Delete app.py

app.py

@@ -1,49 +0,0 @@
-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}")
-