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import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from PIL import Image
import streamlit as st
from groq import Groq
# Set Groq API key in environment variable
os.environ['GROQ_API_KEY'] = "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"
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
# Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)
# Load the classification model (make sure the model is trained and saved)
classification_model = load_model('classification_model.h5') # Model for normal/abnormal classification
# Function to load the image and process it
def load_image(image_file):
img = Image.open(image_file)
img = img.resize((224, 224)) # Resize image to match model input
img_array = np.array(img) / 255.0 # Normalize the image
return np.expand_dims(img_array, axis=0)
# Function for AI-based knowledge generation using Groq API
def generate_ai_insights(organ_name):
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": f"Explain the diseases and treatments related to {organ_name}.",
}
],
model="llama-3.3-70b-versatile",
)
return chat_completion.choices[0].message.content
# Streamlit UI
st.title('Medical Image Classification and Insights')
st.sidebar.title("Menu")
uploaded_image = st.sidebar.file_uploader("Upload X-ray or MRI Image", type=["jpg", "png", "jpeg"])
if uploaded_image is not None:
image = load_image(uploaded_image)
# Classify normal or abnormal
prediction = classification_model.predict(image)
if prediction[0] > 0.5:
classification_result = "Normal"
else:
classification_result = "Abnormal"
st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
st.write(f"Image Classification: {classification_result}")
# Recognize the organ (You can expand the model to predict organ type)
organ_name = "Lung" # Placeholder
st.write(f"Recognized Organ: {organ_name}")
# Get AI insights for the recognized organ
ai_insights = generate_ai_insights(organ_name)
st.write("AI-Based Insights on Organ Diseases and Treatments:")
st.write(ai_insights)
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