app.py

import gradio as gr
from huggingface_hub import InferenceClient
import torch
from sentence_transformers import SentenceTransformer

#hello -> this is irede

client = InferenceClient("microsoft/phi-4")

#Loading the bio spec txt file

with open("bio_spec.txt", "r", encoding="utf-8", errors="replace") as f:
    bio_spec_text = f.read()

#process file function
def preprocess_text(text):
    cleaned_text = text.strip()
    chunks = cleaned_text.split("\n")
    cleaned_chunks = []

    for chunk in chunks:
        chunk = chunk.strip()
        if chunk != "":
            cleaned_chunks.append(chunk)
    return cleaned_chunks

#Splitting the file
bio_chunks = preprocess_text(bio_spec_text)

#Loading sentance transformer model and then embedding the chunks (idrk it was on colab)
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")

chunk_embeddings = embedding_model.encode(bio_chunks, convert_to_tensor=True)

#Query embedding (on colab step 5)

def get_top_chunks(query, chunk_embeddings, text_chunks, top_k=3):
    query_embedding = embedding_model.encode(query, convert_to_tensor=True)

    query_norm = torch.nn.functional.normalize(query_embedding, p=2, dim=0)
    chunks_norm = torch.nn.functional.normalize(chunk_embeddings, p=2, dim=1)

    similarities = torch.matmul(chunks_norm, query_norm)

    top_indices = torch.topk(similarities, k=top_k).indices

    return [text_chunks[i] for i in top_indices]

def set_topic(topic):
    global chosen_topic
    chosen_topic = topic
    return f"✅ Great! You've chosen **{topic}**. Let's start your study session."

    

def respond(message, history):
    global chosen_topic

    #Getting the relevnt parts from the txt file
    relevant_chunks = get_top_chunks(message, chunk_embeddings, bio_chunks, top_k=4)
    spec_content = "\n".join(relevant_chunks)

    system_prompt = (
        f"You are a helpful science tutor who primarily teaches 14 to 16-year-old students "
        f"under the UK education system, preparing them for GCSEs within the next two years. "
        f"You are tutoring AQA GCSE Biology at both higher and foundation levels. "
        f"Do not include content beyond this scope. "
        f"You will be teaching them about {chosen_topic}. "
        f"First, provide the user with information on the topic in small, digestible sections, "
        f"preferably with each section as separate text. Always keep the aim of teaching this topic in mind. "
        f"Once all the information on that specific topic has been covered, "
        f"ask the user if they have any questions. If they do, answer in a way that helps them understand better. "
        f"When the user has no more questions, give them a set of exam-style questions, one by one, "
        f"covering different areas of the topic. "
        f"The user may also request to focus on a specific area of the topic at first. "
        f"After the user answers each question, provide feedback to ensure they are exam ready before moving on. "
        f"This cycle repeats: content in small sections, check understanding, questions one by one, mark one by one, then repeat. "
        f"Use the following specification excerpts to answer:\n{spec_content}"
)


    messages = [{"role": "system", "content": system_prompt}]

    if history:
        messages.extend(history)
    messages.append({"role": "user", "content": message})
    
    response = client.chat_completion(
        messages,
        max_tokens=300
    )
    return response['choices'][0]['message']['content'].strip()



# Topic list
BIO_TOPICS = [
    "Cell Biology",
    "Organisation",
    "Infection and Response",
    "Bioenergetics",
    "Homeostasis and Response",
    "Inheritance, Variation and Evolution",
    "Ecology"
]

chosen_topic = None


# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# ACE it! 📚 — GCSE Biology Tutor")

    with gr.Row():
        topic_dropdown = gr.Dropdown(choices=BIO_TOPICS, label="Choose a Biology Topic")
        topic_button = gr.Button("Confirm Topic")

    topic_output = gr.Markdown()

    chatbot = gr.ChatInterface(respond, type="messages", title="ACE it!")

    topic_button.click(set_topic, inputs=topic_dropdown, outputs=topic_output)

    

demo.launch()