Update app.py

app.py

@@ -18,11 +18,11 @@ from PIL import Image
 #model_alzheimer = keras.models.load_model(
 #    "models/model_alzheimer.h5", custom_objects={'KerasLayer': hub.KerasLayer}
 
-# API key and user ID for the new API
+# API key and user ID for on-demand
 api_key = 'KGSjxB1uptfSk8I8A7ciCuNT9Xa3qWC3'
 external_user_id = 'plugin-1717464304'
 
-# Step 1: Create a chat session
+# Step 1: Create a chat session with the API
 def create_chat_session():
     create_session_url = 'https://api.on-demand.io/chat/v1/sessions'
     create_session_headers = {
@@ -32,13 +32,12 @@ def create_chat_session():
         "pluginIds": [],
         "externalUserId": external_user_id
     }
-    # Make the request to create a chat session
     response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body)
     response_data = response.json()
     session_id = response_data['data']['id']
     return session_id
 
-# Step 2: Submit a query to the API
+# Step 2: Submit query to the API
 def submit_query(session_id, query):
     submit_query_url = f'https://api.on-demand.io/chat/v1/sessions/{session_id}/query'
     submit_query_headers = {
@@ -51,9 +50,9 @@ def submit_query(session_id, query):
         "responseMode": "sync"
     }
     response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
-    return response.json()['data']['response']
+    return response.json()
 
-# CNN Model for MRI Image Diagnosis (mockup since actual model code isn't available)
+# Combined disease model (placeholder)
 class CombinedDiseaseModel(tf.keras.Model):
     def __init__(self, model_initial, model_alzheimer, model_tumor, model_stroke):
         super(CombinedDiseaseModel, self).__init__()
@@ -62,7 +61,6 @@ class CombinedDiseaseModel(tf.keras.Model):
         self.model_tumor = model_tumor
         self.model_stroke = model_stroke
         self.disease_labels = ["Alzheimer's", 'No Disease', 'Stroke', 'Tumor']
-
         self.sub_models = {
             "Alzheimer's": model_alzheimer,
             'Tumor': model_tumor,
@@ -93,52 +91,30 @@ class CombinedDiseaseModel(tf.keras.Model):
 
             sub_category = sub_category_label[sub_category]
 
-        return f"The MRI image shows {main_disease} with a probability of {main_disease_prob*100:.2f}%.\nThe subcategory of {main_disease} is {sub_category} with a probability of {sub_category_prob*100:.2f}%."
-
-# Example CNN models (mockup)
-# cnn_model = CombinedDiseaseModel(model_initial, model_alzheimer, model_tumor, model_stroke)
+        return f"The MRI image shows {main_disease} with a probability of {main_disease_prob*100:.2f}%.\n" \
+               f"The subcategory of {main_disease} is {sub_category} with a probability of {sub_category_prob*100:.2f}%."
 
+# Placeholder function to process images
 def process_image(image):
-    """
-    Processes the uploaded MRI image and makes predictions using the combined CNN model.
-    """
     image = image.resize((256, 256))
-    image = image.convert("RGB")
+    image.convert("RGB")
     image_array = np.array(image) / 255.0
     image_array = np.expand_dims(image_array, axis=0)
-    
-    predictions = cnn_model(image_array)  # Call the model to get predictions (replace with actual model call)
-    return predictions
-
-def query_llm_via_on_demand(patient_info, query_type):
-    """
-    Sends patient information and query type to the on-demand API and returns the generated response.
-    """
-    session_id = create_chat_session()
-    query = f"Patient Information: {patient_info}\nQuery Type: {query_type}\nPlease provide additional insights."
-    
-    response = submit_query(session_id, query)
-    return response
+    # Prediction logic here
+    # predictions = cnn_model(image_array)
+    return "Mock prediction: Disease identified with a probability of 85%."
 
-def gradio_interface(patient_info, query_type, image=None):
-    """
-    Gradio interface function that processes patient info, query type, and an optional MRI image, 
-    and provides results from both the CNN model and the on-demand LLM API.
-    """
-    image_response = ""
+# Function to handle patient info, query, and image processing
+def gradio_interface(patient_info, query_type, image):
     if image is not None:
         image_response = process_image(image)
-    
-    llm_response = query_llm_via_on_demand(patient_info, query_type)
-    
-    response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\nLLM Response:\n{llm_response}"
-    
-    if image_response:
-        response += f"\n\nImage Diagnosis:\n{image_response}"
-    
-    return response
+        session_id = create_chat_session()
+        llm_response = submit_query(session_id, f"Patient Info: {patient_info}. Query Type: {query_type}.")
+        return f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\n{image_response}\n\nLLM Response:\n{llm_response['data']['message']}"
+    else:
+        return "Please upload an image."
 
-# Create Gradio interface with MRI image upload option
+# Gradio interface
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=[
@@ -150,18 +126,16 @@ iface = gr.Interface(
         ),
         gr.Textbox(
             label="Query Type",
-            placeholder="Enter the query type here..."
+            placeholder="Describe the type of diagnosis or information needed..."
         ),
         gr.Image(
             type="pil",
             label="Upload an MRI Image",
-            optional=True  # Allow the image input to be optional
         )
     ],
     outputs=gr.Textbox(label="Response", placeholder="The response will appear here..."),
-    title="Medical Diagnosis with MRI and On-Demand LLM Insights",
-    description="Upload MRI images and provide patient information for diagnosis. The system integrates MRI diagnosis with insights from the on-demand LLM API."
+    title="Medical Diagnosis with MRI and LLM",
+    description="Upload MRI images and provide patient information for a combined CNN model and LLM analysis."
 )
 
-# Launch the Gradio app
 iface.launch()