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app.py

@@ -1,12 +1,399 @@
-# import os
-# import shutil
+# # import os
+# # import shutil
+
+# # # Clean and recreate HF cache directory
+# # cache_dir = "/tmp/hf_cache"
+# # if os.path.exists(cache_dir):
+# #     shutil.rmtree(cache_dir)
+# # os.makedirs(cache_dir, exist_ok=True)
+# # os.environ["HF_HOME"] = cache_dir
+
+# import streamlit as st
+# from PIL import Image
+# import torch
+# from transformers import AutoModelForCausalLM, AutoProcessor
+# import numpy as np
+# import supervision as sv
+# import albumentations as A
+# import cv2
+# from transformers import AutoConfig
+# import yaml
+
+# # Set Streamlit page configuration for a wide layout
+# st.set_page_config(layout="wide")
+
+# # Custom CSS for better layout and mobile responsiveness
+# st.markdown("""
+#     <style>
+#         .main {
+#             max-width: 1200px;  /* Max width for content */
+#             margin: 0 auto;
+#         }
+#         .block-container {
+#             padding-top: 2rem;
+#             padding-bottom: 2rem;
+#             padding-left: 3rem;
+#             padding-right: 3rem;
+#         }
+#         .title {
+#             font-size: 3.2rem;
+#             text-align: center;
+#             background: linear-gradient(135deg, #0575e6 0%, #ff0080 50%, #7928ca 100%);
+#             -webkit-background-clip: text;
+#             -webkit-text-fill-color: transparent;
+#             background-clip: text;
+#         }
+        
+#         @keyframes gradientShift {
+#             0% { background-position: 0% 50%; }
+#             50% { background-position: 100% 50%; }
+#             100% { background-position: 0% 50%; }
+#         }
+#         .subheader {
+#             font-size: 1.5rem;
+#             margin-bottom: 20px;
+#         }
+#         .btn {
+#             font-size: 1.1rem;
+#             padding: 10px 20px;
+#             background-color: #FF6347;
+#             color: white;
+#             border-radius: 5px;
+#             border: none;
+#             cursor: pointer;
+#         }
+#         .btn:hover {
+#             background-color: #FF4500;
+#         }
+#         .column-spacing {
+#             display: flex;
+#             justify-content: space-between;
+#         }
+#         .col-half {
+#             width: 48%;
+#         }
+#         .col-full {
+#             width: 100%;
+#         }
+#         .instructions {
+#             padding: 20px;
+#             background-color: #f9f9f9;
+#             border-radius: 8px;
+#             box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
+#         }
+#     </style>
+# """, unsafe_allow_html=True)
+
+# # Load Model and Processor
+# @st.cache_resource
+# def load_model():
+#     MODEL_NAME = 'Anonymous-AC/K2Sight-Lite'
+#     DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#     model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True).to(DEVICE)
+#     processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
+#     processor.image_processor.size = 512
+#     processor.image_processor.crop_size = 512
+
+#     return model, processor, DEVICE
+
+# model, processor, DEVICE = load_model()
+
+# # Load Definitions
+# @st.cache_resource
+# def load_definitions():
+#     vindr_path = 'configs/vindr_definition.yaml'
+#     padchest_path = 'configs/padchest_definition.yaml'
+#     prompt_path = 'examples/prompt.yaml'
+
+#     with open(vindr_path, 'r') as file:
+#         vindr_definitions = yaml.safe_load(file)
+#     with open(padchest_path, 'r') as file:
+#         padchest_definitions = yaml.safe_load(file)
+#     with open(prompt_path, 'r') as file:
+#         prompt_definitions = yaml.safe_load(file)
+
+#     return vindr_definitions, padchest_definitions, prompt_definitions
+
+# vindr_definitions, padchest_definitions, prompt_definitions = load_definitions()
+
+# dataset_options = {"Vindr": vindr_definitions, "PadChest": padchest_definitions}
+
+# def load_example_images():
+#     return list(prompt_definitions.keys())
+
+# example_images = load_example_images()
+
+# def apply_transform(image, size_mode=512):
+#     pad_resize_transform = A.Compose([
+#         A.LongestMaxSize(max_size=size_mode, interpolation=cv2.INTER_AREA),
+#         A.PadIfNeeded(min_height=size_mode, min_width=size_mode, border_mode=cv2.BORDER_CONSTANT, value=(0, 0, 0)),
+#         A.Resize(height=512, width=512, interpolation=cv2.INTER_AREA),
+#     ])
+#     image_np = np.array(image)
+#     transformed = pad_resize_transform(image=image_np)
+#     return transformed["image"]
+
+# # Streamlit UI with Colorful Title and Emojis
+# # st.markdown("<div style='text-align: center;'><span style='font-size: 3rem;'>🩺</span></div>", unsafe_allow_html=True)
+# st.markdown("<h1 class='title'>Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding</h1>", unsafe_allow_html=True)
+# # st.markdown("<div style='text-align: center;'><span style='font-size: 3rem;'>🚀</span></div>", unsafe_allow_html=True)
+# st.markdown(
+#     "<p style='text-align: center; font-size: 18px;'>Welcome to a simple demo of our work! 🎉 Choose an example or upload your own image to get started! 👇</p>", 
+#     unsafe_allow_html=True
+# )
+
+# # Display Example Images First
+# st.subheader("🌄 Example Images")
+# selected_example = st.selectbox("Choose an example", example_images)
+# image = Image.open(selected_example).convert("RGB")
+# example_diseases = prompt_definitions.get(selected_example, [])
+# st.write("**Associated Diseases:**", ", ".join(example_diseases))
+
+# # Layout for Original Image and Instructions
+# col1, col2 = st.columns([1, 2])
+
+# # Left column for original image
+# with col1:
+#     st.image(image, caption=f"Original Example Image: {selected_example}", width=400)
+
+# # Right column for Instructions and Run Inference Button
+# with col2:
+#     st.subheader("⚙️ Instructions to Get Started:")
+#     st.write("""
+#         - **Run Inference**: Click the "Run Inference on Example" button to process the image and display the results.
+#         - **Choose an Example**: 🌄 Select an example image from the dataset to view its associated diseases.
+#         - **Upload Your Own Image**: 📤 Upload an image of your choice to analyze it for diseases.
+#         - **Select Dataset**: 📚 Choose between available datasets (Vindr or PadChest) for disease information.
+#         - **Select Disease**: 🦠 Pick the disease to be analyzed from the list of diseases in the selected dataset.
+#     """)
+
+#     st.subheader("⚠️ Warning:")
+#     st.write("""
+#     - **🚫 Please avoid uploading non-frontal chest X-ray images.** Our model has been specifically trained on **frontal chest X-ray images** only.
+#     - This demo is intended for **🔬 research purposes only** and should **❌ not be used for medical diagnoses**.
+#     - The model’s responses may contain **<span style='color:#dc3545; font-weight:bold;'>🤖 hallucinations or incorrect information</span>**. 
+#     - Always consult a **<span style='color:#dc3545; font-weight:bold;'>👨‍⚕️ medical professional</span>** for accurate diagnosis and advice.
+# """, unsafe_allow_html=True)
+
+
+#     st.markdown("</div>", unsafe_allow_html=True)
+
+# # Run Inference Button
+# if st.button("Run Inference on Example", key="example"):
+#     if image is None:
+#         st.error("❌ Please select an example image first.")
+#     else:
+#         # Use the selected example's disease and definition for inference
+#         disease_choice = example_diseases[0] if example_diseases else ""
+#         definition = vindr_definitions.get(disease_choice, padchest_definitions.get(disease_choice, ""))
+
+#         # Generate the prompt for the model
+#         det_obj = f"{disease_choice} means {definition}."
+#         st.write(f"**Definition:** {definition}")
+#         prompt = f"Locate the phrases in the caption: {det_obj}."
+#         prompt = f"<CAPTION_TO_PHRASE_GROUNDING>{prompt}"
+
+#         # Prepare the image and input
+#         np_image = np.array(image)
+#         inputs = processor(text=[prompt], images=[np_image], return_tensors="pt", padding=True).to(DEVICE)
+
+#         with st.spinner("Processing... ⏳"):
+#             outputs = model.generate(
+#                 input_ids=inputs["input_ids"],
+#                 pixel_values=inputs["pixel_values"],
+#                 max_new_tokens=1024,
+#                 num_beams=3,
+#                 output_scores=True,  # Make sure we get the scores/logits
+#                 return_dict_in_generate=True  # Ensures you get both sequences and scores in the output
+#                 )
+            
+
+#             # Ensure transition_scores is properly extracted
+#             transition_scores = model.compute_transition_scores(
+#                 outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
+#             )
+
+#             # Get the generated token IDs (ignoring the input tokens part)
+#             generated_ids = outputs.sequences
+#             generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+
+#             # Get input length
+#             input_length = inputs.input_ids.shape[1]
+#             generated_tokens = outputs.sequences
+
+#             # Calculate output length (number of generated tokens)
+#             output_length = np.sum(transition_scores.cpu().numpy() < 0, axis=1)
+
+#             # Get length penalty
+#             length_penalty = model.generation_config.length_penalty
+
+#             # Calculate total score for the generated sentence
+#             reconstructed_scores = transition_scores.cpu().sum(axis=1) / (output_length**length_penalty)
+
+#             # Convert log-probability to probability (0-1 range)
+#             probabilities = np.exp(reconstructed_scores.cpu().numpy())
+
+#             # Streamlit UI to display the result
+#             st.markdown(f"**🎯 Probability of the Results:** <span style='color:#28a745; font-size:24px; font-weight:bold;'>{probabilities[0] * 100:.2f}%</span>", unsafe_allow_html=True)
+                
+
+#             predictions = processor.post_process_generation(generated_text, task="<CAPTION_TO_PHRASE_GROUNDING>", image_size=np_image.shape[:2])
+
+#             detection = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, predictions, resolution_wh=np_image.shape[:2])
+
+#             # Annotate the image with bounding boxes and labels
+#             bounding_box_annotator = sv.BoundingBoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#             label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#             image_with_predictions = bounding_box_annotator.annotate(np_image.copy(), detection)
+#             image_with_predictions = label_annotator.annotate(image_with_predictions, detection)
+#             annotated_image = Image.fromarray(image_with_predictions.astype(np.uint8))
+
+#             # Display the original and result images side by side
+#             col1, col2 = st.columns([1, 1])
+
+#             with col1:
+#                 st.image(image, caption=f"Original Image: {selected_example}", width=400)
+
+#             with col2:
+#                 st.image(annotated_image, caption="Inference Results 🖼️", width=400)
+
+#             # Display the generated text
+#             st.write("**Generated Text:**", generated_text)
+
+# # Upload Image section
+# st.subheader("📤 Upload Your Own Image")
+
+# col1, col2 = st.columns([1, 1])
+# with col1:
+#     dataset_choice = st.selectbox("Select Dataset 📚", options=list(dataset_options.keys()))
+#     disease_options = list(dataset_options[dataset_choice].keys())
+# with col2:
+#     disease_choice = st.selectbox("Select Disease 🦠", options=disease_options)
+
+# uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
+
+
+# col1, col2 = st.columns([1, 2])
+
+# with col1:
+#     # Handle file upload
+#     if uploaded_file:
+#         image = Image.open(uploaded_file).convert("RGB")
+#         image = apply_transform(image)  # Ensure the uploaded image is transformed correctly
+#         st.image(image, caption="Uploaded Image", width=400)
+
+#         # Let user select dataset and disease dynamically
+#         disease_choice = disease_choice if disease_choice else example_diseases[0]
+
+#         # Get Definition Priority: Dataset -> User Input
+#         definition = vindr_definitions.get(disease_choice, padchest_definitions.get(disease_choice, ""))
+#         if not definition:
+#             definition = st.text_input("Enter Definition Manually 📝", value="")
+
+# with col2:
+#     # Instructions and warnings
+#     st.subheader("⚙️ Instructions to Get Started:")
+#     st.write("""
+#         - **Run Inference**: Click the "Run Inference on Example" button to process the image and display the results.
+#         - **Choose an Example**: 🌄 Select an example image from the dataset to view its associated diseases.
+#         - **Upload Your Own Image**: 📤 Upload an image of your choice to analyze it for diseases.
+#         - **Select Dataset**: 📚 Choose between available datasets (Vindr or PadChest) for disease information.
+#         - **Select Disease**: 🦠 Pick the disease to be analyzed from the list of diseases in the selected dataset.
+#     """)
+
+#     st.subheader("⚠️ Warning:")
+#     st.write("""
+#     - **🚫 Please avoid uploading non-frontal chest X-ray images.** Our model has been specifically trained on **frontal chest X-ray images** only.
+#     - This demo is intended for **🔬 research purposes only** and should **❌ not be used for medical diagnoses**.
+#     - The model’s responses may contain **<span style='color:#dc3545; font-weight:bold;'>🤖 hallucinations or incorrect information</span>**. 
+#     - Always consult a **<span style='color:#dc3545; font-weight:bold;'>👨‍⚕️ medical professional</span>** for accurate diagnosis and advice.
+# """, unsafe_allow_html=True)
+
+# # Run inference after upload
+# if st.button("Run Inference 🏃‍♂️"):
+#     if image is None:
+#         st.error("❌ Please upload an image or select an example.")
+#     else:
+#         det_obj = f"{disease_choice} means {definition}."
+#         st.write(f"**Definition:** {definition}")
+
+#         # Construct Prompt with Disease Definition
+#         prompt = f"Locate the phrases in the caption: {det_obj}."
+#         prompt = f"<CAPTION_TO_PHRASE_GROUNDING>{prompt}"
+
+#         np_image = np.array(image)
+#         inputs = processor(text=[prompt], images=[np_image], return_tensors="pt", padding=True).to(DEVICE)
+
+#         with st.spinner("Processing... ⏳"):
+#             # generated_ids = model.generate(input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3)
+#             # generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+            
+#             outputs = model.generate(
+#                 input_ids=inputs["input_ids"],
+#                 pixel_values=inputs["pixel_values"],
+#                 max_new_tokens=1024,
+#                 num_beams=3,
+#                 output_scores=True,  # Make sure we get the scores/logits
+#                 return_dict_in_generate=True  # Ensures you get both sequences and scores in the output
+#                 )
+
+#             transition_scores = model.compute_transition_scores(
+#                 outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
+#             )
+
+#             # Get the generated token IDs (ignoring the input tokens part)
+#             generated_ids = outputs.sequences
+#             generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+
+#             # Get input length
+#             input_length = inputs.input_ids.shape[1]
+
+#             # Extract generated tokens (ignoring the input tokens)
+#             # generated_tokens = outputs.sequences[:, input_length:]
+#             generated_tokens = outputs.sequences
+
+#             # Calculate output length (number of generated tokens)
+#             output_length = np.sum(transition_scores.cpu().numpy() < 0, axis=1)
+
+#             # Get length penalty
+#             length_penalty = model.generation_config.length_penalty
 
-# # Clean and recreate HF cache directory
-# cache_dir = "/tmp/hf_cache"
-# if os.path.exists(cache_dir):
-#     shutil.rmtree(cache_dir)
-# os.makedirs(cache_dir, exist_ok=True)
-# os.environ["HF_HOME"] = cache_dir
+#             # Calculate total score for the generated sentence
+#             reconstructed_scores = transition_scores.cpu().sum(axis=1) / (output_length**length_penalty)
+
+#             # Convert log-probability to probability (0-1 range)
+#             probabilities = np.exp(reconstructed_scores.cpu().numpy())
+
+#             # Streamlit UI to display the result
+            
+#             # st.write(f"**Probability of the Results (0-1):** {probabilities[0]:.4f}")
+#             st.markdown(f"**🎯 Probability of the Results:** <span style='color:green; font-size:24px; font-weight:bold;'>{probabilities[0] * 100:.2f}%</span>", unsafe_allow_html=True)
+
+
+
+#             predictions = processor.post_process_generation(generated_text, task="<CAPTION_TO_PHRASE_GROUNDING>", image_size=np_image.shape[:2])
+
+#             detection = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, predictions, resolution_wh=np_image.shape[:2])
+
+#             bounding_box_annotator = sv.BoundingBoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#             label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#             image_with_predictions = bounding_box_annotator.annotate(np_image.copy(), detection)
+#             image_with_predictions = label_annotator.annotate(image_with_predictions, detection)
+#             annotated_image = Image.fromarray(image_with_predictions.astype(np.uint8))
+
+#             # Create two columns to display the original and the results side by side
+#             col1, col2 = st.columns([1, 1])
+
+#             # Left column for original image
+#             with col1:
+#                 st.image(image, caption="Uploaded Image", width=400)
+
+#             # Right column for result image
+#             with col2:
+#                 st.image(annotated_image, caption="Inference Results 🖼️", width=400)
+
+#             # Display the generated text
+#             st.write("**Generated Text:**", generated_text)
 
 import streamlit as st
 from PIL import Image
@@ -36,18 +423,9 @@ st.markdown("""
             padding-right: 3rem;
         }
         .title {
-            font-size: 3.2rem;
+            font-size: 2.5rem;
             text-align: center;
-            background: linear-gradient(135deg, #0575e6 0%, #ff0080 50%, #7928ca 100%);
-            -webkit-background-clip: text;
-            -webkit-text-fill-color: transparent;
-            background-clip: text;
-        }
-        
-        @keyframes gradientShift {
-            0% { background-position: 0% 50%; }
-            50% { background-position: 100% 50%; }
-            100% { background-position: 0% 50%; }
+            color: #FF6347;
         }
         .subheader {
             font-size: 1.5rem;
@@ -87,10 +465,18 @@ st.markdown("""
 # Load Model and Processor
 @st.cache_resource
 def load_model():
-    MODEL_NAME = 'Anonymous-AC/K2Sight-Lite'
+    REVISION = 'refs/pr/6'
+    MODEL_NAME = "Anonymous-AC/K2Sight-Lite"
+    # MODEL_NAME = '/u/home/lj0/Checkpoints/AD-KD-MICCAI25'
     DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True).to(DEVICE)
-    processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
+
+    config_model = AutoConfig.from_pretrained ("microsoft/Florence-2-base-ft", trust_remote_code=True)
+    config_model.vision_config.model_type = "davit"
+
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True, config=config_model).to(DEVICE)
+
+    BASE_PROCESSOR = "microsoft/Florence-2-base-ft"
+    processor = AutoProcessor.from_pretrained(BASE_PROCESSOR, trust_remote_code=True)
     processor.image_processor.size = 512
     processor.image_processor.crop_size = 512
 
@@ -134,9 +520,7 @@ def apply_transform(image, size_mode=512):
     return transformed["image"]
 
 # Streamlit UI with Colorful Title and Emojis
-# st.markdown("<div style='text-align: center;'><span style='font-size: 3rem;'>🩺</span></div>", unsafe_allow_html=True)
-st.markdown("<h1 class='title'>Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding</h1>", unsafe_allow_html=True)
-# st.markdown("<div style='text-align: center;'><span style='font-size: 3rem;'>🚀</span></div>", unsafe_allow_html=True)
+st.markdown("<h1 class='title'>🩺 Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding 🚀</h1>", unsafe_allow_html=True)
 st.markdown(
     "<p style='text-align: center; font-size: 18px;'>Welcome to a simple demo of our work! 🎉 Choose an example or upload your own image to get started! 👇</p>", 
     unsafe_allow_html=True
@@ -397,3 +781,4 @@ if st.button("Run Inference 🏃‍♂️"):
 
 
 
+