Update app.py

app.py

@@ -4,55 +4,36 @@ import torch
 import requests
 from PIL import Image
 from io import BytesIO
+# Import necessary libraries
+import torch
+from transformers import pipeline
+
+# Check if CUDA (GPU support) is available, for faster processing if you have a GPU
+device = 0 if torch.cuda.is_available() else -1
 
-fashion_items = ['top', 'trousers', 'jumper']
+# Load the BART model for summarization using Hugging Face's pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
 
-# Load model and processor
-model_name = 'Marqo/marqo-fashionSigLIP'
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
-processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+# Function to summarize a given text
+def summarize_text(input_text):
+    # Get the summary from the model
+    summary = summarizer(input_text, max_length=150, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
 
-# Preprocess and normalize text data
-with torch.no_grad():
-    # Ensure truncation and padding are activated
-    processed_texts = processor(
-        text=fashion_items,
-        return_tensors="pt",
-        truncation=True,  # Ensure text is truncated to fit model input size
-        padding=True      # Pad shorter sequences so that all are the same length
-    )['input_ids']
-    
-    text_features = model.get_text_features(processed_texts)
-    text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+# Example text to summarize
+text_to_summarize = """
+The Hugging Face team has developed several transformers architectures that excel in a variety of NLP tasks.
+These architectures are pre-trained on large text corpora and can be fine-tuned for specific tasks like text classification, named entity recognition, and question answering.
+The transformers library provides easy-to-use tools for downloading pre-trained models and fine-tuning them, making it simple for developers and researchers to apply state-of-the-art NLP methods to their own tasks.
+Hugging Face has made it easier for people to contribute to the world of NLP research by providing open-source libraries that are well-documented and easy to use.
+"""
 
-# Prediction function
-def predict_from_url(url):
-    # Check if the URL is empty
-    if not url:
-        return {"Error": "Please input a URL"}
-    
-    try:
-        image = Image.open(BytesIO(requests.get(url).content))
-    except Exception as e:
-        return {"Error": f"Failed to load image: {str(e)}"}
-    
-    processed_image = processor(images=image, return_tensors="pt")['pixel_values']
-    
-    with torch.no_grad():
-        image_features = model.get_image_features(processed_image)
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
-        
-    return {fashion_items[i]: float(text_probs[0, i]) for i in range(len(fashion_items))}
+# Calling the function to summarize the text
+summary = summarize_text(text_to_summarize)
 
-# Gradio interface
-demo = gr.Interface(
-    fn=predict_from_url, 
-    inputs=gr.Textbox(label="Enter Image URL"),
-    outputs=gr.Label(label="Classification Results"), 
-    title="Fashion Item Classifier",
-    allow_flagging="never"
-)
+# Print the summarized text
+print("Original Text: \n", text_to_summarize)
+print("\nSummary: \n", summary)
 
 # Launch the interface
 demo.launch()