Upload app.py

app.py

@@ -0,0 +1,125 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import torch
+from sentence_transformers import SentenceTransformer
+
+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()
+