Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load DeepSeek model
+model_id = "deepseek-ai/deepseek-llm-7b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+# Inference function
+def generate_response(prompt, temperature, top_p, max_tokens, repetition_penalty):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        max_new_tokens=max_tokens,
+        repetition_penalty=repetition_penalty
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Prompt", lines=6, placeholder="Ask something..."),
+        gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p"),
+        gr.Slider(32, 2048, value=512, step=32, label="Max New Tokens"),
+        gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty")
+    ],
+    outputs="text",
+    title="🧠 DeepSeek LLM Chat with Parameter Tuning",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    iface.launch()