Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Select a model
+model_name = "Salesforce/codegen-2B-mono"  # Ensure this model is available
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate code based on a prompt
+def generate_code(prompt):
+    # Adjust parameters to improve output quality
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=100,       # Adjust as needed for code length
+        temperature=0.3,          # Lower temperature for more deterministic output
+        top_p=0.9,                # Top-p filtering to focus on more likely completions
+        repetition_penalty=1.2,   # Penalizes repetitive phrases
+        do_sample=True            # Enables sampling for a creative touch
+    )
+    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_code
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(lines=5, label="Enter your prompt"),
+    outputs=gr.Code(language="python", label="Generated Code"),
+    title="Python Code Generator",
+    description="Enter a description of the Python code you want to generate."
+)
+
+# Launch the interface
+iface.launch()