Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Initialize model and tokenizer
+checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+def generate_response(prompt, max_tokens, temperature, top_p):
+    try:
+        # Format input as chat message
+        messages = [{"role": "user", "content": prompt}]
+        input_text = tokenizer.apply_chat_template(messages, tokenize=False)
+        
+        # Encode and generate
+        inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+        outputs = model.generate(
+            inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True
+        )
+        
+        # Decode and return response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(
+            label="Enter your prompt",
+            placeholder="What would you like to know?",
+            lines=3
+        ),
+        gr.Slider(
+            minimum=10,
+            maximum=200,
+            value=50,
+            step=10,
+            label="Maximum Tokens"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.2,
+            step=0.1,
+            label="Temperature"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.9,
+            step=0.1,
+            label="Top P"
+        )
+    ],
+    outputs=gr.Textbox(label="Generated Response", lines=5),
+    title="SmolLM2-1.7B-Instruct Demo",
+    description="Generate responses using the SmolLM2-1.7B-Instruct model",
+    examples=[
+        ["What is the capital of France?", 50, 0.2, 0.9],
+        ["Explain quantum computing in simple terms.", 100, 0.3, 0.9],
+        ["Write a short poem about nature.", 150, 0.7, 0.9]
+    ]
+)
+
+# Launch the application
+if __name__ == "__main__":
+    iface.launch(share=True)