app.py

import streamlit as st
import requests
import re
from pydub import AudioSegment
import speech_recognition as sr
import io

# Backend API endpoint
API_URL = "http://10.24.6.170:8000/predict"  # Change to your server IP

# Dictionaries to decode user inputs
gender_map = {1: "Female", 2: "Male"}
cholesterol_map = {1: "Normal", 2: "Elevated", 3: "Peak"}
glucose_map = {1: "Normal", 2: "High", 3: "Extreme"}
binary_map = {0: "No", 1: "Yes"}

# Function to call the backend API
def get_prediction_from_api(data):
    try:
        response = requests.post(API_URL, json=data)
        if response.status_code == 200:
            return response.json().get("diagnosis", "No diagnosis returned.")
        else:
            return f"Error {response.status_code}: {response.text}"
    except Exception as e:
        return f"API request failed: {e}"

# Function to extract patient features from a phrase or transcribed audio
def extract_details_from_text(text):
    age = int(re.search(r'(\d+)\s*year', text).group(1)) if re.search(r'(\d+)\s*year', text) else None
    gender = 2 if "man" in text.lower() else (1 if "female" in text.lower() else None)
    height = int(re.search(r'(\d+)\s*cm', text).group(1)) if re.search(r'(\d+)\s*cm', text) else None
    weight = int(re.search(r'(\d+)\s*kg', text).group(1)) if re.search(r'(\d+)\s*kg', text) else None
    bp_match = re.search(r'BP\s*(\d+)[/](\d+)', text)
    ap_hi, ap_lo = (int(bp_match.group(1)), int(bp_match.group(2))) if bp_match else (None, None)
    cholesterol = 3 if "peak" in text.lower() else 2 if "elevated" in text.lower() else 1
    glucose = 3 if "extreme" in text.lower() else 2 if "high" in text.lower() else 1
    smoke = 1 if "smoke" in text.lower() else 0
    alco = 1 if "alcohol" in text.lower() else 0
    active = 1 if "exercise" in text.lower() or "active" in text.lower() else 0
    return age, gender, height, weight, ap_hi, ap_lo, cholesterol, glucose, smoke, alco, active

# Streamlit UI
st.set_page_config(page_title="Cardiovascular Disease Predictor", layout="centered")
st.title("🫀 Cardiovascular Disease Predictor (LLM Powered)")
st.markdown("This tool uses a fine-tuned BioMedLM model to predict cardiovascular conditions from structured, text, or voice input.")

input_mode = st.radio("Choose input method:", ["Manual Input", "Text Phrase", "Audio Upload"])

if input_mode == "Manual Input":
    age = st.number_input("Age (years)", min_value=1, max_value=120)
    gender = st.selectbox("Gender", [("Female", 1), ("Male", 2)], format_func=lambda x: x[0])[1]
    height = st.number_input("Height (cm)", min_value=50, max_value=250)
    weight = st.number_input("Weight (kg)", min_value=10, max_value=200)
    ap_hi = st.number_input("Systolic BP", min_value=80, max_value=250)
    ap_lo = st.number_input("Diastolic BP", min_value=40, max_value=150)
    cholesterol = st.selectbox("Cholesterol", [("Normal", 1), ("Elevated", 2), ("Peak", 3)], format_func=lambda x: x[0])[1]
    glucose = st.selectbox("Glucose", [("Normal", 1), ("High", 2), ("Extreme", 3)], format_func=lambda x: x[0])[1]
    smoke = st.radio("Smoker?", [("No", 0), ("Yes", 1)], format_func=lambda x: x[0])[1]
    alco = st.radio("Alcohol Intake?", [("No", 0), ("Yes", 1)], format_func=lambda x: x[0])[1]
    active = st.radio("Physically Active?", [("No", 0), ("Yes", 1)], format_func=lambda x: x[0])[1]

    if st.button("Predict Diagnosis"):
        payload = {
            "age": age,
            "gender": gender,
            "height": height,
            "weight": weight,
            "ap_hi": ap_hi,
            "ap_lo": ap_lo,
            "cholesterol": cholesterol,
            "glucose": glucose,
            "smoke": smoke,
            "alco": alco,
            "active": active
        }
        diagnosis = get_prediction_from_api(payload)
        st.success(f"🩺 **Predicted Diagnosis:** {diagnosis}")

elif input_mode == "Text Phrase":
    phrase = st.text_area("Enter patient details in natural language:", height=200)
    if st.button("Extract & Predict"):
        try:
            values = extract_details_from_text(phrase)
            if all(v is not None for v in values):
                keys = ["age", "gender", "height", "weight", "ap_hi", "ap_lo", "cholesterol", "glucose", "smoke", "alco", "active"]
                payload = dict(zip(keys, values))
                diagnosis = get_prediction_from_api(payload)
                st.success(f"🩺 **Predicted Diagnosis:** {diagnosis}")
            else:
                st.warning("Couldn't extract all fields from the text. Please revise.")
        except Exception as e:
            st.error(f"Error: {e}")

elif input_mode == "Audio Upload":
    uploaded_file = st.file_uploader("Upload audio file (WAV, MP3, M4A, MPEG)", type=["wav", "mp3", "m4a", "mpeg"])
    if uploaded_file:
        st.audio(uploaded_file, format='audio/wav')
        audio = AudioSegment.from_file(uploaded_file)
        wav_io = io.BytesIO()
        audio.export(wav_io, format="wav")
        wav_io.seek(0)

        recognizer = sr.Recognizer()
        with sr.AudioFile(wav_io) as source:
            audio_data = recognizer.record(source)

        try:
            text = recognizer.recognize_google(audio_data)
            st.markdown(f"**Transcribed Text:** _{text}_")
            values = extract_details_from_text(text)
            if all(v is not None for v in values):
                keys = ["age", "gender", "height", "weight", "ap_hi", "ap_lo", "cholesterol", "glucose", "smoke", "alco", "active"]
                payload = dict(zip(keys, values))
                diagnosis = get_prediction_from_api(payload)
                st.success(f"🩺 **Predicted Diagnosis:** {diagnosis}")
            else:
                st.warning("Could not extract complete information from audio.")
        except Exception as e:
            st.error(f"Audio processing error: {e}")