Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| import pandas as pd | |
| from transformers import pipeline | |
| import random | |
| # Load the Hugging Face model for text generation and summarization (FLAN-T5 or T5-Small) | |
| def load_text_generator(): | |
| return pipeline("text2text-generation", model="google/flan-t5-base") # Efficient and professional model | |
| text_generator = load_text_generator() | |
| # Function to extract text from a PDF file | |
| def extract_pdf_content(pdf_file): | |
| reader = PdfReader(pdf_file) | |
| content = "" | |
| for page in reader.pages: | |
| content += page.extract_text() | |
| return content | |
| # Function to extract content from a text file | |
| def extract_text_file(file): | |
| return file.read().decode("utf-8") | |
| # Function to load a CSV file | |
| def read_csv_file(file): | |
| df = pd.read_csv(file) | |
| return df.to_string() | |
| # Function to search for a topic in the extracted content | |
| def search_topic_in_content(content, topic): | |
| sentences = content.split(".") # Break content into sentences | |
| topic_sentences = [s for s in sentences if topic.lower() in s.lower()] # Filter sentences containing the topic | |
| return ". ".join(topic_sentences) if topic_sentences else None | |
| # Function to generate structured content using Hugging Face model | |
| def generate_professional_content(topic): | |
| prompt = f"Explain '{topic}' in bullet points, highlighting the key concepts, examples, and applications in a professional manner for electrical engineering students." | |
| response = text_generator(prompt, max_length=300, num_return_sequences=1) | |
| return response[0]['generated_text'] | |
| # Function to generate a quiz question | |
| def generate_quiz(topic): | |
| questions = [ | |
| f"What is the fundamental principle of {topic}?", | |
| f"Name a practical application of {topic}.", | |
| f"What are the key equations associated with {topic}?", | |
| f"Describe how {topic} is used in real-world scenarios.", | |
| f"List common problems and solutions related to {topic}.", | |
| ] | |
| return random.choice(questions) | |
| # Streamlit App | |
| st.title("Generative AI for Electrical Engineering Education") | |
| st.sidebar.header("AI-Based Tutor") | |
| # File upload section | |
| uploaded_file = st.sidebar.file_uploader("Upload Study Material (PDF/TXT/CSV)", type=["pdf", "txt", "csv"]) | |
| topic = st.sidebar.text_input("Enter a topic (e.g., Newton's Third Law, DC Motors)") | |
| # Process uploaded file | |
| content = "" | |
| if uploaded_file: | |
| file_type = uploaded_file.name.split(".")[-1] | |
| if file_type == "pdf": | |
| content = extract_pdf_content(uploaded_file) | |
| elif file_type == "txt": | |
| content = extract_text_file(uploaded_file) | |
| elif file_type == "csv": | |
| content = read_csv_file(uploaded_file) | |
| st.sidebar.success(f"{uploaded_file.name} uploaded successfully!") | |
| st.write("**Extracted Content from File:**") | |
| st.write(content[:1000] + "...") # Display a snippet of the content | |
| # Generate study material | |
| if st.button("Generate Study Material"): | |
| if topic: | |
| st.header(f"Study Material: {topic}") | |
| # Extract relevant content from the uploaded material | |
| filtered_content = search_topic_in_content(content, topic) if content else "" | |
| if filtered_content: | |
| st.write("**Relevant Extracted Content from Uploaded Material:**") | |
| st.write(filtered_content) | |
| else: | |
| st.warning("No relevant content found in the uploaded material. Generating AI-based content instead.") | |
| ai_content = generate_professional_content(topic) | |
| st.write("**AI-Generated Content:**") | |
| st.write(ai_content) | |
| else: | |
| st.warning("Please enter a topic!") | |
| # Generate quiz | |
| if st.button("Generate Quiz"): | |
| if topic: | |
| st.header("Quiz Question") | |
| question = generate_quiz(topic) | |
| st.write(question) | |
| else: | |
| st.warning("Please enter a topic!") | |