Update app.py

app.py

@@ -1,30 +1,52 @@
-import streamlit as st
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-import torch
-
-
-model_path = "./saved_model"
-tokenizer_path = "./saved_tokenizer"
-
-tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
-model = T5ForConditionalGeneration.from_pretrained(model_path)
-
-# دالة توليد الملخص
-def generate_summary(text):
-    inputs = ["summarize: " + text]
-    inputs = tokenizer(inputs, max_length=1024, truncation=True, return_tensors="pt")
-    outputs = model.generate(inputs.input_ids.to(model.device), max_length=150, length_penalty=2.0, num_beams=4, early_stopping=True)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# واجهة المستخدم باستخدام Streamlit
-st.title("application summarize")
-
-text = st.text_area("enter text you want to summarize.")
-
-if st.button("summary:"):
-    if text:
-        summary = generate_summary(text)
-        st.write("summary")
-        st.write(summary)
-    else:
-        st.write("please enter text.")
+import streamlit as st
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+
+# Load model and tokenizer
+model_path = "./saved_model"
+tokenizer_path = "./saved_tokenizer"
+
+try:
+    tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
+    model = T5ForConditionalGeneration.from_pretrained(model_path)
+    device = torch.device("cpu")
+    model.to(device)
+    model_loaded = True
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    model_loaded = False
+
+# Function to generate summary
+def generate_summary(text):
+    try:
+        inputs = ["summarize: " + text]
+        inputs = tokenizer(inputs, max_length=1024, truncation=True, return_tensors="pt").to(device)
+        outputs = model.generate(inputs.input_ids, max_length=150, length_penalty=2.0, num_beams=4, early_stopping=True)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    except Exception as e:
+        st.error(f"Error generating summary: {e}")
+        return None
+
+# Streamlit UI
+st.title("Text Summarization Application")
+
+st.markdown("Enter the text you want to summarize, and the application will generate a concise summary.")
+
+text = st.text_area("Enter text here:", height=200)
+
+if st.button("Summarize"):
+    if text and model_loaded:
+        with st.spinner("Generating summary..."):
+            summary = generate_summary(text)
+        if summary:
+            st.subheader("Summary:")
+            st.write(summary)
+        else:
+            st.warning("Summary generation failed. Please check the input text.")
+    elif not model_loaded:
+        st.error("Model failed to load. Please check the application logs.")
+    else:
+        st.warning("Please enter text.")
+
+st.markdown("---")
+st.markdown("This application was developed using the T5 model.")