Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from threading import Thread
+import torch
+
+# Load the model and tokenizer
+model_name = "Qwen/Qwen2.5-Coder-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32, low_cpu_mem_usage=True)
+
+def generate_code(prompt, max_length):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    streamer = TextIteratorStreamer(tokenizer)
+    
+    generation_kwargs = dict(inputs, streamer=streamer, max_length=max_length)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    generated_text = ""
+    for new_text in streamer:
+        generated_text += new_text
+        yield generated_text
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_code,
+    inputs=[
+        gr.Textbox(lines=5, label="Enter your prompt"),
+        gr.Slider(minimum=50, maximum=500, value=200, step=10, label="Max Length")
+    ],
+    outputs=gr.Code(language="python", label="Generated Code"),
+    title="Qwen2.5-Coder-1.5B Code Generator",
+    description="Enter a prompt to generate Python code using Qwen2.5-Coder-1.5B",
+    examples=[
+        ["Write a Python function to calculate the factorial of a number."],
+        ["Create a class representing a simple bank account with deposit and withdraw methods."]
+    ]
+)
+
+iface.launch()