Create app.py

app.py

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+import gradio as gr
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer
+)
+from threading import Thread
+
+# Configuration
+MODEL_NAME = "deepseek-ai/DeepSeek-R1"  # Verify exact model ID on Hugging Face Hub
+DEFAULT_MAX_NEW_TOKENS = 512
+
+# Load model and tokenizer
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        device_map="auto",
+        torch_dtype="auto",
+        # load_in_4bit=True  # Uncomment for 4-bit quantization
+    )
+except Exception as e:
+    raise gr.Error(f"Error loading model: {str(e)}")
+
+def generate_text(prompt, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, temperature=0.7, top_p=0.9):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Streamer for real-time output
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
+    
+    generation_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True
+    )
+    
+    # Start generation in a thread
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    # Yield generated text
+    generated_text = ""
+    for new_text in streamer:
+        generated_text += new_text
+        yield generated_text
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# DeepSeek-R1 Demo")
+    
+    with gr.Row():
+        input_text = gr.Textbox(
+            label="Input Prompt",
+            placeholder="Enter your prompt here...",
+            lines=5
+        )
+        output_text = gr.Textbox(
+            label="Generated Response",
+            interactive=False,
+            lines=10
+        )
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        max_tokens = gr.Slider(
+            minimum=64,
+            maximum=2048,
+            value=DEFAULT_MAX_NEW_TOKENS,
+            label="Max New Tokens"
+        )
+        temperature = gr.Slider(
+            minimum=0.1,
+            maximum=1.5,
+            value=0.7,
+            label="Temperature"
+        )
+        top_p = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.9,
+            label="Top-p"
+        )
+    
+    submit_btn = gr.Button("Generate")
+    submit_btn.click(
+        fn=generate_text,
+        inputs=[input_text, max_tokens, temperature, top_p],
+        outputs=output_text,
+        api_name="generate"
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)