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import os
from crewai import Agent, Task, Crew
from langchain_groq import ChatGroq
from PIL import Image
import streamlit as st
# Initialize the LLM (assuming it can also handle image processing)
llm = ChatGroq(
groq_api_key="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",
model_name="llama-3.1-70b-versatile",
)
# Define the agent for medical image analysis
medical_image_agent = Agent(
role='Medical Image Captioning Agent',
goal='Analyze medical images and provide a title, description, and reasons for detected issues.',
backstory=(
"You are a Medical Image Captioning Agent. Your role is to analyze medical-related images "
"Figure out issues from the image of X-rays, skin blemishes, or accident pictures, and then generate a relevant title, description, "
"and reasons why issues might have occurred."
),
verbose=True,
llm=llm,
)
def process_image_with_agent(image_path, agent):
"""Process the uploaded image using the AI agent."""
try:
image = Image.open(image_path)
except Exception as e:
return f"Error opening image: {e}"
# Define the task for the agent
task_description = f"Analyze the medical image and provide a title, description, and reasons for detected issues. Image path: {image_path}"
# Define the task
medical_image_task = Task(
description=task_description,
agent=agent,
human_input=False,
expected_output="Title, description, and reasons for detected issues",
)
# Instantiate the crew with the defined agent and task
crew = Crew(
agents=[agent],
tasks=[medical_image_task],
verbose=2,
)
# Execute the task and return the result
result = crew.kickoff()
return result
# Streamlit App
def main():
st.title("Medical Image Analysis and Captioning")
st.write(
"Upload a medical-related image (e.g., X-ray, skin blemish, accident photo), "
"and the AI agent will analyze it to provide relevant insights."
)
# File uploader
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png", "bmp"])
if uploaded_file:
# Save the uploaded file locally
temp_file_path = f"temp_{uploaded_file.name}"
with open(temp_file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
# Display the uploaded image
st.image(temp_file_path, caption="Uploaded Image", use_column_width=True)
# Process the image
with st.spinner("Analyzing the image..."):
result = process_image_with_agent(temp_file_path, medical_image_agent)
# Display the result
st.write("### Analysis Result:")
st.write(result)
# Remove the temporary file
os.remove(temp_file_path)
if __name__ == "__main__":
main()
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