Update app.py

app.py

@@ -1,65 +1,84 @@
 import os
-import numpy as np
+import streamlit as st
 import tensorflow as tf
-from tensorflow.keras.models import load_model
+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
-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 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 the classification model (make sure the model is trained and saved)
-classification_model = load_model('classification_model.h5')  # Model for normal/abnormal classification
+# Load pre-trained ResNet50 for organ recognition
+organ_model = ResNet50(weights='imagenet')
 
-# 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)
+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'
 
-# 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
+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
 
-# 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"
+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:
-        classification_result = "Abnormal"
+        return "Failed to fetch insights. Please try again later."
 
-    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
-    st.write(f"Image Classification: {classification_result}")
+def main():
+    st.title("Medical Image Classification App")
+    st.sidebar.title("Navigation")
     
-    # 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)
+    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()