Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+from transformers import AutoTokenizer, pipeline
+import torch
+
+# Load the model and tokenizer
+model = "K00B404/DeepQwenScalerPlus"
+tokenizer = AutoTokenizer.from_pretrained(model)
+
+# Initialize the pipeline for text generation
+pipeline = pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+# Function to interact with the model
+def generate_response(user_message):
+    messages = [
+        {"role": "system", "content": "You are a reasoning coder and specialize in generating Python scripts"},
+        {"role": "user", "content": user_message}
+    ]
+    
+    # Tokenize the input message
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    
+    # Get the model's output
+    outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+    
+    return outputs[0]["generated_text"]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_response, 
+    inputs=gr.Textbox(label="Ask a Question", placeholder="Enter your question here..."), 
+    outputs=gr.Textbox(label="Generated Response"), 
+    title="DeepQwenScalerPlus Gradio App",
+    description="Interact with the DeepQwenScalerPlus model to get Python script generation responses."
+)
+
+# Launch the Gradio app
+iface.launch()