Update README.md

README.md

@@ -33,76 +33,98 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Usage
+
+import os
+from datasets import load_dataset
+
+***Load dataset***
+```
+ds = load_dataset("mychen76/medtrinity_brain_408_hf")
+train=ds["train"]
+
+idx=20
+test_image = test_ds[idx]["image"]
+test_image.resize([350, 350])
+```
+***Load Model***
+```
+import torch
+from PIL import Image
+import matplotlib.pyplot as plt
+import textwrap
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+mode_id_or_path = "mychen76/Florence2-FT-Med-brain-408"
+
+# Load fine-tuned model and processor
+model = AutoModelForCausalLM.from_pretrained(model_id_or_path, trust_remote_code=True).to(device)
+processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
+```
+***Test Model***
+```
+# Function to run the model on an example
+def run_model_inference(task_prompt, text_input, image, device="cpu"):
+
+    if text_input is None:
+        prompt = task_prompt
+    else:
+        prompt = task_prompt + text_input
+
+    # print("PROMPT=",prompt)
+    # Ensure the image is in RGB mode
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
+    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]
+    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
+    return parsed_answer
+```
+***Task-1 CAPTION***
+```
+results = run_model_inference("<CAPTION>",None,test_image)
+print(results)
+```
+Results
+```
+<CAPTION>The image is a non-contrasted CT scan of the brain, showing the abnormal abnormal density, located approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure. The region of interest, located adjacent to the adjacent brain, is indicative of a brain tissue. This abnormal area could be related to the brain structures due to the presence of blood or a mass effect, which is a common feature of adjacent brain structures.'
+```
+***Task-2 CAPTION_DETAILS***
+```
+results = run_model_inference("<CAPTION_DETAILS>",None,test_image)
+print(results)
+```
+Results
+```
+<CAPTION_DETAILS>The image is a non-contrasted CT scan of the brain, showing the intracranial structures without any medical devices present.\n\nREGION OF INTEREST\nThe region of interest, located brain tissue, occupies approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue.\nCONDITION\nThis region's proximity to other brain structures could be related to a mass effect or as a result of a massage, which is indicative of a intracronial pressure.\nThis abnormal area could be indicative of an abnormal area, potentially potentially leading to a potential mass effect on adjacent brain structures.
+```
+***Task-3 REGION_OF_INTEREST***
+```
+results = run_model_inference("<REGION_OF_INTEREST>",None,test_image)
+print(results)
+```
+Results
+```
+<REGION_OF_INTEREST>The region of interest, located adjacent to the brain, occupies approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure.
+```
+***Task-4 OBSERVATION***
+```
+results = run_model_inference("<REGION_OF_INTEREST>",None,test_image)
+print(results)
+```
+Results
+```
+<OBSERVATION>The region of interest, located approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure.
+```
 
 ### Testing Data, Factors & Metrics