Update app.py

app.py

@@ -1,64 +1,172 @@
+# app.py - SmallLM Gradio Demo
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import warnings
+warnings.filterwarnings("ignore")
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Global variables for model and tokenizer
+model = None
+tokenizer = None
 
+def load_model():
+    """Load the SmallLM model and tokenizer"""
+    global model, tokenizer
+    
+    try:
+        print("Loading SmallLM model...")
+        model_name = "XsoraS/SmallLM"
+        
+        # Load tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        
+        # Add padding token if it doesn't exist
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        # Load model
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            device_map="auto" if torch.cuda.is_available() else None,
+            trust_remote_code=True
+        )
+        
+        print("Model loaded successfully!")
+        return "Model loaded successfully!"
+        
+    except Exception as e:
+        error_msg = f"Error loading model: {str(e)}"
+        print(error_msg)
+        return error_msg
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+    """Generate text using the loaded model"""
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        return "Please load the model first!"
+    
+    try:
+        # Tokenize input
+        inputs = tokenizer.encode(prompt, return_tensors="pt")
+        
+        # Move to same device as model
+        if torch.cuda.is_available():
+            inputs = inputs.to(model.device)
+        
+        # Generate
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs,
+                max_length=max_length,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                num_return_sequences=1
+            )
+        
+        # Decode output
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Return only the new generated part
+        return generated_text[len(prompt):].strip()
+        
+    except Exception as e:
+        return f"Error generating text: {str(e)}"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def clear_text():
+    """Clear the input and output"""
+    return "", ""
 
+# Create Gradio interface
+with gr.Blocks(title="SmallLM Demo", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 SmallLM Inference Demo")
+    gr.Markdown("Simple demo for XsoraS/SmallLM text generation")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            load_btn = gr.Button("🔄 Load Model", variant="primary")
+            status = gr.Textbox(
+                label="Status", 
+                value="Click 'Load Model' to start",
+                interactive=False
+            )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt_input = gr.Textbox(
+                label="Enter your prompt:",
+                placeholder="Once upon a time...",
+                lines=3
+            )
+            
+            with gr.Row():
+                max_length = gr.Slider(
+                    label="Max Length",
+                    minimum=10,
+                    maximum=500,
+                    value=100,
+                    step=10
+                )
+                temperature = gr.Slider(
+                    label="Temperature",
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.7,
+                    step=0.1
+                )
+                top_p = gr.Slider(
+                    label="Top P",
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05
+                )
+            
+            with gr.Row():
+                generate_btn = gr.Button("✨ Generate", variant="primary")
+                clear_btn = gr.Button("🗑️ Clear")
+        
+        with gr.Column(scale=2):
+            output = gr.Textbox(
+                label="Generated Text:",
+                lines=10,
+                interactive=False
+            )
+    
+    # Event handlers
+    load_btn.click(
+        fn=load_model,
+        outputs=status
+    )
+    
+    generate_btn.click(
+        fn=generate_text,
+        inputs=[prompt_input, max_length, temperature, top_p],
+        outputs=output
+    )
+    
+    clear_btn.click(
+        fn=clear_text,
+        outputs=[prompt_input, output]
+    )
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["The future of artificial intelligence is"],
+            ["In a world where technology and nature coexist"],
+            ["Write a short story about a robot who"],
+            ["Explain quantum computing in simple terms:"],
+        ],
+        inputs=prompt_input
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )