Delete app.py

app.py

@@ -1,119 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM, pipeline
-import torch
-import faiss
-import numpy as np
-
-# -------------------
-# CONFIG
-# -------------------
-DEVICE = "cpu"
-MAX_TOKENS = 256
-SEARCH_RESULTS = 3
-
-SYSTEM_MESSAGE = (
-    "You are a helpful medical tutor. Provide clear, accurate explanations "
-    "based strictly on the provided notes. If the answer isn't in the notes, "
-    "say you don't have enough information."
-)
-
-# -------------------
-# MODELS
-# -------------------
-# PubMedBERT for embeddings
-EMBED_MODEL_NAME = "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"
-embed_tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_NAME)
-embed_model = AutoModel.from_pretrained(EMBED_MODEL_NAME)
-
-# Flan-T5 for generation
-GEN_MODEL_NAME = "google/flan-t5-small"
-gen_tokenizer = AutoTokenizer.from_pretrained(GEN_MODEL_NAME)
-gen_model = AutoModelForSeq2SeqLM.from_pretrained(GEN_MODEL_NAME)
-generator = pipeline("text2text-generation", model=gen_model, tokenizer=gen_tokenizer, device=-1)
-
-# -------------------
-# SAMPLE NOTES
-# -------------------
-notes_dict = {
-    "Anatomy": """
-    The heart pumps blood through pulmonary and systemic circuits.
-    Oxygenated blood leaves the left ventricle and travels through the aorta.
-    """,
-    "Physiology": """
-    The respiratory system involves the exchange of oxygen and carbon dioxide
-    in the alveoli of the lungs.
-    """,
-    "Pharmacology": """
-    Paracetamol is used to relieve pain and reduce fever.
-    It acts centrally on the hypothalamic heat-regulating center.
-    """,
-}
-
-# -------------------
-# BUILD EMBEDDING INDEX
-# -------------------
-def embed_texts(texts):
-    inputs = embed_tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
-    with torch.no_grad():
-        outputs = embed_model(**inputs)
-    embeddings = outputs.last_hidden_state.mean(dim=1).numpy().astype("float32")
-    return embeddings
-
-doc_map = []
-chunks = []
-for subject, content in notes_dict.items():
-    parts = [content[i:i + 400] for i in range(0, len(content), 400)]
-    for p in parts:
-        doc_map.append((subject, p))
-        chunks.append(p)
-
-embeddings = embed_texts(chunks)
-index = faiss.IndexFlatL2(embeddings.shape[1])
-index.add(embeddings)
-
-# -------------------
-# SEMANTIC SEARCH
-# -------------------
-def semantic_search(query, max_results=SEARCH_RESULTS):
-    query_emb = embed_texts([query])
-    distances, ids = index.search(query_emb, max_results)
-    results = []
-    for idx in ids[0]:
-        subject, chunk = doc_map[idx]
-        results.append((subject, chunk))
-    return results
-
-# -------------------
-# RESPONSE FUNCTION
-# -------------------
-def respond(message, history):
-    if not message.strip():
-        return "Please enter a question."
-
-    search_results = semantic_search(message)
-    context_parts = [f"From {s}:\n{t}" for s, t in search_results]
-    context_text = "\n\n".join(context_parts) if context_parts else "No relevant notes found."
-
-    prompt = f"{SYSTEM_MESSAGE}\n\nNotes:\n{context_text}\n\nQuestion: {message}\nAnswer:"
-
-    response = generator(prompt, max_new_tokens=MAX_TOKENS)[0]["generated_text"]
-    return response
-
-# -------------------
-# GRADIO APP
-# -------------------
-with gr.Blocks(title="PubMed Medical Tutor") as demo:
-    gr.Markdown("# 🧬 PubMed Medical Tutor (CPU Friendly)")
-    chatbot = gr.Chatbot(label="Tutor Chat")
-    msg = gr.Textbox(label="Ask a medical question...")
-    clear = gr.Button("Clear Chat")
-
-    def user_input(message, history):
-        bot_reply = respond(message, history)
-        history.append((message, bot_reply))
-        return "", history
-
-    msg.submit(user_input, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.launch()