Update README.md

README.md

@@ -1,29 +1,81 @@
 ---
-base_model: unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
 library_name: transformers
 model_name: HazardNet-unsloth-v0.4
 tags:
-- generated_from_trainer
-- unsloth
 - trl
 - sft
 licence: license
+license: apache-2.0
+datasets:
+- Tami3/HazardQA
+language:
+- en
+pipeline_tag: visual-question-answering
 ---
 
 # Model Card for HazardNet-unsloth-v0.4
 
-This model is a fine-tuned version of [unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit).
+This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
 
 ```python
 from transformers import pipeline
+from PIL import Image
+import requests
+from io import BytesIO
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="Tami3/HazardNet-unsloth-v0.4", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+# Initialize the Visual Question Answering pipeline with HazardNet
+hazard_vqa = pipeline(
+    "visual-question-answering",
+    model="Tami3/HazardNet"
+)
+
+# Function to load image from a local path or URL
+def load_image(image_path=None, image_url=None):
+    if image_path:
+        return Image.open(image_path).convert("RGB")
+    elif image_url:
+        response = requests.get(image_url)
+        response.raise_for_status()  # Ensure the request was successful
+        return Image.open(BytesIO(response.content)).convert("RGB")
+    else:
+        raise ValueError("Provide either image_path or image_url.")
+
+# Example 1: Loading image from a local file
+try:
+    image_path = "path_to_your_ego_car_image.jpg"  # Replace with your local image path
+    image = load_image(image_path=image_path)
+except Exception as e:
+    print(f"Error loading image from path: {e}")
+    # Optionally, handle the error or exit
+
+# Example 2: Loading image from a URL
+# try:
+#     image_url = "https://example.com/path_to_image.jpg"  # Replace with your image URL
+#     image = load_image(image_url=image_url)
+# except Exception as e:
+#     print(f"Error loading image from URL: {e}")
+#     # Optionally, handle the error or exit
+
+# Define your question about potential hazards
+question = "Is there a pedestrian crossing the road ahead?"
+
+# Get the answer from the HazardNet pipeline
+try:
+    result = hazard_vqa(question=question, image=image)
+    answer = result.get('answer', 'No answer provided.')
+    score = result.get('score', 0.0)
+    
+    print("Question:", question)
+    print("Answer:", answer)
+    print("Confidence Score:", score)
+except Exception as e:
+    print(f"Error during inference: {e}")
+    # Optionally, handle the error or exit
 ```
 
 ## Training procedure