Update README.md

README.md

@@ -10,7 +10,65 @@ license: apache-2.0
 metrics:
 - bleu
 library_name: transformers
+datasets:
+- phiyodr/coco2017
 ---
 # My Fine-Tuned Image Captioning Model
 
-This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on a custom dataset. It generates captions for images based on the features in the image.
+This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on a custom dataset. It generates captions for images based on the features in the image.
+
+
+Model Summary
+This model is an image captioning model developed for generating descriptive captions for images. Leveraging Hugging Face’s BLIP model (BlipProcessor and BlipForConditionalGeneration), it accepts an input image and outputs a coherent caption that describes the image’s contents.
+
+Example:
+Input: An image of a dog playing in a garden.
+
+Output: "A dog is playing in a green garden."
+
+Source Model
+This image captioning model is based on the BLIP (Bootstrapping Language-Image Pretraining) model, which is widely used for tasks involving multimodal input, such as image and text. The model architecture is optimized for conditional image captioning.
+
+Dataset
+The model was fine-tuned using a custom dataset suitable for generating descriptive captions.
+
+To use this model's inference endpoint, you will need the Hugging Face Inference API URL and an API token. Replace your_model_name with the name of the model (e.g., quadranttechnologies/Dileep_model) and YOUR_API_TOKEN with your Hugging Face API token.
+
+
+Inference Endpoint
+This model is hosted on Hugging Face and can be accessed via an inference endpoint, allowing users to generate captions for images by making an API call.
+
+How to Use the Endpoint
+To use this model's inference endpoint, you will need the Hugging Face Inference API URL and an API token. Replace quadranttechnologies/qhub-blip-image-captioning-finetuned with the name of the model and YOUR_API_TOKEN with your Hugging Face API token.
+
+
+import requests
+
+# Set your model URL and API token
+API_URL = "https://api-inference.huggingface.co/models/quadranttechnologies/Dileep_model"
+headers = {"Authorization": "Bearer YOUR_API_TOKEN"}
+
+def generate_caption(image_path):
+    # Open the image and read its bytes
+    with open(image_path, "rb") as image_file:
+        image_bytes = image_file.read()
+
+    # Make a POST request to the API endpoint
+    response = requests.post(API_URL, headers=headers, files={"file": image_bytes})
+
+    # Handle response
+    if response.status_code == 200:
+        return response.json().get("generated_text", "No caption generated")
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+# Example usage
+caption = generate_caption("path_to_your_image.jpg")
+print("Generated Caption:", caption)
+
+
+Explanation
+API_URL: This is the endpoint URL for the model. Replace quadranttechnologies/Dileep_model with the actual model name if it’s different.
+Authorization: To authenticate the request, use the Hugging Face API token in the headers.
+POST Request: The image file is sent as a POST request to the endpoint, where the model processes it and returns a caption.
+This endpoint makes it easy to integrate the model into various applications for automated image captioning tasks.