app.py

# import streamlit as st
# from fpdf import FPDF
# import os

# llm_result = """
# Diagnosis: Pneumonia

# Prescription:
# - Amoxicillin 500 mg, twice daily for 7 days
# - Paracetamol 500 mg, every 6 hours for fever
# - Rest and hydration
# - Follow-up in 7 days if symptoms persist
# """

# def save_pdf(content):
#     pdf = FPDF()
#     pdf.add_page()
#     pdf.set_font("Arial", size=12)
#     pdf.multi_cell(0, 10, txt=content)

#     pdf_output_path = "prescription.pdf"
#     pdf.output(pdf_output_path)
    
#     # Return the path to download
#     return pdf_output_path

# # Streamlit app
# def main():
#     st.title("Doctor's Assistance: Review and Edit Prescription")
    
#     st.write("## Review the LLM-generated prescription and make edits if necessary.")
#     edited_text = st.text_area("Edit Prescription", value=llm_result, height=300)

#     if st.button("Save Prescription"):
#         if edited_text.strip():
#             pdf_file_path = save_pdf(edited_text)
#             st.success("Prescription saved!")
#             with open(pdf_file_path, "rb") as file:
#                 st.download_button(
#                     label="Download Prescription as PDF",
#                     data=file,
#                     file_name="prescription.pdf",
#                     mime="application/pdf"
#                 )
#         else:
#             st.error("Prescription content is empty. Please add details.")

# if __name__ == "__main__":
#     main()


import streamlit as st
from fpdf import FPDF
import speech_recognition as sr
from io import BytesIO

# Function to predict disease based on symptoms
def predict_disease(symptoms):
    # Example logic (replace with actual logic or model predictions)
    if "fever" in symptoms.lower() and "cough" in symptoms.lower():
        return "Flu"
    elif "pain" in symptoms.lower() and "swelling" in symptoms.lower():
        return "Arthritis"
    else:
        return "Unknown disease. Please provide more details."

# Function to handle voice input
def voice_input():
    r = sr.Recognizer()
    try:
        with sr.Microphone() as source:
            r.adjust_for_ambient_noise(source)
            st.write("Listening...")
            audio = r.listen(source)
        symptoms = r.recognize_google(audio)
        return symptoms
    except sr.UnknownValueError:
        return "Sorry, I could not understand the audio."
    except sr.RequestError:
        return "Error with the speech recognition service."
    except Exception as e:
        return f"Microphone error: {e}"

# Function to save response to a PDF
def save_to_pdf(response):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    pdf.multi_cell(0, 10, f"Medical Assistant Response:\n\n{response}")
    pdf_output = BytesIO()
    pdf.output(pdf_output)
    return pdf_output

# Streamlit app
st.title("Medical Assistance for Doctors")
st.write("Enter symptoms either by typing or using voice input.")

# Input field for entering symptoms manually
symptoms_input = st.text_area("Enter symptoms here:")

# Toggle for voice input
use_voice_input = st.checkbox("Use Voice Input")

# Checkbox to save the result as PDF
save_as_pdf = st.checkbox("Save result as PDF")

# Button to trigger prediction
if st.button("Submit"):
    if use_voice_input:
        symptoms = voice_input()  # Get symptoms via voice input
        if "error" in symptoms.lower():
            st.error(symptoms)  # Display the error message
        else:
            st.write(f"Symptoms recognized: {symptoms}")
    else:
        symptoms = symptoms_input  # Use keyboard input

    if symptoms:
        prediction = predict_disease(symptoms)  # Predict disease based on symptoms
        st.write(f"Predicted Disease: {prediction}")

        # Optionally save the response as a PDF
        if save_as_pdf:
            pdf_output = save_to_pdf(prediction)
            st.download_button(
                label="Download PDF",
                data=pdf_output.getvalue(),
                file_name="medical_assistance.pdf",
                mime="application/pdf"
            )