Update app.py

app.py

@@ -1,72 +1,65 @@
 import os
-import streamlit as st
+import numpy as np
 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
+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')
 
-# 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)]
+# Initialize Groq client
+client = Groq(api_key=GROQ_API_KEY)
 
-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."
+# Load the classification model (make sure the model is trained and saved)
+classification_model = load_model('classification_model.h5')  # Model for normal/abnormal classification
 
-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"])
+# 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)
 
-    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())
+# 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
 
-        st.write("### Classification Result")
-        result = classify_image("temp_image.jpg")
-        st.write(f"The X-ray is classified as: **{result}**")
+# Streamlit UI
+st.title('Medical Image Classification and Insights')
+st.sidebar.title("Menu")
 
-        st.write("### Organ Recognition")
-        organ = recognize_organ("temp_image.jpg")
-        st.write(f"Recognized Organ: **{organ}**")
+uploaded_image = st.sidebar.file_uploader("Upload X-ray or MRI Image", type=["jpg", "png", "jpeg"])
 
-        st.write("### AI-Based Insights")
-        insights = get_ai_insights(organ)
-        st.write(insights)
+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"
 
-if __name__ == "__main__":
-    main()
+    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)