Update app.py

app.py

@@ -1,58 +1,21 @@
+# Import libraries
 import gradio as gr
-from transformers import AutoModel, AutoProcessor
-import torch
-import requests
-from PIL import Image
-from io import BytesIO
+from transformers import pipeline
 
-fashion_items = ['top', 'trousers', 'jumper']
+# Create a summarization pipeline
+summarizer = pipeline("summarization")
 
-# 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)
+# Define a function that takes a text and returns a summary
+def summarize(text):
+  summary = summarizer(text, max_length=150, min_length=40, do_sample=False)[0]
+  return summary["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)
-
-# 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))}
-
-# 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"
+# Create a Gradio interface
+interface = gr.Interface(
+  fn=summarize, # the function to wrap
+  inputs=gr.Textbox(lines=10, label="Input Text"), # the input component
+  outputs=gr.Textbox(label="Summary") # the output component
 )
 
 # Launch the interface
-demo.launch()
+interface.launch()