Update app.py

app.py

@@ -1,12 +1,25 @@
-from google.colab import drive
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Mount Google Drive
-drive.mount('/content/drive')
 
-# Path to the saved model on Google Drive
-model_path = '/content/drive/My Drive/T5_samsum'
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load the tokenizer and model from the specified path
+# Load the tokenizer and model from the Hugging Face Model Hub
+model_path = 'abdulllah01/mt5-Summarizer-FineTuned'
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+
+# Streamlit app
+st.title("Summarization App")
+st.write("This app summarizes text using a fine-tuned T5 model.")
+
+# User input
+user_input = st.text_area("Enter text to summarize", "")
+
+if st.button("Summarize"):
+    if user_input:
+        inputs = tokenizer.encode("summarize: " + user_input, return_tensors="pt", max_length=512, truncation=True)
+        summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        st.write("Summary:")
+        st.write(summary)
+    else:
+        st.write("Please enter some text to summarize.")