wowww

app.py

@@ -1,42 +1,29 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import pipeline
 
-# Load model and tokenizer
-model_id = "deepseek-ai/DeepSeek-R1"
+# Load using CPU-only settings
+pipe = pipeline(
+    "text-generation",
+    model="deepseek-ai/DeepSeek-R1",
+    device=-1,             # Force CPU usage
+    trust_remote_code=True # Support custom model code
+)
 
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
-
-# Inference function
 def generate(prompt, max_new_tokens=256, temperature=0.7, top_p=0.95):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    output = model.generate(
-        **inputs,
+    outputs = pipe(
+        prompt,
         max_new_tokens=max_new_tokens,
         temperature=temperature,
-        top_p=top_p,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
+        top_p=top_p
     )
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response
+    return outputs[0]["generated_text"]
 
-# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🚀 DeepSeek-R1 - Hugging Face Space Demo")
-    
-    with gr.Row():
-        prompt = gr.Textbox(label="Prompt", placeholder="Ask me anything...")
-    
-    with gr.Row():
-        max_tokens = gr.Slider(64, 1024, value=256, step=16, label="Max new tokens")
-        temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
-        top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
-
+    gr.Markdown("## 🚀 DeepSeek‑R1 (CPU Gradio Demo)")
+    prompt = gr.Textbox(label="Prompt")
+    max_tokens = gr.Slider(64, 1024, value=256, step=16, label="Max new tokens")
+    temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top‑p")
     output = gr.Textbox(label="Generated Text")
-
-    generate_btn = gr.Button("Generate")
-    generate_btn.click(fn=generate, inputs=[prompt, max_tokens, temperature, top_p], outputs=output)
-
+    demo.Button("Generate").click(fn=generate, inputs=[prompt, max_tokens, temperature, top_p], outputs=output)
 demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+transformers[torch]
+torch
+gradio