Create app.py

app.py

@@ -0,0 +1,52 @@
+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()