Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Load model
+@st.cache_resource
+def load_model():
+    model_id = "ibm-granite/granite-3.3-2b-instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+generator = load_model()
+
+# Streamlit UI
+st.title("🧠 HealthAI Chatbot")
+st.markdown("Ask me about your symptoms or health advice!")
+
+user_input = st.text_input("💬 Enter your symptoms or question:", "")
+
+if user_input:
+    prompt = f"Answer as a health assistant: {user_input}"
+    output = generator(prompt, max_new_tokens=150, do_sample=True)[0]["generated_text"]
+    cleaned_output = output.replace(prompt, "").strip()
+    st.success(cleaned_output)