Create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from transformers import pipeline
+
+st.title("Text Generator")
+
+# Using Qwen2.5-0.5B-Instruct - efficient 500M parameter model
+# Good balance between size and quality
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "text-generation",
+        model="Qwen/Qwen2.5-0.5B-Instruct"
+    )
+
+pipe = load_model()
+
+text = st.text_input("Enter text to complete or ask a question")
+
+if st.button("Generate"):
+    if text:
+        with st.spinner("Generating..."):
+            # Qwen2.5-Instruct works well with direct text or formatted prompts
+            # For questions, format as instruction; for completion, use directly
+            if text.strip().endswith("?"):
+                prompt = f"<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n"
+            else:
+                # For text completion, just use the text directly
+                prompt = text
+            
+            output = pipe(
+                prompt,
+                max_length=len(prompt.split()) + 100,  # Generate more text
+                num_return_sequences=1,
+                temperature=0.7,
+                do_sample=True,
+                top_p=0.9,
+                repetition_penalty=1.2,
+                pad_token_id=pipe.tokenizer.eos_token_id
+            )
+            result = output[0]["generated_text"]
+            
+            # Clean up the result - remove the prompt if it was included
+            if prompt in result:
+                result = result.replace(prompt, "").strip()
+            
+            st.write("**Generated Text:**")
+            st.write(result)