Upload 2 files

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Set model ID
+# comment out the model you want to use
+# model_id = "gpt2" # for testing purposes only
+# model_id = "deepseek-ai/deepseek-coder-1.3b"
+# model_id = "deepseek-ai/deepseek-coder-1.3b-base"
+model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
+
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+)
+
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def generate_code(prompt):
+    if not prompt.strip():
+        return "⚠ Please enter a valid prompt."
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.7
+        )
+
+    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Strip the prompt if it appears at the start
+    if output_text.startswith(prompt):
+        output_text = output_text[len(prompt):].lstrip()
+
+    return output_text
+
+demo = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(lines=5, label="Enter Prompt"),
+    outputs=gr.Textbox(label="Generated Output"),
+    title="Code Generator using DeepSeek"
+)
+
+demo.launch()

requirements.txt

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