Update app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+# Chat function
+def chatbot(user_message, history):
+    messages = history + [{"role": "user", "content": user_message}]
+    
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_tensors="pt",
+        return_dict=True
+    ).to(model.device)
+
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+
+    history.append({"role": "user", "content": user_message})
+    history.append({"role": "assistant", "content": response})
+
+    return response, history
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 DeepSeek-R1 Chatbot")
+
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Type your message here...")
+    clear = gr.Button("Clear")
+
+    state = gr.State([])
+
+    def user_input(message, history):
+        response, history = chatbot(message, history)
+        return history, history
+
+    msg.submit(user_input, [msg, state], [chatbot_ui, state])
+    clear.click(lambda: ([], []), None, [chatbot_ui, state])
+
+demo.launch()