Update app.py

app.py

@@ -1,22 +1,166 @@
+# import requests
+# import numpy as np
+# import tensorflow as tf
+# import tensorflow_hub as hub
+# import gradio as gr
+# from PIL import Image
+
+# # Load models
+# #model_initial = keras.models.load_model(
+# #    "models/initial_model.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+# #)
+# #model_tumor = keras.models.load_model(
+# #    "models/model_tumor.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+# #)
+# #model_stroke = keras.models.load_model(
+# #    "models/model_stroke.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+# #)
+# #model_alzheimer = keras.models.load_model(
+# #    "models/model_alzheimer.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+
+# # API key and user ID for on-demand
+# api_key = 'KGSjxB1uptfSk8I8A7ciCuNT9Xa3qWC3'
+# external_user_id = 'plugin-1717464304'
+
+# # 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 = {
+#         'apikey': api_key
+#     }
+#     create_session_body = {
+#         "pluginIds": [],
+#         "externalUserId": external_user_id
+#     }
+#     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 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 = {
+#         'apikey': api_key
+#     }
+#     submit_query_body = {
+#         "endpointId": "predefined-openai-gpt4o",
+#         "query": query,
+#         "pluginIds": ["plugin-1712327325", "plugin-1713962163"],
+#         "responseMode": "sync"
+#     }
+#     response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
+#     return response.json()
+
+# # Combined disease model (placeholder)
+# class CombinedDiseaseModel(tf.keras.Model):
+#     def __init__(self, model_initial, model_alzheimer, model_tumor, model_stroke):
+#         super(CombinedDiseaseModel, self).__init__()
+#         self.model_initial = model_initial
+#         self.model_alzheimer = model_alzheimer
+#         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,
+#             'Stroke': model_stroke
+#         }
+
+#     def call(self, inputs):
+#         initial_probs = self.model_initial(inputs, training=False)
+#         main_disease_idx = tf.argmax(initial_probs, axis=1)
+#         main_disease = self.disease_labels[main_disease_idx[0].numpy()]
+#         main_disease_prob = initial_probs[0, main_disease_idx[0]].numpy()
+
+#         if main_disease == 'No Disease':
+#             sub_category = "No Disease"
+#             sub_category_prob = main_disease_prob
+#         else:
+#             sub_model = self.sub_models[main_disease]
+#             sub_category_pred = sub_model(inputs, training=False)
+#             sub_category = tf.argmax(sub_category_pred, axis=1).numpy()[0]
+#             sub_category_prob = sub_category_pred[0, sub_category].numpy()
+
+#             if main_disease == "Alzheimer's":
+#                 sub_category_label = ['Very Mild', 'Mild', 'Moderate']
+#             elif main_disease == 'Tumor':
+#                 sub_category_label = ['Glioma', 'Meningioma', 'Pituitary']
+#             elif main_disease == 'Stroke':
+#                 sub_category_label = ['Ischemic', 'Hemorrhagic']
+
+#             sub_category = sub_category_label[sub_category]
+
+#         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):
+#     image = image.resize((256, 256))
+#     image.convert("RGB")
+#     image_array = np.array(image) / 255.0
+#     image_array = np.expand_dims(image_array, axis=0)
+#     # Prediction logic here
+#     # predictions = cnn_model(image_array)
+#     return "Mock prediction: Disease identified with a probability of 85%."
+
+# # 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)
+
+#         # Call LLM with patient info and query
+#         session_id = create_chat_session()
+#         query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
+#         llm_response = submit_query(session_id, query)
+        
+#         # Debug: Print the full response to inspect it
+#         print("LLM Response:", llm_response)  # This will print the full response for inspection
+
+#         # Safely handle 'message' if it exists
+#         message = llm_response.get('data', {}).get('message', 'No message returned from LLM')
+
+#         # Check if message is empty and print the complete response if necessary
+#         if message == 'No message returned from LLM':
+#             print("Full LLM Response Data:", llm_response)  # Inspect the full LLM response for any helpful info
+
+#         response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\n{image_response}\n\nLLM Response:\n{message}"
+#         return response
+#     else:
+#         return "Please upload an image."
+
+# # Gradio interface
+# iface = gr.Interface(
+#     fn=gradio_interface,
+#     inputs=[
+#         gr.Textbox(
+#             label="Patient Information",
+#             placeholder="Enter patient details here...",
+#             lines=5,
+#             max_lines=10
+#         ),
+#         gr.Textbox(
+#             label="Query Type",
+#             placeholder="Describe the type of diagnosis or information needed..."
+#         ),
+#         gr.Image(
+#             type="pil",
+#             label="Upload an MRI Image",
+#         )
+#     ],
+#     outputs=gr.Textbox(label="Response", placeholder="The response will appear here..."),
+#     title="Medical Diagnosis with MRI and LLM",
+#     description="Upload MRI images and provide patient information for a combined CNN model and LLM analysis."
+# )
+
+# iface.launch()
+
+
+
+
+
 import requests
-import numpy as np
-import tensorflow as tf
-import tensorflow_hub as hub
 import gradio as gr
-from PIL import Image
-
-# Load models
-#model_initial = keras.models.load_model(
-#    "models/initial_model.h5", custom_objects={'KerasLayer': hub.KerasLayer}
-#)
-#model_tumor = keras.models.load_model(
-#    "models/model_tumor.h5", custom_objects={'KerasLayer': hub.KerasLayer}
-#)
-#model_stroke = keras.models.load_model(
-#    "models/model_stroke.h5", custom_objects={'KerasLayer': hub.KerasLayer}
-#)
-#model_alzheimer = keras.models.load_model(
-#    "models/model_alzheimer.h5", custom_objects={'KerasLayer': hub.KerasLayer}
 
 # API key and user ID for on-demand
 api_key = 'KGSjxB1uptfSk8I8A7ciCuNT9Xa3qWC3'
@@ -52,82 +196,25 @@ def submit_query(session_id, query):
     response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
     return response.json()
 
-# Combined disease model (placeholder)
-class CombinedDiseaseModel(tf.keras.Model):
-    def __init__(self, model_initial, model_alzheimer, model_tumor, model_stroke):
-        super(CombinedDiseaseModel, self).__init__()
-        self.model_initial = model_initial
-        self.model_alzheimer = model_alzheimer
-        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,
-            'Stroke': model_stroke
-        }
-
-    def call(self, inputs):
-        initial_probs = self.model_initial(inputs, training=False)
-        main_disease_idx = tf.argmax(initial_probs, axis=1)
-        main_disease = self.disease_labels[main_disease_idx[0].numpy()]
-        main_disease_prob = initial_probs[0, main_disease_idx[0]].numpy()
-
-        if main_disease == 'No Disease':
-            sub_category = "No Disease"
-            sub_category_prob = main_disease_prob
-        else:
-            sub_model = self.sub_models[main_disease]
-            sub_category_pred = sub_model(inputs, training=False)
-            sub_category = tf.argmax(sub_category_pred, axis=1).numpy()[0]
-            sub_category_prob = sub_category_pred[0, sub_category].numpy()
-
-            if main_disease == "Alzheimer's":
-                sub_category_label = ['Very Mild', 'Mild', 'Moderate']
-            elif main_disease == 'Tumor':
-                sub_category_label = ['Glioma', 'Meningioma', 'Pituitary']
-            elif main_disease == 'Stroke':
-                sub_category_label = ['Ischemic', 'Hemorrhagic']
-
-            sub_category = sub_category_label[sub_category]
-
-        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):
-    image = image.resize((256, 256))
-    image.convert("RGB")
-    image_array = np.array(image) / 255.0
-    image_array = np.expand_dims(image_array, axis=0)
-    # Prediction logic here
-    # predictions = cnn_model(image_array)
-    return "Mock prediction: Disease identified with a probability of 85%."
-
-# 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)
-
-        # Call LLM with patient info and query
-        session_id = create_chat_session()
-        query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
-        llm_response = submit_query(session_id, query)
-        
-        # Debug: Print the full response to inspect it
-        print("LLM Response:", llm_response)  # This will print the full response for inspection
+# Function to handle patient info, query, and image processing (now focusing on LLM)
+def gradio_interface(patient_info, query_type):
+    # Call LLM with patient info and query
+    session_id = create_chat_session()
+    query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
+    llm_response = submit_query(session_id, query)
+    
+    # Debug: Print the full response to inspect it
+    print("LLM Response:", llm_response)  # This will print the full response for inspection
 
-        # Safely handle 'message' if it exists
-        message = llm_response.get('data', {}).get('message', 'No message returned from LLM')
+    # Safely handle 'message' if it exists
+    message = llm_response.get('data', {}).get('message', 'No message returned from LLM')
 
-        # Check if message is empty and print the complete response if necessary
-        if message == 'No message returned from LLM':
-            print("Full LLM Response Data:", llm_response)  # Inspect the full LLM response for any helpful info
+    # Check if message is empty and print the complete response if necessary
+    if message == 'No message returned from LLM':
+        print("Full LLM Response Data:", llm_response)  # Inspect the full LLM response for any helpful info
 
-        response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\n{image_response}\n\nLLM Response:\n{message}"
-        return response
-    else:
-        return "Please upload an image."
+    response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\nLLM Response:\n{message}"
+    return response
 
 # Gradio interface
 iface = gr.Interface(
@@ -143,14 +230,10 @@ iface = gr.Interface(
             label="Query Type",
             placeholder="Describe the type of diagnosis or information needed..."
         ),
-        gr.Image(
-            type="pil",
-            label="Upload an MRI Image",
-        )
     ],
     outputs=gr.Textbox(label="Response", placeholder="The response will appear here..."),
-    title="Medical Diagnosis with MRI and LLM",
-    description="Upload MRI images and provide patient information for a combined CNN model and LLM analysis."
+    title="Medical Diagnosis with LLM",
+    description="Provide patient information and a query type for analysis by the LLM."
 )
 
 iface.launch()