Update app.py

app.py

@@ -1,8 +1,33 @@
-# Use a pipeline as a high-level helper
+import streamlit as st
 from transformers import pipeline
 
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
-pipe(messages)
+# Title of the app
+st.title("Text Generation with DeepSeek-R1-Distill-Qwen-1.5B")
+
+# Load the text-generation pipeline
+@st.cache_resource  # Cache the model to avoid reloading on every interaction
+def load_model():
+    return pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
+
+model = load_model()
+
+# Input text box for user input
+user_input = st.text_area("Enter your prompt:", "Who are you?")
+
+# Slider to control the max length of the generated text
+max_length = st.slider("Max length of generated text", min_value=10, max_value=200, value=50)
+
+# Button to generate text
+if st.button("Generate Text"):
+    if user_input:
+        with st.spinner("Generating text..."):
+            # Generate text using the pipeline
+            messages = [{"role": "user", "content": user_input}]
+            output = model(messages, max_length=max_length, num_return_sequences=1)
+            generated_text = output[0]["generated_text"]
+
+        # Display the generated text
+        st.success("Generated Text:")
+        st.write(generated_text)
+    else:
+        st.warning("Please enter a prompt!")