Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+import pandas as pd
+from transformers import pipeline
+
+# Load the Hugging Face model for text generation (Bloom or any other open-source model)
+@st.cache_resource
+def load_text_generator():
+    return pipeline("text-generation", model="bigscience/bloom-560m")  # Smaller version of Bloom for free use
+
+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):
+    topic_content = [line for line in content.split("\n") if topic.lower() in line.lower()]
+    return "\n".join(topic_content)
+
+# Function to generate study material using Hugging Face model
+def generate_content(topic):
+    prompt = f"Explain the topic '{topic}' in simple terms 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., DC Motors, Transformers)")
+
+# 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}")
+        filtered_content = search_topic_in_content(content, topic) if content else ""
+        if filtered_content:
+            st.write("**Relevant Extracted Content:**")
+            st.write(filtered_content)
+
+        ai_content = generate_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!")