Delete app.py

app.py

@@ -1,127 +0,0 @@
-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()
-