Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import torch
+from transformers import CodeT5ForConditionalGeneration, CodeT5Tokenizer
+
+# Load pre-trained CodeT5 model and tokenizer
+model = CodeT5ForConditionalGeneration.from_pretrained("Salesforce/code-t5-small")
+tokenizer = CodeT5Tokenizer.from_pretrained("Salesforce/code-t5-small")
+
+def generate_code(prompt, code_file):
+    # Read uploaded code file
+    if code_file:
+        code_text = code_file.read().decode("utf-8")
+    else:
+        code_text = ""
+
+    # Tokenize input prompt
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Generate code using CodeT5 model
+    output = model.generate(input_ids=input_ids, max_length=256)
+    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # Return generated code and code preview
+    return generated_code, f"```python\n{generated_code}\n```"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_code,
+    inputs=[
+________gr.Textbox(label="Input_Prompt",_placeholder="Enter_a_prompt"),
+________gr.Upload(label="Upload_Code_File",_file_types=["py"])
+    ],
+    outputs=[
+________gr.Textbox(label="Generated_Code"),
+________gr.Code(label="Code_Preview",_language="python")
+____],
+    title="Code Generation with CodeT5",
+    description="Generate Python code based on input prompt and uploaded code file."
+)
+
+# Launch Gradio interface
+iface.launch()