Spaces:
Running
Running
File size: 3,196 Bytes
c953483 914d63a dd380e5 4fdc44f dd380e5 c953483 dd380e5 c953483 dd380e5 c953483 dd380e5 c953483 dd380e5 c953483 dd380e5 11ddcf8 dd380e5 11ddcf8 dd380e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import streamlit as st
from PIL import Image
import google.generativeai as genai
# Configure Google Generative AI
genai_api_key = "AIzaSyCOEqA_IZlpWCHhMOGaDJ3iJjl5cRmzKgQ"
genai.configure(api_key=genai_api_key)
# Initialize Gemini model
@st.cache_resource
def load_gemini_model():
model = genai.GenerativeModel("gemini-1.5-flash")
return model
# Function to extract text from the image using Gemini model
def extract_text_from_image(uploaded_file, model):
# Open the uploaded file as a PIL image
image = Image.open(uploaded_file).convert("RGB")
# Generate content using the Gemini model with the image
response = model.generate_content(["Extract text from this medical report:", image])
extracted_text = response.text.strip()
return extracted_text
# Function to interpret the extracted text in layman's language
def interpret_medical_report(extracted_text, model):
# Provide interpretation in layman's terms
prompt = (
f"The following is a medical report text:\n\n"
f"{extracted_text}\n\n"
"Please interpret this report for 7th grader and non native english speaker, "
"explaining the main findings in as short as possible"
)
response = model.generate_content([prompt])
interpretation = response.text.strip()
return interpretation
# Function to provide recommendations based on the extracted text
def provide_recommendations(extracted_text, model):
# Provide recommendations
prompt = (
f"Based on the medical report text below:\n\n"
f"{extracted_text}\n\n"
"What recommendations would you give to the patient for managing their health?"
"Provide brief suggestions that are easy to understand for someone without medical knowledge."
)
response = model.generate_content([prompt])
recommendations = response.text.strip()
return recommendations
# Streamlit UI for the web app
def main():
st.title("Medical Report Analyzer Using Gemini Model")
st.write("Upload an image of a medical report")
# Load the Gemini model
model = load_gemini_model()
# File uploader for medical report image
uploaded_image = st.file_uploader("Upload Medical Report Image", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
image = Image.open(uploaded_image).convert("RGB")
st.image(image, caption="Uploaded Medical Report Image", use_container_width=True)
if st.button("Analyze Report"):
with st.spinner("Processing image and analyzing report..."):
# Extract text from image
extracted_text = extract_text_from_image(uploaded_image, model)
# Interpret the extracted text
st.subheader("Interpretation in Layman's Language:")
interpretation = interpret_medical_report(extracted_text, model)
st.text(interpretation)
# Provide health recommendations
st.subheader("Health Recommendations:")
recommendations = provide_recommendations(extracted_text, model)
st.text(recommendations)
if __name__ == "__main__":
main()
|