Update app.py

app.py

@@ -2,25 +2,27 @@
 import streamlit as st
 from transformers import pipeline
 
-# Page configuration
+# Page settings
 st.set_page_config(
     page_title="AI Text Generator",
     page_icon="🤖",
     layout="centered"
 )
 
-# Mandatory title: Name and Student ID
+# Required student information
 st.title("Dawar Shariq | Student ID: B04-0923-000022")
-
 st.header("AI Text Generator")
+
 st.write(
-    "Enter a prompt and let the AI generate a continuation "
-    "using a pretrained Hugging Face model."
+    "Enter any starting text and let the AI generate "
+    "a continuation using a pretrained Hugging Face model."
 )
 
-# Load and cache the model so it is not reloaded on every interaction
+# Pretrained model
 MODEL_NAME = "distilgpt2"
 
+
+# Load the model once and reuse it
 @st.cache_resource
 def load_model():
     return pipeline(
@@ -28,20 +30,21 @@ def load_model():
         model=MODEL_NAME
     )
 
-# Load the model
+
+# Load model with a loading message
 with st.spinner("Loading AI model..."):
     generator = load_model()
 
 st.success(f"Model loaded: {MODEL_NAME}")
 
-# Input widget
+# User can enter their own text
 prompt = st.text_area(
     "Enter your starting text:",
-    placeholder="Artificial intelligence can help people...",
+    placeholder="Example: Artificial intelligence can help people...",
     height=120
 )
 
-# Required feature: control maximum generated tokens
+# User controls the length of generated text
 max_new_tokens = st.slider(
     "Maximum new tokens",
     min_value=10,
@@ -50,35 +53,43 @@ max_new_tokens = st.slider(
     step=10
 )
 
-# Button and model call
+# Generate text when the button is clicked
 if st.button("Generate Text", type="primary"):
-    if prompt.strip():
-        with st.spinner("Generating text..."):
-            result = generator(
-                prompt,
-                max_new_tokens=max_new_tokens,
-                num_return_sequences=1,
-                do_sample=True,
-                temperature=0.8,
-                pad_token_id=generator.tokenizer.eos_token_id
-            )
-
-        # Display only the generated continuation
-        full_text = result[0]["generated_text"]
-        continuation = full_text[len(prompt):]
-
-        st.subheader("Generated Continuation")
-        if continuation.strip():
-            st.write(continuation)
-        else:
-            st.write(full_text)
+
+    # Handle empty input
+    if not prompt.strip():
+        st.warning("Please enter some text before generating.")
 
     else:
-        st.warning("Please enter a prompt before generating text.")
+        try:
+            with st.spinner("Generating text..."):
+                result = generator(
+                    prompt,
+                    max_new_tokens=max_new_tokens,
+                    num_return_sequences=1,
+                    do_sample=True,
+                    temperature=0.8,
+                    pad_token_id=generator.tokenizer.eos_token_id
+                )
+
+            generated_text = result[0]["generated_text"]
+
+            # Display only the newly generated continuation
+            continuation = generated_text[len(prompt):].strip()
+
+            st.subheader("Generated Continuation")
+
+            if continuation:
+                st.write(continuation)
+            else:
+                st.write(generated_text)
+
+        except Exception as error:
+            st.error(f"Something went wrong: {error}")
 
-# Model information
+# Information about the app
 with st.expander("About this application"):
-    st.write("Model: distilgpt2")
-    st.write("Pipeline task: text-generation")
-    st.write("Framework: Hugging Face Transformers")
-    st.write("Interface: Streamlit")
+    st.write("**Model:** distilgpt2")
+    st.write("**Task:** Text generation")
+    st.write("**Framework:** Hugging Face Transformers")
+    st.write("**Interface:** Streamlit")