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import os
import streamlit as st
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import numpy as np
import requests
from PIL import Image
# Set Groq API key in environment variable
os.environ['GROQ_API_KEY'] = "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"
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
# Load pre-trained ResNet50 for normal/abnormal classification
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
classification_model = Model(inputs=base_model.input, outputs=predictions)
# Load pre-trained ResNet50 for organ recognition
organ_model = ResNet50(weights='imagenet')
def classify_image(image_path):
"""Classify the image as normal or abnormal."""
image = load_img(image_path, target_size=(224, 224))
image_array = img_to_array(image)
image_array = preprocess_input(image_array)
image_array = np.expand_dims(image_array, axis=0)
prediction = classification_model.predict(image_array)
return 'Abnormal' if prediction[0][0] > 0.5 else 'Normal'
def recognize_organ(image_path):
"""Recognize the organ in the image."""
image = load_img(image_path, target_size=(224, 224))
image_array = img_to_array(image)
image_array = preprocess_input(image_array)
image_array = np.expand_dims(image_array, axis=0)
prediction = organ_model.predict(image_array)
decoded = decode_predictions(prediction, top=3)[0]
return decoded[0][1] # Top predicted class
def get_ai_insights(organ):
"""Fetch AI-based insights about the organ using Groq API."""
url = "https://api.groq.com/v1/insights"
headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
data = {"query": f"Provide detailed insights about {organ} X-ray, its diseases, and treatments."}
response = requests.post(url, headers=headers, json=data)
if response.status_code == 200:
return response.json().get("insights", "No insights available.")
else:
return "Failed to fetch insights. Please try again later."
def main():
st.title("Medical Image Classification App")
st.sidebar.title("Navigation")
uploaded_file = st.file_uploader("Upload an X-ray or MRI image", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
with open("temp_image.jpg", "wb") as f:
f.write(uploaded_file.getbuffer())
st.write("### Classification Result")
result = classify_image("temp_image.jpg")
st.write(f"The X-ray is classified as: **{result}**")
st.write("### Organ Recognition")
organ = recognize_organ("temp_image.jpg")
st.write(f"Recognized Organ: **{organ}**")
st.write("### AI-Based Insights")
insights = get_ai_insights(organ)
st.write(insights)
if __name__ == "__main__":
main()
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