Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import os
+from zipfile import ZipFile
+
+# Streamlit UI for uploading model
+st.title("Text Summarizer")
+uploaded_file = st.file_uploader("bart-base.zip", type="zip")
+
+if uploaded_file is not None:
+    # Extract the uploaded zip file
+    with ZipFile(uploaded_file, 'r') as zip_ref:
+        zip_ref.extractall("model_directory")
+
+    # Load the model from the extracted directory
+    try:
+        model_path = "model_directory"
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+        st.success("Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+
+# Text area for input
+text = st.text_area("Enter the text to generate its Summary:")
+
+# Configuration for generation
+generation_config = {'max_length': 100, 'do_sample': True, 'temperature': 0.7}
+
+if text:
+    try:
+        # Encode input
+        inputs_encoded = tokenizer(text, return_tensors='pt')
+
+        # Generate output
+        with torch.no_grad():
+            model_output = model.generate(inputs_encoded["input_ids"], **generation_config)[0]
+
+        # Decode output
+        output = tokenizer.decode(model_output, skip_special_tokens=True)
+
+        # Display results
+        with st.expander("Output", expanded=True):
+            st.write(output)
+
+    except Exception as e:
+        st.error(f"An error occurred during summarization: {e}")