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| import streamlit as st | |
| import fitz # PyMuPDF | |
| from gtts import gTTS | |
| import tempfile | |
| import os | |
| import requests | |
| # Streamlit page setup | |
| st.set_page_config(page_title="Health Report Analyzer", page_icon="🩺") | |
| st.title("🩺 Health Report Analyzer") | |
| # Upload PDF | |
| uploaded_file = st.file_uploader("Upload a Health Report (PDF only)", type="pdf") | |
| # Extract text from PDF | |
| def extract_text(file): | |
| doc = fitz.open(stream=file.read(), filetype="pdf") | |
| text = "" | |
| for page in doc: | |
| text += page.get_text() | |
| return text | |
| # Ask medical question using Hugging Face Inference API (BioGPT) | |
| API_URL = "https://api-inference.huggingface.co/models/microsoft/BioGPT-Large" | |
| headers = {"Content-Type": "application/json"} | |
| def ask_medical_question(question): | |
| payload = { | |
| "inputs": f"Explain this medical concept in detail for a general audience: {question}" | |
| } | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| return response.json()[0]['generated_text'] | |
| else: | |
| return "⚠️ Sorry, something went wrong while contacting the medical AI." | |
| # Doctor recommendation logic | |
| def suggest_doctor(term): | |
| term = term.lower() | |
| if "kidney" in term or "creatinine" in term: | |
| return "You should consider seeing a **nephrologist**." | |
| elif "hemoglobin" in term: | |
| return "You might want to consult a **hematologist** if your hemoglobin is outside the normal range." | |
| elif "liver" in term or "bilirubin" in term: | |
| return "Consult a **hepatologist** for liver-related concerns." | |
| elif "sugar" in term or "glucose" in term: | |
| return "A **diabetologist** or **endocrinologist** is recommended for sugar level issues." | |
| return "Please consult a **general physician** for further evaluation." | |
| # Basic reference ranges | |
| def get_reference_range(term): | |
| term = term.lower() | |
| if "hemoglobin" in term: | |
| return "Normal range: 13.8 to 17.2 g/dL for men, 12.1 to 15.1 g/dL for women." | |
| elif "creatinine" in term: | |
| return "Normal range: 0.74 to 1.35 mg/dL for men, 0.59 to 1.04 mg/dL for women." | |
| elif "bilirubin" in term: | |
| return "Normal range: 0.1 to 1.2 mg/dL." | |
| return "Range data not available for this term." | |
| # Main logic | |
| if uploaded_file: | |
| text_data = extract_text(uploaded_file) | |
| st.success("✅ Health Report Uploaded Successfully!") | |
| # Display the report text | |
| st.markdown("### 📄 Health Report Content") | |
| st.write(text_data) | |
| st.session_state['report_text'] = text_data | |
| st.subheader("💬 Ask About Any Medical Term or Part of the Report") | |
| user_question = st.text_input("Enter a medical term or question (e.g. 'CT scan', 'Explain creatinine'):", key="question") | |
| if st.button("Get AI Explanation") and user_question: | |
| with st.spinner("Thinking..."): | |
| explanation = ask_medical_question(user_question) | |
| range_info = get_reference_range(user_question) | |
| suggestion = suggest_doctor(user_question) | |
| st.success("Explanation:") | |
| st.write(explanation) | |
| st.info(f"📏 Reference Range: {range_info}") | |
| st.warning(f"👨⚕️ Doctor Suggestion: {suggestion}") | |
| # Text-to-speech using gTTS | |
| tts = gTTS(text=f"{explanation}. {range_info}. {suggestion}") | |
| temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") | |
| tts.save(temp_audio.name) | |
| with open(temp_audio.name, 'rb') as audio_file: | |
| st.audio(audio_file.read(), format='audio/mp3') | |
| else: | |
| st.info("📂 Upload a PDF Health Report to begin.") | |