| import streamlit as st |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
| import pandas as pd |
|
|
| |
| @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) |
| return pipeline("text-generation", model=model, tokenizer=tokenizer) |
|
|
| generator = load_model() |
|
|
| |
| st.title("🩺 HealthAI – Intelligent Healthcare Assistant") |
|
|
| |
| tab1, tab2, tab3, tab4 = st.tabs([ |
| "🧠 Patient Chat", "🧾 Disease Prediction", |
| "💊 Treatment Plans", "📊 Health Analytics" |
| ]) |
|
|
| |
| |
| |
| with tab1: |
| st.subheader("Ask any health-related question") |
| query = st.text_area("Enter your question here") |
| |
| if st.button("Get Advice", key="chat"): |
| if query.strip() == "": |
| st.warning("Please enter a question.") |
| else: |
| with st.spinner("Thinking..."): |
| response = generator(query, max_new_tokens=200)[0]["generated_text"] |
| st.success("AI Response:") |
| st.markdown(f"markdown\n{response}\n") |
|
|
| |
| |
| |
| with tab2: |
| st.subheader("Enter your symptoms (comma-separated)") |
| symptoms = st.text_input("E.g. persistent fever, fatigue, dry cough") |
| |
| if st.button("AI Diagnose", key="predict"): |
| if symptoms.strip() == "": |
| st.warning("Please enter your symptoms.") |
| else: |
| prompt = ( |
| f"I am feeling unwell. My symptoms are: {symptoms}.\n" |
| "Can you please suggest what possible conditions I might have based on this?\n" |
| "List top 3 possible diseases with a short reason for each, and give a seriousness score out of 10." |
| ) |
| with st.spinner("Analyzing symptoms..."): |
| result = generator(prompt, max_new_tokens=300, do_sample=True)[0]['generated_text'] |
| st.success("AI Prediction:") |
| st.markdown(f"markdown\n{result}\n") |
|
|
| |
| |
| |
| with tab3: |
| st.header("💊 Treatment Plan Generator") |
| condition = st.text_input("Enter the known condition (e.g., Asthma, Diabetes)") |
|
|
| if st.button("Get Full Treatment Plan"): |
| if not condition.strip(): |
| st.warning("Please enter a condition.") |
| else: |
| with st.spinner("Generating treatment plan..."): |
|
|
| def get_response(prompt): |
| return generator(prompt, max_new_tokens=1000, temperature=0.7, do_sample=True)[0]['generated_text'].strip() |
|
|
| prompts = { |
| "1️⃣ Medications": f"What medications are usually prescribed for {condition}?", |
| "2️⃣ Diet": f"What diet is recommended for someone with {condition}?", |
| "3️⃣ Exercise": f"What type of physical activities should a person with {condition} follow?", |
| "4️⃣ Follow-Up & Monitoring": f"What follow-up steps and monitoring should be done for {condition}?", |
| "5️⃣ Precautions": f"What precautions should be taken by someone with {condition}?", |
| "6️⃣ Mental Health & Stress": f"How can someone with {condition} manage stress and mental health?" |
| } |
|
|
| for section, prompt in prompts.items(): |
| st.subheader(section) |
| st.markdown(f"markdown\n{get_response(prompt)}\n") |
|
|
| |
| |
| |
| with tab4: |
| st.subheader("Track your health data over time") |
| uploaded = st.file_uploader("Upload your CSV file (with columns like 'blood_pressure', 'heart_rate')", type=["csv"]) |
| |
| if uploaded: |
| df = pd.read_csv(uploaded) |
| st.dataframe(df) |
|
|
| for col in df.select_dtypes(include=['float', 'int']).columns: |
| st.line_chart(df[col]) |
| if df[col].mean() > df[col].iloc[-1]: |
| st.info(f"📉 {col} is improving.") |
| else: |
| st.warning(f"📈 {col} is rising — consider medical advice.") |
