Update app.py

app.py

@@ -1,6 +1,64 @@
+Here is an example of a Gradio interface code generation builder that meets your requirements:
+
 import gradio as gr
+from transformers import CodeT5ForConditionalGeneration, CodeT5Tokenizer
+
+# Initialize the CodeT5 model and tokenizer
+model = CodeT5ForConditionalGeneration.from_pretrained(" Salesforce/code-t5-base")
+tokenizer = CodeT5Tokenizer.from_pretrained("Salesforce/code-t5-base")
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=lambda input_code, upload_file, temperature, max_length: generate_code(input_code, upload_file, temperature, max_length),
+    inputs=[
+________gr.Textbox(label="Input_Code/Prompt"),
+________gr.File(label="Upload_Code_File"),
+________gr.Slider(label="Temperature",_minimum=0,_maximum=1,_default=0.5),
+________gr.Slider(label="Max_Length",_minimum=10,_maximum=512,_default=256)
+____],
+    outputs=[
+________gr.Code(label="Generated_Code"),
+________gr.Textbox(label="Conversation_History")
+____],
+    title="CodeT5 Code Generation Builder",
+    description="Generate code snippets using CodeT5 and interact with the AI model through a simple web interface."
+)
+
+def generate_code(input_code, upload_file, temperature, max_length):
+    # Preprocess the input code and uploaded file
+    if upload_file is not None:
+        with open(upload_file.name, 'r') as file:
+            input_code = file.read()
+
+    # Tokenize the input code
+    input_ids = tokenizer.encode(input_code, return_tensors='pt')
+
+    # Generate code using CodeT5
+    output = model.generate(input_ids, temperature=temperature, max_length=max_length)
+
+    # Convert the output to a string
+    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # Update the conversation history
+    conversation_history = f"Input Code: {input_code}\nGenerated Code: {generated_code}"
+
+    return generated_code, conversation_history
+
+# Launch the Gradio interface
+demo.launch()
+
+This code defines a Gradio interface that takes four inputs:
+
+A text box for inputting code or prompts
+A file uploader for uploading code files
+A temperature slider to adjust the generation temperature
+A max length slider to adjust the maximum generated code length
+
+The interface returns two outputs:
+
+A code box displaying the generated code
+A text box displaying the conversation history (including the input code and generated code)
+
+When the user interacts with the interface, the generate_code function is called, which preprocesses the input code and uploaded file, tokenizes the input code, generates code using CodeT5, and converts the output to a string. The conversation history is also updated accordingly.
 
-gr.load(
-   "models/Salesforce/codet5-base",
-   provider="hf-inference",
-).launch()
+Note that you need to install the transformers library and have the CodeT5 model and tokenizer downloaded for this code to work.