Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+MODEL_NAME = "deepseek-ai/deepseek-coder-1.3b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+
+# Function to generate responses
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    input_ids = inputs["input_ids"]
+    attention_mask = inputs["attention_mask"]
+
+    outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=200, pad_token_id=tokenizer.eos_token_id)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create a Gradio UI
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Enter your prompt"),
+    outputs=gr.Textbox(label="Generated Response"),
+    title="DeepSeek Coder Chatbot",
+    description="A chatbot powered by DeepSeek Coder 1.3B"
+)
+
+iface.launch()