Create App.py

App.py

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+import os
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+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 models (assumed to be in the same directory or provide the correct path)
+classification_model = load_model('classification_model.h5')  # Model for normal/abnormal classification
+organ_recognition_model = load_model('organ_recognition_model.h5')  # Model for organ recognition
+
+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) / 255.0
+    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) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+    prediction = organ_recognition_model.predict(image_array)
+    organ_classes = ['Lung', 'Heart', 'Brain']  # Example classes
+    return organ_classes[np.argmax(prediction)]
+
+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()