import requests import gradio as gr import logging import json import tf_keras import tensorflow_hub as hub import numpy as np from PIL import Image import io import os # Set up logging with more detailed format logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) 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 } response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body) response.raise_for_status() return response.json()['data']['id'] except requests.exceptions.RequestException as e: logger.error(f"Error creating chat session: {str(e)}") 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' } structured_query = f""" Based on the following patient information, provide a detailed medical analysis in JSON format: {query} Return only valid JSON with these fields: - diagnosis_details - probable_diagnoses (array) - treatment_plans (array) - lifestyle_modifications (array) - medications (array of objects with name and dosage) - additional_tests (array) - precautions (array) - follow_up (string) """ submit_query_body = { "endpointId": "predefined-openai-gpt4o", "query": structured_query, "pluginIds": ["plugin-1712327325", "plugin-1713962163"], "responseMode": "sync" } response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body) response.raise_for_status() return response.json() except requests.exceptions.RequestException as e: logger.error(f"Error submitting query: {str(e)}") raise def extract_json_from_answer(answer, image_analysis): """Extract and clean JSON from the LLM response and append image analysis results.""" try: # Try to parse the JSON answer directly json_data = json.loads(answer) except json.JSONDecodeError: try: # If that fails, try to find JSON content and parse it start_idx = answer.find('{') end_idx = answer.rfind('}') + 1 if start_idx != -1 and end_idx != 0: json_str = answer[start_idx:end_idx] json_data = json.loads(json_str) else: raise ValueError("Failed to locate JSON in the answer") except (json.JSONDecodeError, ValueError) as e: logger.error(f"Failed to parse JSON from response: {str(e)}") raise # Append the image analysis data if image_analysis: json_data["image_analysis"] = { "prediction": image_analysis["prediction"], "confidence": f"{image_analysis['confidence']:.2f}%" # Format confidence as percentage } return json_data def load_model(): try: model_path = 'model_epoch_01.h5.keras' # Check if model file exists if not os.path.exists(model_path): raise FileNotFoundError(f"Model file not found at {model_path}") logger.info(f"Attempting to load model from {model_path}") # Define custom objects dictionary custom_objects = { 'KerasLayer': hub.KerasLayer # Add more custom objects if needed } # Try loading with different configurations try: logger.info("Attempting to load model with custom objects...") model = tf_keras.models.load_model(model_path, custom_objects={'KerasLayer': hub.KerasLayer}) except Exception as e: logger.error(f"Failed to load with custom objects: {str(e)}") logger.info("Attempting to load model without custom objects...") model = tf_keras.models.load_model(model_path) # Verify model loaded correctly if model is None: raise ValueError("Model loading returned None") # Print model summary for debugging model.summary() logger.info("Model loaded successfully") return model except Exception as e: logger.error(f"Error loading model: {str(e)}") logger.error(f"Model loading failed with exception type: {type(e)}") raise # Initialize the model globally try: logger.info("Initializing model...") model = load_model() logger.info("Model initialization completed") except Exception as e: logger.error(f"Failed to initialize model: {str(e)}") model = None def preprocess_image(image): try: # Log image shape and type for debugging #logger.info(f"Input image shape: {image.}, dtype: {image.dtype}") image = image.convert('RGB') image = image.resize((256, 256)) image = np.array(image) # Normalize pixel values image = image / 255.0 # Add batch dimension image = np.expand_dims(image, axis=0) logger.info(f"Final preprocessed image shape: {image.shape}") return image except Exception as e: logger.error(f"Error preprocessing image: {str(e)}") raise def gradio_interface(patient_info, image): try: if model is None: logger.error("Model is not initialized") return json.dumps({ "error": "Model initialization failed. Please check the logs for details.", "status": "error" }, indent=2) classes = ["Alzheimer's", "Normal", "Stroke", "Tumor"] # Process image if provided image_analysis = None if image is not None: logger.info("Processing uploaded image") # Preprocess image processed_image = preprocess_image(image) # Get model prediction logger.info("Running model prediction") prediction = model.predict(processed_image) logger.info(f"Raw prediction shape: {prediction.shape}") logger.info(f"Prediction: {prediction}") # Format prediction results image_analysis = { "prediction": classes[np.argmax(prediction[0])], "confidence": np.max(prediction[0]) * 100 } logger.info(f"Image analysis results: {image_analysis}") patient_info += f"Prediction based on MRI images: {image_analysis['prediction']}, Confidence: {image_analysis['confidence']}" # Create chat session and submit query session_id = create_chat_session() llm_response = submit_query(session_id, patient_info) if not llm_response or 'data' not in llm_response or 'answer' not in llm_response['data']: raise ValueError("Invalid response structure from LLM") # Extract and clean JSON from the response logger.info(f"llm_response: {llm_response}") logger.info(f"llm_response[data]: {llm_response['data']}") logger.info(f"llm_response[data][answer]: {llm_response['data']['answer']}") json_data = extract_json_from_answer(llm_response['data']['answer'], image_analysis) return json.dumps(json_data, indent=2) except Exception as e: logger.error(f"Error in gradio_interface: {str(e)}") return json.dumps({ "error": str(e), "status": "error", "details": "Check the application logs for more information" }, indent=2) # Gradio interface iface = gr.Interface( fn=gradio_interface, inputs=[ gr.Textbox( label="Patient Information", placeholder="Enter patient details including: symptoms, medical history, current medications, age, gender, and any relevant test results...", lines=5, max_lines=10 ), gr.Image( label="Medical Image", type="pil", interactive=True ) ], outputs=gr.Textbox( label="Medical Analysis", placeholder="JSON analysis will appear here...", lines=15 ), title="Medical Diagnosis Assistant", description="Enter patient information and optionally upload a medical image for analysis." ) if __name__ == "__main__": # Add version information logging logger.info(f"TensorFlow Keras version: {tf_keras.__version__}") logger.info(f"TensorFlow Hub version: {hub.__version__}") logger.info(f"Gradio version: {gr.__version__}") iface.launch( server_name="0.0.0.0", debug=True ) # 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 # } # response = requests.post(create_session_url, headers=create_session_headers, json=create_session_body) # response.raise_for_status() # return response.json()['data']['id'] # except requests.exceptions.RequestException as e: # logger.error(f"Error creating chat session: {str(e)}") # 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' # } # structured_query = f""" # Based on the following patient information, provide a detailed medical analysis in JSON format: # {query} # Return only valid JSON with these fields: # - diagnosis_details # - probable_diagnoses (array) # - treatment_plans (array) # - lifestyle_modifications (array) # - medications (array of objects with name and dosage) # - additional_tests (array) # - precautions (array) # - follow_up (string) # """ # submit_query_body = { # "endpointId": "predefined-openai-gpt4o", # "query": structured_query, # "pluginIds": ["plugin-1712327325", "plugin-1713962163"], # "responseMode": "sync" # } # response = requests.post(submit_query_url, headers=submit_query_headers, json=submit_query_body) # response.raise_for_status() # return response.json() # except requests.exceptions.RequestException as e: # logger.error(f"Error submitting query: {str(e)}") # raise # def extract_json_from_answer(answer): # """Extract and clean JSON from the LLM response""" # try: # # First try to parse the answer directly # return json.loads(answer) # except json.JSONDecodeError: # try: # # If that fails, try to find JSON content and parse it # start_idx = answer.find('{') # end_idx = answer.rfind('}') + 1 # if start_idx != -1 and end_idx != 0: # json_str = answer[start_idx:end_idx] # return json.loads(json_str) # except (json.JSONDecodeError, ValueError): # logger.error("Failed to parse JSON from response") # raise # def gradio_interface(patient_info): # try: # session_id = create_chat_session() # llm_response = submit_query(session_id, patient_info) # if not llm_response or 'data' not in llm_response or 'answer' not in llm_response['data']: # raise ValueError("Invalid response structure") # # Extract and clean JSON from the response # json_data = extract_json_from_answer(llm_response['data']['answer']) # # Return clean JSON string without extra formatting # return json.dumps(json_data) # except Exception as e: # logger.error(f"Error in gradio_interface: {str(e)}") # return json.dumps({"error": str(e)}) # # Gradio interface # iface = gr.Interface( # fn=gradio_interface, # inputs=[ # gr.Textbox( # label="Patient Information", # placeholder="Enter patient details including: symptoms, medical history, current medications, age, gender, and any relevant test results...", # lines=5, # max_lines=10 # ) # ], # outputs=gr.Textbox( # label="Medical Analysis", # placeholder="JSON analysis will appear here...", # lines=15 # ), # title="Medical Diagnosis Assistant", # description="Enter detailed patient information to receive a structured medical analysis in JSON format." # ) # if __name__ == "__main__": # iface.launch()