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# 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 gradio as gr
import logging
import json
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# API key and user ID for on-demand
api_key = 'KGSjxB1uptfSk8I8A7ciCuNT9Xa3qWC3'
external_user_id = 'plugin-1717464304'
def create_chat_session():
try:
create_session_url = 'https://api.on-demand.io/chat/v1/sessions'
create_session_headers = {
'apikey': api_key,
'Content-Type': 'application/json'
}
create_session_body = {
"pluginIds": [],
"externalUserId": external_user_id
}
logger.info("Creating chat session...")
response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body)
response.raise_for_status() # Raise an exception for bad status codes
response_data = response.json()
logger.info(f"Session created successfully: {json.dumps(response_data, indent=2)}")
session_id = response_data['data']['id']
return session_id
except requests.exceptions.RequestException as e:
logger.error(f"Error creating chat session: {str(e)}")
if hasattr(e.response, 'text'):
logger.error(f"Response content: {e.response.text}")
raise
def submit_query(session_id, query):
try:
submit_query_url = f'https://api.on-demand.io/chat/v1/sessions/{session_id}/query'
submit_query_headers = {
'apikey': api_key,
'Content-Type': 'application/json'
}
submit_query_body = {
"endpointId": "predefined-openai-gpt4o",
"query": query,
"pluginIds": ["plugin-1712327325", "plugin-1713962163"],
"responseMode": "sync"
}
logger.info(f"Submitting query for session {session_id}")
logger.info(f"Query content: {query}")
response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body)
response.raise_for_status()
response_data = response.json()
logger.info(f"Query response received: {json.dumps(response_data, indent=2)}")
return response_data
except requests.exceptions.RequestException as e:
logger.error(f"Error submitting query: {str(e)}")
if hasattr(e.response, 'text'):
logger.error(f"Response content: {e.response.text}")
raise
def gradio_interface(patient_info, query_type):
try:
# Create session
session_id = create_chat_session()
# Construct query
query = f"Patient Info: {patient_info}\nQuery Type: {query_type}"
# Submit query and get response
llm_response = submit_query(session_id, query)
# Enhanced response handling
if not llm_response:
logger.error("Empty response received from LLM")
return "Error: No response received from the LLM service"
# Navigate the response structure with detailed logging
logger.info(f"Processing LLM response: {json.dumps(llm_response, indent=2)}")
if 'data' not in llm_response:
logger.error("Response missing 'data' field")
return f"Error: Unexpected response structure\nFull response: {json.dumps(llm_response, indent=2)}"
message = llm_response.get('data', {}).get('message')
if not message:
logger.error("No message found in response data")
return f"Error: No message in response\nFull response: {json.dumps(llm_response, indent=2)}"
response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n\nLLM Response:\n{message}"
return response
except Exception as e:
logger.error(f"Error in gradio_interface: {str(e)}", exc_info=True)
return f"Error processing request: {str(e)}"
# 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..."
),
],
outputs=gr.Textbox(label="Response", placeholder="The response will appear here..."),
title="Medical Diagnosis with LLM",
description="Provide patient information and a query type for analysis by the LLM."
)
if __name__ == "__main__":
iface.launch() |