Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
+model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")
+
+def generate_text(prompt, max_length=100, min_length=20, temperature=1.0):
+    # Tokenize the prompt
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Generate text
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        min_length=min_length,
+        num_return_sequences=1,
+        temperature=temperature
+    )
+
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return generated_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    generate_text,
+    [
+        gr.Textbox(lines=5, label="Prompt"),
+        gr.Slider(0, 2048, 100, label="Max Length"),
+        gr.Slider(0, 2048, 20, label="Min Length"),
+        gr.Slider(0.1, 2.0, 1.0, label="Temperature"),
+    ],
+    "textbox",
+    title="TinyLlama Text Generator",
+    live=True,  # Enable live updates as the user types
+)
+
+# Launch the Gradio app
+iface.launch()