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| import streamlit as st | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import re | |
| from peft import PeftModel | |
| from pydub import AudioSegment | |
| import speech_recognition as sr | |
| import io | |
| from audio_recorder_streamlit import audio_recorder # Add at the top with other imports | |
| # Load model and tokenizer from local fine-tuned directory | |
| # Define base and adapter model paths | |
| BASE_MODEL = "stanford-crfm/BioMedLM" # or the path you used originally | |
| ADAPTER_ID = "Tufan1/BioMedLM-Cardio-Fold10-CPU" # HF model ID | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) | |
| # Load base model with safe settings | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| BASE_MODEL, | |
| torch_dtype=torch.float32, | |
| low_cpu_mem_usage=True, # Reduces memory spikes | |
| device_map="cpu" # Force CPU loading | |
| ) | |
| # Load adapter directly from Hub | |
| model = PeftModel.from_pretrained( | |
| base_model, | |
| ADAPTER_ID, | |
| device_map="cpu", | |
| adapter_name="cardio_adapter" | |
| ) | |
| # Force CPU-safe model loading | |
| #base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.float32) | |
| #model = PeftModel.from_pretrained(base_model, ADAPTER_PATH, device_map=None).to("cpu") | |
| # 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 predict diagnosis using the LLM | |
| def get_prediction(age, gender, height, weight, ap_hi, ap_lo, | |
| cholesterol, glucose, smoke, alco, active): | |
| input_text = f"""Patient Record: | |
| - Age: {age} years | |
| - Gender: {gender_map[gender]} | |
| - Height: {height} cm | |
| - Weight: {weight} kg | |
| - Systolic BP: {ap_hi} mmHg | |
| - Diastolic BP: {ap_lo} mmHg | |
| - Cholesterol Level: {cholesterol_map[cholesterol]} | |
| - Glucose Level: {glucose_map[glucose]} | |
| - Smokes: {binary_map[smoke]} | |
| - Alcohol Intake: {binary_map[alco]} | |
| - Physically Active: {binary_map[active]} | |
| Diagnosis:""" | |
| inputs = tokenizer(input_text, return_tensors="pt").to("cpu") | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_new_tokens=4) | |
| print("Raw output:", outputs) # Add this line | |
| decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| diagnosis = decoded.split("Diagnosis:")[-1].strip() | |
| return diagnosis | |
| # 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"): | |
| diagnosis = get_prediction(age, gender, height, weight, ap_hi, ap_lo, | |
| cholesterol, glucose, smoke, alco, active) | |
| 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): | |
| diagnosis = get_prediction(*values) | |
| 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": | |
| audio_input_mode = st.radio("Choose audio input type:", ["Upload Audio File", "Record Audio"]) | |
| if audio_input_mode == "Upload Audio File": | |
| 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) | |
| if audio is not None and len(audio) > 0: | |
| 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): | |
| diagnosis = get_prediction(*values) | |
| 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}") | |
| else: | |
| st.error("Uploaded audio file is empty or not valid.") | |
| elif audio_input_mode == "Record Audio": | |
| audio = audio_recorder("Click to record", "Recording...") | |
| if audio is not None and len(audio) > 0: # Check if audio is not None and has length | |
| st.audio(audio, format="audio/wav") # Directly use audio as it is already a bytes object | |
| wav_io = io.BytesIO(audio) | |
| 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): | |
| diagnosis = get_prediction(*values) | |
| st.success(f"🩺 **Predicted Diagnosis:** {diagnosis}") | |
| else: | |
| st.warning("Could not extract complete information from recorded audio.") | |
| except Exception as e: | |
| st.error(f"Recording processing error: {e}") | |
| else: | |
| st.error("No audio recorded or audio is empty.") | |