Update app.py

app.py

@@ -1,84 +1,44 @@
-# import gradio as gr
-# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
-
-# # Load the model and tokenizer
-# model_name = 'IMISLab/GreekT5-umt5-base-greeksum'
-# model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# # Set up the summarizer pipeline
-# summarizer = pipeline(
-#     'summarization',
-#     model=model,
-#     tokenizer=tokenizer,
-#     device=-1,  # -1 for CPU; set to 0 for GPU if available
-#     max_new_tokens=128,
-#     truncation=True
-# )
-
-# # Define the summarization function
-# def summarize_text(text):
-#     output = summarizer('summarize: ' + text)
-#     return output[0]['summary_text']
-
-# # Create a Gradio interface
-# iface = gr.Interface(
-#     fn=summarize_text,            # Function to run
-#     inputs=gr.Textbox(label="Enter Greek Text", placeholder="Type or paste your text here..."),  # Input component
-#     outputs=gr.Textbox(label="Summary", interactive=True),  # Output component
-#     title="Greek Text Summarization",  # Title for the UI
-#     description="This app uses a pre-trained Greek summarization model to generate a brief summary of your input text.",  # Description
-#     allow_flagging="never"         # Optional: Disable flagging feature
-# )
-
-# # Launch the interface
-# iface.launch()
-
 import gradio as gr
-from transformers import pipeline
-
-# Load the summarizer model
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-# Function to summarize text
-def summarize_text(article):
-    summary = summarizer(article, max_length=130, min_length=30, do_sample=False)
-    return summary[0]['summary_text']
-
-# Create the Gradio interface
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("kriton/greek-text-summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("kriton/greek-text-summarization")
+
+# Set up the summarizer pipeline
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+# Define the summarization function
+def generate_summary(article):
+    inputs = tokenizer(
+        'summarize: ' + article, 
+        return_tensors="pt", 
+        max_length=1024, 
+        truncation=True,
+        padding="max_length",
+    )
+
+    outputs = model.generate(
+        inputs["input_ids"], 
+        max_length=512, 
+        min_length=130, 
+        length_penalty=3.0, 
+        num_beams=8, 
+        early_stopping=True,
+        repetition_penalty=3.0,
+    )
+
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create Gradio interface
 iface = gr.Interface(
-    fn=summarize_text,  # The function to be called
-    inputs=gr.Textbox(label="Enter Article Text", placeholder="Type or paste the article here..."),  # Input component
+    fn=generate_summary,  # Function to run
+    inputs=gr.Textbox(label="Enter Greek Article", placeholder="Type or paste your article here..."),  # Input component
     outputs=gr.Textbox(label="Summary", interactive=True),  # Output component
-    title="Text Summarization",  # Title of the interface
-    description="This app uses a pre-trained summarization model (BART) to summarize the provided article.",  # Description
-    allow_flagging="never"  # Disable flagging
+    title="Greek Text Summarization",  # Title for the UI
+    description="This app uses a pre-trained Greek summarization model to generate a brief summary of your input text.",  # Description
+    allow_flagging="never"  # Optional: Disable flagging feature
 )
 
 # Launch the interface
 iface.launch()
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