Update app.py

app.py

@@ -1,35 +1,84 @@
+# 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 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
-)
+from transformers import pipeline
 
-# Define the summarization function
-def summarize_text(text):
-    output = summarizer('summarize: ' + text)
-    return output[0]['summary_text']
+# Load the summarizer model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Create a Gradio interface
+# 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
 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
+    fn=summarize_text,  # The function to be called
+    inputs=gr.Textbox(label="Enter Article Text", placeholder="Type or paste the article 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
+    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
 )
 
 # Launch the interface
 iface.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+