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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}")
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