Spaces:
Sleeping
Sleeping
File size: 3,531 Bytes
f552345 33e502d f552345 33e502d f552345 33e502d f552345 33e502d |
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 |
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr
import re
model_path = "prajjwal888/Llama-2-7b-chat-question-generation"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
def parse_generated_text(text: str) -> dict:
clean_text = re.sub(r"\[/?INST\]", "", text)
clean_text = re.sub(r"Question:\s*Question:", "Question:", clean_text)
clean_text = clean_text.strip()
match = re.search(r"Question:\s*(.*?)(?:\nHint:|Hint:)(.*)", clean_text, re.DOTALL)
if match:
question = match.group(1).strip().strip('"').replace("Question:", "").strip()
hint = match.group(2).strip().strip('"')
else:
question = clean_text.strip()
hint = "No hint available"
return {
"question": question,
"hint": hint
}
def generate_questions(topic, difficulty, types, count):
print("Received input:", topic, difficulty, types, count)
try:
pipe = pipeline(
task="text-generation",
model=model,
tokenizer=tokenizer,
max_length=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
questions = []
for _ in range(count):
for q_type in types:
prompt = (
f"Generate a {difficulty} difficulty {q_type} question about {topic}.\n"
"Format strictly as follows:\n"
"Question: <your question here>\n"
"Hint: <your hint here or 'No hint available'>"
)
formatted_prompt = f"<s>[INST] {prompt} [/INST]"
print("Prompt:", formatted_prompt)
result = pipe(formatted_prompt)
print("Raw Output:", result)
generated_text = result[0]['generated_text'].replace(formatted_prompt, "").strip()
parsed = parse_generated_text(generated_text)
print("Parsed Output:", parsed)
# Safe fallback
if not parsed['question']:
parsed['question'] = "⚠️ Could not parse question."
if not parsed['hint']:
parsed['hint'] = "No hint available"
formatted = f"**Type**: {q_type}\n\n**Question**: {parsed['question']}\n\n**Hint**: {parsed['hint']}\n\n---"
questions.append(formatted)
final_output = "\n\n".join(questions)
print("Final Output:", final_output)
return final_output
except Exception as e:
print("Error:", e)
return f"❌ Something went wrong: {e}"
except Exception as e:
print("Error:", e)
return f"Something went wrong: {e}"
iface = gr.Interface(
fn=generate_questions,
inputs=[
gr.Textbox(label="Topic"),
gr.Dropdown(choices=["easy", "medium", "hard"], label="Difficulty", value="medium"),
gr.CheckboxGroup(choices=["Conceptual", "Numerical", "Application"], label="Question Types"),
gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Number of Questions per Type")
],
outputs=gr.Markdown(label="Generated Questions"),
title="AI Question Generator",
description="Enter a topic, select difficulty and question types to generate AI-powered questions."
)
if __name__ == "__main__":
iface.queue().launch()
|