Update app.py

app.py

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+# app.py
+!pip install transformers
+!pip install streamlit
+
+import streamlit as st
+from transformers import pipeline
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+import gdown
+
+# Download the model from Google Drive
+@st.cache(allow_output_mutation=True)
+def load_model_from_gdrive():
+    url = https://drive.google.com/drive/folders/19P3ZcWor8znyaOMJgx_gaHuOyf4alnP3?usp=drive_link  # Replace with your actual Google Drive link
+    output = 'model.zip'
+    gdown.download(url, output, quiet=False)
+    # Unzip the model
+    import zipfile
+    with zipfile.ZipFile(output, 'r') as zip_ref:
+        zip_ref.extractall('model')
+    # Load the model and tokenizer
+    model = AutoModelForSeq2SeqLM.from_pretrained('model')
+    tokenizer = AutoTokenizer.from_pretrained('model')
+    return model, tokenizer
+
+model, tokenizer = load_model_from_gdrive()
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+# Streamlit app
+st.title("Text Summarization App")
+st.write("Enter the text you want to summarize:")
+
+# Text input
+user_input = st.text_area("Text to summarize", height=200)
+
+# Summarize text
+if st.button("Summarize"):
+    if user_input:
+        summary = summarizer(user_input, max_length=130, min_length=30, do_sample=False)
+        st.subheader("Summary:")
+        st.write(summary[0]['summary_text'])
+    else:
+        st.write("Please enter text to summarize.")