty_cache()
|
574 |
+
|
575 |
+
# If still too high after garbage collection, abort batch processing
|
576 |
+
current_ram = get_process_memory_usage()
|
577 |
+
if current_ram > MAX_RAM_GB * 0.95: # Still dangerously high
|
578 |
+
print(f"CRITICAL: RAM usage too high ({current_ram:.2f}GB), stopping processing")
|
579 |
+
break
|
580 |
+
|
581 |
+
# Clear CUDA cache between chunks
|
582 |
+
if torch.cuda.is_available():
|
583 |
+
torch.cuda.empty_cache()
|
584 |
+
|
585 |
+
try:
|
586 |
+
qa_pairs, raw_output = generator.generate_qa_from_pdf_chunk(
|
587 |
+
chunk,
|
588 |
+
num_questions=num_questions_per_chunk,
|
589 |
+
include_tags=include_tags,
|
590 |
+
difficulty_levels=include_difficulty
|
591 |
+
)
|
592 |
+
except Exception as e:
|
593 |
+
error_type = str(e)
|
594 |
+
if "CUDA" in error_type or "GPU" in error_type or "ZeroGPU" in error_type:
|
595 |
+
print(f"GPU error during generation for chunk {chunk_index+1}: {e}")
|
596 |
+
# Fall back to CPU for this specific generation
|
597 |
+
raw_output = f"Error in chunk {chunk_index+1}: {str(e)}. Skipping..."
|
598 |
+
qa_pairs = None
|
599 |
+
elif "memory" in error_type.lower() or "ram" in error_type.lower():
|
600 |
+
print(f"Memory error processing chunk {chunk_index+1}: {e}")
|
601 |
+
# Force garbage collection and skip chunk
|
602 |
+
import gc
|
603 |
+
gc.collect()
|
604 |
+
if torch.cuda.is_available():
|
605 |
+
torch.cuda.empty_cache()
|
606 |
+
raw_output = f"Memory error in chunk {chunk_index+1}: {str(e)}. Skipping..."
|
607 |
+
qa_pairs = None
|
608 |
+
else:
|
609 |
+
# For other errors, just log and continue
|
610 |
+
print(f"Error processing chunk {chunk_index+1}: {e}")
|
611 |
+
raw_output = f"Error in chunk {chunk_index+1}: {str(e)}"
|
612 |
+
qa_pairs = None
|
613 |
+
|
614 |
+
if qa_pairs:
|
615 |
+
all_qa_pairs.extend(qa_pairs)
|
616 |
+
all_raw_outputs.append(raw_output)
|
617 |
+
|
618 |
+
# Check RAM usage after processing this chunk
|
619 |
+
current_ram = get_process_memory_usage()
|
620 |
+
print(f"RAM after chunk {chunk_index+1}: {current_ram:.2f}GB")
|
621 |
+
|
622 |
+
# Do a thorough cleanup after each batch
|
623 |
+
if torch.cuda.is_available():
|
624 |
+
torch.cuda.empty_cache()
|
625 |
+
|
626 |
+
# Force garbage collection between batches
|
627 |
+
import gc
|
628 |
+
gc.collect()
|
629 |
+
|
630 |
+
# Check if we need to abort due to memory constraints
|
631 |
+
current_ram = get_process_memory_usage()
|
632 |
+
if current_ram > MAX_RAM_GB:
|
633 |
+
print(f"WARNING: Exceeding RAM limit ({current_ram:.2f}GB). Stopping further processing.")
|
634 |
+
if progress is not None:
|
635 |
+
progress(1.0, f"Stopped early due to high memory usage ({current_ram:.2f}GB)")
|
636 |
+
break
|
637 |
+
|
638 |
+
if progress is not None:
|
639 |
+
progress(1.0, "Finished processing")
|
640 |
+
|
641 |
+
# Final cache clear and garbage collection
|
642 |
+
if torch.cuda.is_available():
|
643 |
+
torch.cuda.empty_cache()
|
644 |
+
import gc
|
645 |
+
gc.collect()
|
646 |
+
|
647 |
+
if not all_qa_pairs:
|
648 |
+
return None, "Failed to generate Q&A pairs", "\n\n".join(all_raw_outputs), "No data generated"
|
649 |
+
|
650 |
+
# Save data to file
|
651 |
+
filename = save_data(
|
652 |
+
all_qa_pairs,
|
653 |
+
output_file_format,
|
654 |
+
"qa_dataset"
|
655 |
+
)
|
656 |
+
|
657 |
+
# Format for display
|
658 |
+
formatted_data = format_data_preview(all_qa_pairs)
|
659 |
+
|
660 |
+
# Final memory report
|
661 |
+
final_ram = get_process_memory_usage()
|
662 |
+
print(f"Final RAM usage: {final_ram:.2f}GB")
|
663 |
+
|
664 |
+
return all_qa_pairs, formatted_data, "\n\n".join(all_raw_outputs), f"Data saved to {filename}"
|
665 |
+
except Exception as e:
|
666 |
+
error_msg = f"Error processing PDF: {str(e)}"
|
667 |
+
print(error_msg)
|
668 |
+
import traceback
|
669 |
+
print(traceback.format_exc())
|
670 |
+
return None, error_msg, "", "Processing failed"
|
671 |
+
|
672 |
+
# Set up the Gradio interface
|
673 |
+
def create_interface():
|
674 |
+
with gr.Blocks(title="PDF Q&A Dataset Generator") as app:
|
675 |
+
gr.Markdown("# π PDF Q&A Dataset Generator")
|
676 |
+
gr.Markdown("""
|
677 |
+
Generate question & answer datasets from PDF documents using instruction-tuned language models.
|
678 |
+
Perfect for creating educational resources, quiz materials, or training data for Q&A systems.
|
679 |
+
""")
|
680 |
+
|
681 |
+
with gr.Tabs() as tabs:
|
682 |
+
with gr.TabItem("Generate Q&A Dataset"):
|
683 |
+
with gr.Row():
|
684 |
+
with gr.Column(scale=1):
|
685 |
+
pdf_file = gr.File(
|
686 |
+
label="Upload PDF",
|
687 |
+
file_types=[".pdf"],
|
688 |
+
type="binary"
|
689 |
+
)
|
690 |
+
|
691 |
+
model_dropdown = gr.Dropdown(
|
692 |
+
choices=load_models(),
|
693 |
+
value=DEFAULT_MODEL,
|
694 |
+
label="Model"
|
695 |
+
)
|
696 |
+
|
697 |
+
num_questions = gr.Slider(
|
698 |
+
minimum=1,
|
699 |
+
maximum=5,
|
700 |
+
value=3,
|
701 |
+
step=1,
|
702 |
+
label="Questions per Section"
|
703 |
+
)
|
704 |
+
|
705 |
+
include_tags = gr.Checkbox(
|
706 |
+
value=True,
|
707 |
+
label="Include Tags"
|
708 |
+
)
|
709 |
+
|
710 |
+
include_difficulty = gr.Checkbox(
|
711 |
+
value=True,
|
712 |
+
label="Include Difficulty Levels"
|
713 |
+
)
|
714 |
+
|
715 |
+
output_file_format = gr.Radio(
|
716 |
+
choices=["json", "csv", "excel"],
|
717 |
+
value="json",
|
718 |
+
label="Save File Format"
|
719 |
+
)
|
720 |
+
|
721 |
+
generate_btn = gr.Button("Generate Q&A Dataset", variant="primary")
|
722 |
+
|
723 |
+
progress_bar = gr.Progress()
|
724 |
+
|
725 |
+
with gr.Column(scale=2):
|
726 |
+
with gr.Tab("Parsed Data"):
|
727 |
+
parsed_data_output = gr.JSON(label="Generated Q&A Pairs")
|
728 |
+
formatted_data_output = gr.Textbox(
|
729 |
+
label="Formatted Preview",
|
730 |
+
lines=15
|
731 |
+
)
|
732 |
+
|
733 |
+
with gr.Tab("Raw Output"):
|
734 |
+
raw_output = gr.Textbox(
|
735 |
+
label="Raw Model Output",
|
736 |
+
lines=15
|
737 |
+
)
|
738 |
+
|
739 |
+
file_output = gr.Textbox(label="File Output")
|
740 |
+
|
741 |
+
with gr.TabItem("Documentation"):
|
742 |
+
gr.Markdown("""
|
743 |
+
## How to Use
|
744 |
+
|
745 |
+
1. **Upload a PDF**: Select a PDF document containing the content you want to generate questions from.
|
746 |
+
2. **Select a model**: Choose an instruction-tuned language model from the dropdown.
|
747 |
+
3. **Configure settings**:
|
748 |
+
- Set the number of questions to generate per text section
|
749 |
+
- Choose whether to include tags and difficulty levels
|
750 |
+
- Select your preferred output file format
|
751 |
+
4. **Generate dataset**: Click the "Generate Q&A Dataset" button to create your dataset.
|
752 |
+
|
753 |
+
## About This App
|
754 |
+
|
755 |
+
This app uses instruction-tuned language models to generate question and answer pairs from PDF documents. It:
|
756 |
+
|
757 |
+
1. Extracts text from the uploaded PDF
|
758 |
+
2. Splits the text into manageable chunks
|
759 |
+
3. Generates questions, answers, tags, and difficulty levels for each chunk
|
760 |
+
4. Combines all Q&A pairs into a comprehensive dataset
|
761 |
+
|
762 |
+
### Features:
|
763 |
+
- Automatic text extraction from PDFs
|
764 |
+
- Smart text chunking to maintain context
|
765 |
+
- Customizable number of questions per chunk
|
766 |
+
- Optional tagging and difficulty classification
|
767 |
+
- Multiple output formats (JSON, CSV, Excel)
|
768 |
+
|
769 |
+
### Use Cases:
|
770 |
+
- Create educational resources and quiz materials
|
771 |
+
- Generate training data for Q&A systems
|
772 |
+
- Build flashcard datasets for studying
|
773 |
+
- Develop content for educational applications
|
774 |
+
""")
|
775 |
+
|
776 |
+
with gr.TabItem("Status"):
|
777 |
+
gr.Markdown("""
|
778 |
+
## System Status
|
779 |
+
|
780 |
+
This app runs on CPU mode. Some larger models might be slower to load and generate content.
|
781 |
+
If you encounter any issues with a specific model, try switching to a smaller model like `databricks/dolly-v2-3b`.
|
782 |
+
|
783 |
+
### Troubleshooting
|
784 |
+
|
785 |
+
- If the app seems unresponsive after clicking "Generate", please be patient - model loading may take time.
|
786 |
+
- If you get an error about model loading, try refreshing the page and selecting a different model.
|
787 |
+
- Not all PDFs can be properly processed - if text extraction fails, try with a different PDF.
|
788 |
+
""")
|
789 |
+
|
790 |
+
# Event handler for generate button
|
791 |
+
generate_btn.click(
|
792 |
+
process_pdf_generate_qa,
|
793 |
+
inputs=[
|
794 |
+
pdf_file,
|
795 |
+
model_dropdown,
|
796 |
+
num_questions,
|
797 |
+
include_tags,
|
798 |
+
include_difficulty,
|
799 |
+
output_file_format
|
800 |
+
],
|
801 |
+
outputs=[parsed_data_output, formatted_data_output, raw_output, file_output],
|
802 |
+
show_progress=True
|
803 |
+
)
|
804 |
+
|
805 |
+
return app
|
806 |
+
|
807 |
+
# Export the app for Hugging Face Spaces
|
808 |
+
app = create_interface()
|
809 |
+
|
810 |
+
# Launch the app depending on the environment
|
811 |
+
if __name__ == "__main__":
|
812 |
+
app.launch()
|
813 |
+
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gradio==5.29.0
|
2 |
+
torch>=2.0.0
|
3 |
+
transformers>=4.30.0
|
4 |
+
pandas>=1.5.0
|
5 |
+
PyYAML>=6.0
|
6 |
+
openpyxl>=3.1.0
|
7 |
+
pypdf>=3.9.0
|
8 |
+
huggingface-hub>=0.20.0
|
9 |
+
accelerate>=0.25.0
|