app.py

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 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, 3))
        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)
            
        # 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": float(prediction[0][0]),
                "confidence": float(prediction[0][0]) * 100
            }
            logger.info(f"Image analysis results: {image_analysis}")
        
        # Create chat session and submit query
        session_id = create_chat_session()
        llm_response = submit_query(session_id, patient_info, 
                                  json.dumps(image_analysis) if image_analysis else None)
        
        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
        json_data = extract_json_from_answer(llm_response['data']['answer'])
        
        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()