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.")