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app.py

@@ -1,399 +1,3 @@
-# # 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
-
-#             # 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
@@ -423,9 +27,18 @@ st.markdown("""
             padding-right: 3rem;
         }
         .title {
-            font-size: 2.5rem;
+            font-size: 3.2rem;
             text-align: center;
-            color: #FF6347;
+            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;
@@ -462,6 +75,59 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
+# # 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: 2.5rem;
+#             text-align: center;
+#             color: #FF6347;
+#         }
+#         .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():
@@ -520,7 +186,8 @@ def apply_transform(image, size_mode=512):
     return transformed["image"]
 
 # Streamlit UI with Colorful Title and Emojis
-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("<h1 class='title'>🩺 Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding 🚀</h1>", 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
@@ -692,7 +359,10 @@ with col2:
     - 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("""
+    <img src="//www.clustrmaps.com/map_v2.png?d=uM9v_RTadJ3hLvNbBSQ2PZ0KNPABbilkZgDyiXmuC0M&cl=ffffff" 
+         style="position:absolute;top:-9999px;left:-9999px;width:1px;height:1px;visibility:hidden;opacity:0;pointer-events:none;z-index:-1;display:none;" />
+""", unsafe_allow_html=True)
 # Run inference after upload
 if st.button("Run Inference 🏃‍♂️"):
     if image is None: