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import streamlit as st
import google.generativeai as genai
from PIL import Image
import PyPDF2
import tempfile
import os
from google.api_core import exceptions
from dotenv import load_dotenv
import time
from gtts import gTTS
import base64
load_dotenv()
# Load the API key from the environment variable
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
st.error("Gemini API key not found. Please set the GEMINI_API_KEY environment variable.")
st.stop()
# Configure the Gemini API
genai.configure(api_key=api_key)
model = genai.GenerativeModel('gemini-1.5-flash') # Initialize the model
MAX_RETRIES = 3
RETRY_DELAY = 2 # seconds
# Add Chinese (Simplified) to language support
LANGUAGES = {
"English": "en",
"Spanish": "es",
"French": "fr",
"German": "de",
"Italian": "it",
"Portuguese": "pt",
"Urdu": "ur",
"Chinese (Simplified)": "zh-cn"
}
def analyze_medical_report(content, content_type, lang):
prompt = "Analyze this medical report concisely. Provide key findings, diagnoses, and recommendations:"
# Adjust prompt language if not English
if lang != "en":
translations = {
"es": "Analiza este informe médico de manera concisa. Proporcione hallazgos clave, diagnósticos y recomendaciones:",
"fr": "Analysez ce rapport médical de manière concise. Fournissez les résultats clés, les diagnostics et les recommandations :",
"de": "Analysieren Sie diesen medizinischen Bericht kurz und prägnant. Geben Sie wichtige Ergebnisse, Diagnosen und Empfehlungen an:",
"it": "Analizza questo rapporto medico in modo conciso. Fornisci risultati chiave, diagnosi e raccomandazioni:",
"pt": "Analise este relatório médico de forma concisa. Forneça os principais resultados, diagnósticos e recomendações:",
"ur": "اس طبی رپورٹ کا مختصر تجزیہ کریں۔ اہم نتائج، تشخیصات، اور سفارشات فراہم کریں:",
"zh-cn": "简明分析此医疗报告。提供关键发现、诊断和建议:"
}
prompt = translations.get(lang, prompt)
for attempt in range(MAX_RETRIES):
try:
if content_type == "image":
response = model.generate_content([prompt, content])
else: # text
response = model.generate_content(f"{prompt}\n\n{content}")
return response.text
except exceptions.GoogleAPIError as e:
if attempt < MAX_RETRIES - 1:
st.warning(f"An error occurred. Retrying in {RETRY_DELAY} seconds... (Attempt {attempt + 1}/{MAX_RETRIES})")
time.sleep(RETRY_DELAY)
else:
st.error(f"Failed to analyze the report after {MAX_RETRIES} attempts. Error: {str(e)}")
return fallback_analysis(content, content_type)
def generate_tts_audio(text, lang_code):
# Generate TTS audio from the provided text and language code
tts = gTTS(text=text, lang=lang_code)
# Save the audio to a temporary file
audio_path = "audio_output.mp3"
tts.save(audio_path)
return audio_path
def audio_player(audio_path):
# Display an audio player in Streamlit
audio_file = open(audio_path, "rb")
audio_bytes = audio_file.read()
st.audio(audio_bytes, format="audio/mp3")
def extract_text_from_pdf(pdf_file):
# Create a PDF reader object
pdf_reader = PyPDF2.PdfReader(pdf_file)
# Extract text from each page
text = ""
for page_num in range(len(pdf_reader.pages)):
page = pdf_reader.pages[page_num]
text += page.extract_text()
return text
def main():
st.title("AI-driven Medical Report Analyzer with Multilingual Audio Feedback")
st.write("Upload a medical report (image or PDF) for analysis")
language = st.selectbox("Select language for analysis and audio feedback:", list(LANGUAGES.keys()))
lang_code = LANGUAGES[language]
file_type = st.radio("Select file type:", ("Image", "PDF"))
if file_type == "Image":
uploaded_file = st.file_uploader("Choose a medical report image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp_file:
tmp_file.write(uploaded_file.getvalue())
tmp_file_path = tmp_file.name
image = Image.open(tmp_file_path)
st.image(image, caption="Uploaded Medical Report", use_column_width=True)
if st.button("Analyze Image Report"):
with st.spinner("Analyzing the medical report image..."):
analysis = analyze_medical_report(image, "image", lang_code)
st.subheader("Analysis Results:")
st.write(analysis)
# Generate audio of the analysis
audio_path = generate_tts_audio(analysis, lang_code)
st.write("Listen to the analysis:")
audio_player(audio_path)
os.unlink(tmp_file_path)
else: # PDF
uploaded_file = st.file_uploader("Choose a medical report PDF", type=["pdf"])
if uploaded_file is not None:
st.write("PDF uploaded successfully")
if st.button("Analyze PDF Report"):
with st.spinner("Analyzing the medical report PDF..."):
with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
tmp_file.write(uploaded_file.getvalue())
tmp_file_path = tmp_file.name
with open(tmp_file_path, 'rb') as pdf_file:
pdf_text = extract_text_from_pdf(pdf_file)
analysis = analyze_medical_report(pdf_text, "text", lang_code)
st.subheader("Analysis Results:")
st.write(analysis)
# Generate audio of the analysis
audio_path = generate_tts_audio(analysis, lang_code)
st.write("Listen to the analysis:")
audio_player(audio_path)
os.unlink(tmp_file_path)
# Footer with "Made by Shan"
st.markdown("---")
st.markdown("<p style='text-align: center;'>😎Made by Shan-Ul-Haq😎</p>", unsafe_allow_html=True)
if __name__ == "__main__":
main()