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app.py

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+import os
+import glob
+import time
+import threading
+import requests
+import wikipedia
+import torch
+import cv2
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import base64  # Added import
+
+import gradio as gr
+from ultralytics import YOLO
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from diffusers import MarigoldDepthPipeline  # Updated import for depth model
+from realesrgan import RealESRGANer
+from basicsr.archs.rrdbnet_arch import RRDBNet
+
+# Set environment variable for PyTorch MPS fallback before importing torch
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
+
+# Initialize Models
+def initialize_models():
+    models = {}
+    
+    # Device detection
+    if torch.cuda.is_available():
+        device = 'cuda'
+    elif torch.backends.mps.is_available():
+        device = 'mps'
+    else:
+        device = 'cpu'
+    models['device'] = device
+
+    print(f"Using device: {device}")
+
+    # Initialize the RoBERTa model for question answering
+    try:
+        models['qa_pipeline'] = pipeline(
+            "question-answering", model="deepset/roberta-base-squad2", device=0 if device == 'cuda' else -1)
+        print("RoBERTa QA pipeline initialized.")
+    except Exception as e:
+        print(f"Error initializing the RoBERTa model: {e}")
+        models['qa_pipeline'] = None
+
+    # Initialize the Gemma model
+    try:
+        models['gemma_tokenizer'] = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
+        models['gemma_model'] = AutoModelForCausalLM.from_pretrained(
+            "google/gemma-2-2b-it",
+            device_map="auto",
+            torch_dtype=torch.bfloat16 if device == 'cuda' else torch.float32
+        )
+        print("Gemma model initialized.")
+    except Exception as e:
+        print(f"Error initializing the Gemma model: {e}")
+        models['gemma_model'] = None
+
+    # Initialize the depth estimation model using MarigoldDepthPipeline exactly as per your sample
+    try:
+        if device == 'cuda':
+            models['depth_pipe'] = MarigoldDepthPipeline.from_pretrained(
+                "prs-eth/marigold-depth-lcm-v1-0",
+                variant="fp16",
+                torch_dtype=torch.float16
+            ).to('cuda')
+        else:
+            # For CPU or MPS devices, keep on 'cpu' to avoid unsupported operators
+            models['depth_pipe'] = MarigoldDepthPipeline.from_pretrained(
+                "prs-eth/marigold-depth-lcm-v1-0",
+                torch_dtype=torch.float32
+            ).to('cpu')
+        print("Depth estimation model initialized.")
+    except Exception as e:
+        error_message = f"Error initializing the depth estimation model: {e}"
+        print(error_message)
+        models['depth_pipe'] = None
+        models['depth_init_error'] = error_message  # Store the error message
+
+    # Initialize the upscaling model
+    try:
+        upscaler_model_path = 'weights/RealESRGAN_x4plus.pth'  # Ensure this path is correct
+        if not os.path.exists(upscaler_model_path):
+            print(f"Upscaling model weights not found at {upscaler_model_path}. Please download them.")
+            models['upscaler'] = None
+        else:
+            # Define the model architecture
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
+                            num_block=23, num_grow_ch=32, scale=4)
+
+            # Initialize RealESRGANer
+            models['upscaler'] = RealESRGANer(
+                scale=4,
+                model_path=upscaler_model_path,
+                model=model,
+                pre_pad=0,
+                half=(device == 'cuda'),
+                device=device
+            )
+            print("Real-ESRGAN upscaler initialized.")
+    except Exception as e:
+        print(f"Error initializing the upscaling model: {e}")
+        models['upscaler'] = None
+
+    # Initialize YOLO model
+    try:
+        source_weights_path = "/Users/David/Downloads/WheelOfFortuneLab-DavidDriscoll/Eurybia1.3/mbari_315k_yolov8.pt"
+        if not os.path.exists(source_weights_path):
+            print(f"YOLO weights not found at {source_weights_path}. Please download them.")
+            models['yolo_model'] = None
+        else:
+            models['yolo_model'] = YOLO(source_weights_path)
+            print("YOLO model initialized.")
+    except Exception as e:
+        print(f"Error initializing YOLO model: {e}")
+        models['yolo_model'] = None
+
+    return models
+
+models = initialize_models()
+
+# Utility Functions
+def search_class_description(class_name):
+    wikipedia.set_lang("en")
+    wikipedia.set_rate_limiting(True)
+    description = ""
+
+    try:
+        page = wikipedia.page(class_name)
+        if page:
+            description = page.content[:5000]  # Get more content
+    except Exception as e:
+        print(f"Error fetching description for {class_name}: {e}")
+
+    return description
+
+def search_class_image(class_name):
+    wikipedia.set_lang("en")
+    wikipedia.set_rate_limiting(True)
+    img_url = ""
+
+    try:
+        page = wikipedia.page(class_name)
+        if page:
+            for img in page.images:
+                if img.lower().endswith(('.jpg', '.jpeg', '.png', '.gif')):
+                    img_url = img
+                    break
+    except Exception as e:
+        print(f"Error fetching image for {class_name}: {e}")
+
+    return img_url
+
+def process_image(image):
+    if models['yolo_model'] is None:
+        return None, "YOLO model is not initialized.", "YOLO model is not initialized.", [], None
+    
+    try:
+        if image is None:
+            return None, "No image uploaded.", "No image uploaded.", [], None
+
+        # Convert Gradio Image to OpenCV format
+        image_np = np.array(image)
+        if image_np.dtype != np.uint8:
+            image_np = image_np.astype(np.uint8)
+
+        if len(image_np.shape) != 3 or image_np.shape[2] != 3:
+            return None, "Invalid image format. Please upload a RGB image.", "Invalid image format. Please upload a RGB image.", [], None
+
+        image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+
+        # Store the original image before drawing bounding boxes
+        original_image_cv = image_cv.copy()
+        original_image_pil = Image.fromarray(cv2.cvtColor(original_image_cv, cv2.COLOR_BGR2RGB))
+
+        # Perform YOLO prediction
+        results = models['yolo_model'].predict(
+            source=image_cv, conf=0.075)[0]  # Lowered the threshold
+
+        bounding_boxes = []
+        image_processed = image_cv.copy()
+
+        if results.boxes is not None:
+            for box in results.boxes:
+                x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
+                class_name = models['yolo_model'].names[int(box.cls)]
+                confidence = box.conf.item() * 100  # Convert to percentage
+
+                bounding_boxes.append({
+                    "coords": (x1, y1, x2, y2),
+                    "class_name": class_name,
+                    "confidence": confidence
+                })
+
+                cv2.rectangle(image_processed, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                cv2.putText(image_processed, f'{class_name} {confidence:.2f}%',
+                            (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX,
+                            0.9, (0, 0, 255), 2)
+
+        # Convert back to PIL Image
+        processed_image = Image.fromarray(cv2.cvtColor(image_processed, cv2.COLOR_BGR2RGB))
+
+        # Prepare detection info
+        if bounding_boxes:
+            detection_info = "\n".join(
+                [f'{box["class_name"]}: {box["confidence"]:.2f}%' for box in bounding_boxes]
+            )
+        else:
+            detection_info = "No detections found."
+
+        # Prepare detection details as Markdown
+        if bounding_boxes:
+            details = ""
+            for idx, box in enumerate(bounding_boxes):
+                class_name = box['class_name']
+                confidence = box['confidence']
+                description = search_class_description(class_name)
+                img_url = search_class_image(class_name)
+                img_md = ""
+                if img_url:
+                    try:
+                        headers = {
+                            'User-Agent': 'MyApp/1.0 (https://example.com/contact; [email protected])'
+                        }
+                        response = requests.get(img_url, headers=headers, timeout=10)
+                        img_data = response.content
+                        img = Image.open(BytesIO(img_data)).convert("RGB")
+                        img.thumbnail((400, 400))  # Resize for faster loading
+                        buffered = BytesIO()
+                        img.save(buffered, format="PNG")
+                        img_str = base64.b64encode(buffered.getvalue()).decode()
+                        img_md = f"![{class_name}](data:image/png;base64,{img_str})\n\n"
+                    except Exception as e:
+                        print(f"Error fetching image for {class_name}: {e}")
+                details += f"### {idx+1}. {class_name} ({confidence:.2f}%)\n\n"
+                if description:
+                    details += f"{description}\n\n"
+                if img_md:
+                    details += f"{img_md}\n\n"
+            detection_details_md = details
+        else:
+            detection_details_md = "No detections to show."
+
+        return processed_image, detection_info, detection_details_md, bounding_boxes, original_image_pil
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None, f"Error processing image: {e}", f"Error processing image: {e}", [], None
+
+def ask_eurybia(question, state):
+    if not question.strip():
+        return "Please enter a valid question.", state
+
+    if not state['bounding_boxes']:
+        return "No detected objects to ask about.", state
+
+    # Combine descriptions of all detected objects as context
+    context = ""
+    for box in state['bounding_boxes']:
+        description = search_class_description(box['class_name'])
+        if description:
+            context += description + "\n"
+
+    if not context.strip():
+        return "No sufficient context available to answer the question.", state
+
+    try:
+        if models['qa_pipeline'] is None:
+            return "QA pipeline is not initialized.", state
+
+        answer = models['qa_pipeline'](question=question, context=context)
+        answer_text = answer['answer'].strip()
+        if not answer_text:
+            return "I couldn't find an answer to that question based on the detected objects.", state
+        return answer_text, state
+    except Exception as e:
+        print(f"Error during question answering: {e}")
+        return f"Error during question answering: {e}", state
+
+def enhance_image(cropped_image_pil):
+    if models['upscaler'] is None:
+        return None, "Upscaling model is not initialized."
+    
+    try:
+        input_image = cropped_image_pil.convert("RGB")
+        img = np.array(input_image)
+
+        # Run the model to enhance the image
+        output, _ = models['upscaler'].enhance(img, outscale=4)
+
+        enhanced_image = Image.fromarray(output)
+
+        return enhanced_image, "Image enhanced successfully."
+    except Exception as e:
+        print(f"Error during image enhancement: {e}")
+        return None, f"Error during image enhancement: {e}"
+
+def run_depth_prediction(original_image):
+    if models['depth_pipe'] is None:
+        error_msg = models.get('depth_init_error', "Depth estimation model is not initialized.")
+        return None, error_msg
+
+    try:
+        if original_image is None:
+            return None, "No image uploaded for depth prediction."
+
+        # Prepare the image
+        input_image = original_image.convert("RGB")
+
+        # Run the depth pipeline
+        result = models['depth_pipe'](input_image)
+
+        # Access the depth prediction
+        depth_prediction = result.prediction  # Adjust based on sample code
+
+        # Visualize the depth map
+        vis_depth = models['depth_pipe'].image_processor.visualize_depth(depth_prediction)
+        
+        # Ensure vis_depth is a list and extract the first image
+        if isinstance(vis_depth, list) and len(vis_depth) > 0:
+            vis_depth_image = vis_depth[0]
+        else:
+            vis_depth_image = vis_depth  # Fallback if not a list
+
+        return vis_depth_image, "Depth prediction completed."
+    except Exception as e:
+        print(f"Error during depth prediction: {e}")
+        return None, f"Error during depth prediction: {e}"
+
+# Gradio Interface Components
+with gr.Blocks() as demo:
+    gr.Markdown("# Eurybia Mini - Object Detection and Analysis Tool")
+
+    with gr.Tab("Upload & Process"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload Image")
+                process_button = gr.Button("Process Image")
+                clear_button = gr.Button("Clear")
+            with gr.Column():
+                processed_image = gr.Image(type="pil", label="Processed Image")
+                detection_info = gr.Textbox(label="Detection Information", lines=10)
+
+    with gr.Tab("Detection Details"):
+        with gr.Accordion("Click to see detection details", open=False):
+            detection_details_md = gr.Markdown("No detections to show.")
+
+    with gr.Tab("Ask Eurybia"):
+        with gr.Row():
+            with gr.Column():
+                question_input = gr.Textbox(label="Ask a question about the detected objects")
+                ask_button = gr.Button("Ask Eurybia")
+            with gr.Column():
+                answer_output = gr.Markdown(label="Eurybia's Answer")
+
+    with gr.Tab("Depth Estimation"):
+        with gr.Row():
+            with gr.Column():
+                depth_button = gr.Button("Run Depth Prediction")
+            with gr.Column():
+                depth_output = gr.Image(type="pil", label="Depth Map")
+                depth_status = gr.Textbox(label="Status", lines=2)
+        
+        # Display error message if depth estimation model failed to initialize
+        if models.get('depth_init_error'):
+            gr.Markdown(f"**Depth Estimation Initialization Error:** {models['depth_init_error']}")
+
+    with gr.Tab("Enhance Detected Objects"):
+        if models['yolo_model'] is not None and models['upscaler'] is not None:
+            with gr.Row():
+                detected_objects = gr.Dropdown(choices=[], label="Select Detected Object", interactive=True)
+                enhance_btn = gr.Button("Enhance Image")
+            with gr.Column():
+                enhanced_image = gr.Image(type="pil", label="Enhanced Image")
+                enhance_status = gr.Textbox(label="Status", lines=2)
+        else:
+            gr.Markdown("**Warning:** YOLO model or Upscaling model is not initialized. Image enhancement functionality will be unavailable.")
+
+    with gr.Tab("Credits"):
+        gr.Markdown("""
+# Credits and Licensing Information
+
+This project utilizes various open-source libraries, tools, pretrained models, and datasets. Below is the list of components used and their respective credits/licenses:
+
+## Libraries
+- **Python** - Python Software Foundation License (PSF License)
+- **Gradio** - Licensed under the Apache License 2.0
+- **Torch (PyTorch)** - Licensed under the BSD 3-Clause License
+- **OpenCV (cv2)** - Licensed under the Apache License 2.0
+- **NumPy** - Licensed under the BSD License
+- **Pillow (PIL)** - Licensed under the HPND License
+- **Requests** - Licensed under the Apache License 2.0
+- **Wikipedia API** - Licensed under the MIT License
+- **Transformers** - Licensed under the Apache License 2.0
+- **Diffusers** - Licensed under the Apache License 2.0
+- **Real-ESRGAN** - Licensed under the MIT License
+- **BasicSR** - Licensed under the Apache License 2.0
+- **Ultralytics YOLO** - Licensed under the GPL-3.0 License
+
+## Pretrained Models
+- **deepset/roberta-base-squad2 (RoBERTa)** - Model provided by Hugging Face under the Apache License 2.0.
+- **google/gemma-2-2b-it** - Model provided by Hugging Face under the Apache License 2.0.
+- **prs-eth/marigold-depth-lcm-v1-0** - Licensed under the Apache License 2.0.
+- **Real-ESRGAN model weights (RealESRGAN_x4plus.pth)** - Distributed under the MIT License.
+- **FathomNet MBARI 315K YOLOv8 Model**:
+  - **Dataset**: Sourced from [FathomNet](https://fathomnet.org).
+  - **Model**: Derived from MBARI’s curated dataset of 315,000 marine annotations.
+  - **License**: Dataset and models adhere to MBARI’s Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
+
+## Datasets
+- **FathomNet MBARI Dataset**:
+  - A large-scale dataset for marine biodiversity image annotations.
+  - All content adheres to the [CC BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/).
+
+## Acknowledgments
+- **Ultralytics YOLO**: For the YOLOv8 architecture used for object detection.
+- **FathomNet and MBARI**: For providing the marine dataset and annotations that support object detection in underwater imagery.
+- **Gradio**: For providing an intuitive interface for machine learning applications.
+- **Hugging Face**: For pretrained models and pipelines (e.g., Transformers, Diffusers).
+- **Real-ESRGAN**: For image enhancement and upscaling models.
+- **Wikipedia API**: For fetching object descriptions and images.
+""")
+
+    # Hidden state to store bounding boxes, original and processed images
+    state = gr.State({"bounding_boxes": [], "last_image": None, "original_image": None})
+
+    # Event Handlers
+    def on_process_image(image, state):
+        processed_img, info, details, bounding_boxes, original_image_pil = process_image(image)
+        if processed_img is not None:
+            # Update the state with new bounding boxes and images
+            state['bounding_boxes'] = bounding_boxes
+            state['last_image'] = processed_img
+            state['original_image'] = original_image_pil
+            # Update the dropdown choices for detected objects
+            choices = [f"{idx+1}. {box['class_name']} ({box['confidence']:.2f}%)" for idx, box in enumerate(bounding_boxes)]
+        else:
+            choices = []
+        return processed_img, info, details, gr.update(choices=choices), state
+
+    process_button.click(
+        on_process_image,
+        inputs=[image_input, state],
+        outputs=[processed_image, detection_info, detection_details_md, detected_objects, state]
+    )
+
+    def on_clear(state):
+        state = {"bounding_boxes": [], "last_image": None, "original_image": None}
+        return None, "No detections found.", "No detections to show.", gr.update(choices=[]), state
+
+    clear_button.click(
+        on_clear,
+        inputs=state,
+        outputs=[processed_image, detection_info, detection_details_md, detected_objects, state]
+    )
+
+    def on_ask_eurybia(question, state):
+        answer, state = ask_eurybia(question, state)
+        return answer, state
+
+    ask_button.click(
+        on_ask_eurybia,
+        inputs=[question_input, state],
+        outputs=[answer_output, state]
+    )
+
+    def on_depth_prediction(state):
+        original_image = state.get('original_image')
+        depth_img, status = run_depth_prediction(original_image)
+        return depth_img, status
+
+    depth_button.click(
+        on_depth_prediction,
+        inputs=state,
+        outputs=[depth_output, depth_status]
+    )
+
+    def on_enhance_image(selected_object, state):
+        if not selected_object:
+            return None, "No object selected.", state
+
+        try:
+            idx = int(selected_object.split('.')[0]) - 1
+            box = state['bounding_boxes'][idx]
+            class_name = box['class_name']
+            x1, y1, x2, y2 = box['coords']
+
+            if not state.get('last_image'):
+                return None, "Processed image is not available.", state
+
+            # Ensure processed_image is stored in state
+            processed_img_pil = state['last_image']
+            if not isinstance(processed_img_pil, Image.Image):
+                return None, "Processed image is in an unsupported format.", state
+
+            # Convert processed_image to OpenCV format with checks
+            processed_img_cv = np.array(processed_img_pil)
+            if processed_img_cv.dtype != np.uint8:
+                processed_img_cv = processed_img_cv.astype(np.uint8)
+
+            if len(processed_img_cv.shape) != 3 or processed_img_cv.shape[2] != 3:
+                return None, "Invalid processed image format.", state
+
+            processed_img_cv = cv2.cvtColor(processed_img_cv, cv2.COLOR_RGB2BGR)
+
+            # Crop the detected object from the processed image
+            cropped_img_cv = processed_img_cv[y1:y2, x1:x2]
+            if cropped_img_cv.size == 0:
+                return None, "Cropped image is empty.", state
+
+            cropped_img_pil = Image.fromarray(cv2.cvtColor(cropped_img_cv, cv2.COLOR_BGR2RGB))
+
+            # Enhance the cropped image
+            enhanced_img, status = enhance_image(cropped_img_pil)
+            return enhanced_img, status, state
+        except Exception as e:
+            return None, f"Error: {e}", state
+
+    if models['yolo_model'] is not None and models['upscaler'] is not None:
+        enhance_btn.click(
+            on_enhance_image,
+            inputs=[detected_objects, state],
+            outputs=[enhanced_image, enhance_status, state]
+        )
+
+    # Optional: Add a note if the depth model isn't initialized
+    if models['depth_pipe'] is None and not models.get('depth_init_error'):
+        gr.Markdown("**Warning:** Depth estimation model is not initialized. Depth prediction functionality will be unavailable.")
+
+    # Optional: Add a note if the upscaler isn't initialized
+    if models['upscaler'] is None:
+        gr.Markdown("**Warning:** Upscaling model is not initialized. Image enhancement functionality will be unavailable.")
+
+# Launch the Gradio app
+demo.launch()

requirements.txt

@@ -0,0 +1,31 @@
+# Core Libraries
+numpy==1.23.5
+opencv-python==4.8.0.74
+Pillow==10.0.0
+requests==2.31.0
+wikipedia==1.4.0
+
+# PyTorch
+torch==2.0.1
+
+# Hugging Face Ecosystem
+transformers==4.31.0
+huggingface-hub==0.14.1
+diffusers==0.19.3
+accelerate==0.20.3
+
+# Real-ESRGAN and Dependencies
+realesrgan==0.3.0
+basicsr==1.4.2
+
+# Ultralytics (YOLO)
+ultralytics==8.0.120
+
+# Gradio
+gradio==3.40.0
+
+# Additional Packages (Ensure Compatibility)
+datasets==2.8.0          # Example version; adjust as needed
+protobuf==3.20.3         # Compatible with <4
+click==8.0.4             # Compatible with <8.1
+pydantic==1.10.7         # Compatible with ~=1.0