Delete app.py

app.py

@@ -1,52 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-# -------------------
-# CONFIG
-# -------------------
-MODEL_NAME = "Varshitha/flan-t5-small-finetuned-medicine"  # Medical Flan-T5
-DEVICE = "cpu"
-MAX_TOKENS = 256
-
-SYSTEM_MESSAGE = (
-    "You are a helpful and accurate medical tutor. "
-    "Explain clearly and safely, using standard medical knowledge. "
-    "If unsure, say you don't have enough information."
-)
-
-# -------------------
-# LOAD MODEL
-# -------------------
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
-generator = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=-1)
-
-# -------------------
-# CHAT FUNCTION
-# -------------------
-def respond(message, history):
-    if not message.strip():
-        return "Please enter a medical question."
-
-    prompt = f"{SYSTEM_MESSAGE}\n\nQuestion: {message}\nAnswer:"
-    response = generator(prompt, max_new_tokens=MAX_TOKENS)[0]["generated_text"]
-    return response
-
-# -------------------
-# GRADIO APP
-# -------------------
-with gr.Blocks(title="Simple Medical Tutor") as demo:
-    gr.Markdown("# 🩺 Simple 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()