Update app.py

app.py

@@ -1,40 +1,48 @@
 import streamlit as st
-
 from transformers import T5Tokenizer, TFAutoModelForSeq2SeqLM, pipeline
-
 import zipfile
+import os
+
 # Define the path to the saved model zip file
 zip_model_path = 'T5_samsum-20240723T171755Z-001.zip'
 
+# Define the directory to extract the model
+model_dir = './model'
+
 # Unzip the model
 with zipfile.ZipFile(zip_model_path, 'r') as zip_ref:
-    zip_ref.extractall('./model')
-
-# Define the path to the saved model
-model_path = './model/T5_samsum-20240723T171755Z-001'
-
-# Load the tokenizer and model
-tokenizer = T5Tokenizer.from_pretrained(model_path)
-model = TFAutoModelForSeq2SeqLM.from_pretrained(model_path)
-
-summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
-
-# Set the title for the Streamlit app
-st.title("T5 Summary Generator")
-
-# Text input for the user
-text = st.text_area("Enter your text: ")
-
-def generate_summary(input_text):
-    # Perform summarization
-    summary = summarizer(input_text, max_length=200, min_length=40, do_sample=False)
-    return summary[0]['summary_text']
-
-if st.button("Generate"):
-    if text:
-        generated_summary = generate_summary(text)
-        # Display the generated summary
-        st.subheader("Generated Summary")
-        st.write(generated_summary)
-    else:
-        st.warning("Please enter some text to generate a summary.")
+    zip_ref.extractall(model_dir)
+
+# After unzipping, the model should be in a specific directory, check the directory structure
+model_path = os.path.join(model_dir, 'T5_samsum')
+
+# Verify that the directory exists and contains the necessary files
+if not os.path.exists(model_path):
+    st.error(f"Model directory {model_path} does not exist or is incorrect.")
+else:
+    # Load the tokenizer and model
+    tokenizer = T5Tokenizer.from_pretrained(model_path)
+    model = TFAutoModelForSeq2SeqLM.from_pretrained(model_path)
+
+    # Create a summarization pipeline
+    summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+    # Set the title for the Streamlit app
+    st.title("T5 Summary Generator")
+
+    # Text input for the user
+    text = st.text_area("Enter your text: ")
+
+    def generate_summary(input_text):
+        # Perform summarization
+        summary = summarizer(input_text, max_length=200, min_length=40, do_sample=False)
+        return summary[0]['summary_text']
+
+    if st.button("Generate"):
+        if text:
+            generated_summary = generate_summary(text)
+            # Display the generated summary
+            st.subheader("Generated Summary")
+            st.write(generated_summary)
+        else:
+            st.warning("Please enter some text to generate a summary.")