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# modules/hf_llm_generator.py
import os
import requests
import json
import streamlit as st
import time
API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large" # Example model, can be changed
def query_huggingface_model(payload, hf_api_key: str):
"""Sends a query to the Hugging Face Inference API."""
if not hf_api_key:
return {"error": "Hugging Face API key not provided."}
headers = {
"Authorization": f"Bearer {hf_api_key}",
"Content-Type": "application/json"
}
response = requests.post(API_URL, headers=headers, json=payload)
response.raise_for_status() # Raise an exception for HTTP errors
return response.json()
def generate_mcq_with_llm(topic: str, difficulty: str, hf_api_key: str) -> dict:
"""
Generates a single MCQ question, options, correct answer, and explanation using an LLM.
"""
if not hf_api_key:
return None # Indicate failure if API key is missing
prompt = f"""
Generate a multiple-choice question about "{topic}" at a "{difficulty}" difficulty level.
Provide 4 options, indicate the correct answer, and give a brief explanation.
Format the output as a JSON object with the following keys:
"question": "...",
"options": ["...", "...", "...", "..."],
"correct_answer": "...",
"explanation": "..."
"""
payload = {
"inputs": prompt,
"parameters": {
"max_new_tokens": 250,
"temperature": 0.7,
"do_sample": True
}
}
try:
response = query_huggingface_model(payload, hf_api_key)
if "error" in response: # Handle API key not found or other API errors
st.error(f"Hugging Face API error: {response['error']}")
return None
# Hugging Face API responses can vary. We need to parse the text output.
# Assuming the model returns a string that needs to be parsed as JSON.
generated_text = response[0]['generated_text']
# Attempt to parse the generated text as JSON
mcq_data = json.loads(generated_text)
# Basic validation
if all(k in mcq_data for k in ["question", "options", "correct_answer", "explanation"]):
return {
"question": mcq_data["question"],
"options": mcq_data["options"],
"correct_answer": mcq_data["correct_answer"],
"explanation": mcq_data["explanation"],
"topic": topic,
"difficulty": difficulty,
}
else:
st.warning("LLM generated incomplete or malformed JSON. Retrying...")
return None # Indicate failure
except json.JSONDecodeError:
st.error(f"Failed to decode JSON from LLM response: {generated_text}")
return None
except requests.exceptions.RequestException as e:
st.error(f"Hugging Face API request failed: {e}")
return None
except Exception as e:
st.error(f"An unexpected error occurred during LLM question generation: {e}")
return None
def generate_quiz_set_with_llm(topic: str, difficulty: str, num_questions: int = 5, hf_api_key: str = None) -> list:
"""
Generates a set of MCQ questions using an LLM.
"""
if not hf_api_key:
st.error("Hugging Face API key not found. Please set the HF_API_KEY environment variable.")
return []
quiz_set = []
for i in range(num_questions):
mcq = None
retries = 3
while mcq is None and retries > 0:
mcq = generate_mcq_with_llm(topic, difficulty, hf_api_key)
if mcq is None:
retries -= 1
time.sleep(1) # Wait before retrying
if mcq:
quiz_set.append(mcq)
else:
st.warning(f"Could not generate question {i+1} after retries for topic '{topic}'.")
break
return quiz_set |