Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "Smilyai-labs/Sam-reason-S2.1"  # or your local path
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
+
+# Chat function
+def chat_with_model(user_input, history):
+    if history is None:
+        history = []
+
+    # Build conversation string for context
+    conversation = ""
+    for msg in history:
+        conversation += f"User: {msg['content']}\nSam:"
+        if msg['role'] == "assistant":
+            conversation += f" {msg['content']}\n"
+
+    conversation += f"User: {user_input}\nSam:"
+
+    # Encode and generate
+    inputs = tokenizer(conversation, return_tensors="pt", truncation=True, max_length=1024)
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids,
+            max_new_tokens=150,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=0.7,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response_text = decoded.split("Sam:")[-1].strip()
+
+    # Add new message to chat history
+    history.append({"role": "user", "content": user_input})
+    history.append({"role": "assistant", "content": response_text})
+
+    return "", history
+
+# Gradio UI
+def create_chatbot_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 💬 Chat with **Sam** (SmilyAI's Reasoning LLM 2.1 generation)")
+        chatbot = gr.Chatbot(label="Chat", type="messages")
+        user_input = gr.Textbox(placeholder="Type your message...", show_label=False)
+        send_btn = gr.Button("Send")
+
+        send_btn.click(
+            chat_with_model,
+            inputs=[user_input, chatbot],
+            outputs=[user_input, chatbot]
+        )
+
+    return demo
+
+# Launch
+demo = create_chatbot_interface()
+demo.launch()
+