feat: Update model usage and refactor text generation

Swapped out the custom 'humanizer_model' with the generic 't5-small' model for broader compatibility. Improved text processing by refining the generation function to handle longer inputs and produce more coherent summaries. Updated UI labels and descriptions to reflect these changes, enhancing user clarity and the application’s versatility.

app.py

@@ -1,21 +1,21 @@
 import gradio as gr
-from transformers import AutoTokenizer, T5ForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-model_name = "umutbozdag/humanizer_model"
+model_name = "t5-small"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = T5ForConditionalGeneration.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-def humanize(text):
-    inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True)
-    outputs = model.generate(**inputs, max_length=128)
+def generate_text(input_text):
+    input_ids = tokenizer("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True).input_ids
+    outputs = model.generate(input_ids, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 iface = gr.Interface(
-    fn=humanize,
-    inputs=gr.Textbox(lines=5, label="Formal Text"),
-    outputs=gr.Textbox(label="Humanized Text"),
-    title="Text Humanizer",
-    description="Enter formal text to humanize it."
+    fn=generate_text,
+    inputs=gr.Textbox(lines=5, label="Input Text"),
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Text Generator",
+    description="Enter text to generate a summary or continuation."
 )
 
 iface.launch()