Update app.py

app.py

@@ -1,7 +1,81 @@
 import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces
+# import pythonexample
+
+
+
+pythonexample = """import gradio as gr
 
 def greet(name):
     return "Hello " + name + "!!"
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+demo.launch()"""
+
+title = """🙋🏻‍♂️Welcome to Tonic's Granite Code ! """
+description = """Granite-8B-Code-Instruct is a 8B parameter model fine tuned from Granite-8B-Code-Base on a combination of permissively licensed instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills.
+### Join us : 
+TeamTonic is always making cool demos! Join our active builder's community on Discord: [Discord](https://discord.gg/GWpVpekp) On Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On Github: [Polytonic](https://github.com/tonic-ai) & contribute to [multitonic](https://github.com/multitonic/multitonic)
+"""
+
+# Define the device and model path
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_path = "ibm-granite/granite-8b-code-instruct"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+model.to(device)
+model.eval()
+
+
+# Function to generate code
+
+@spaces.GGPU
+def generate_code(prompt, max_length):
+    # Prepare the input chat format
+    chat = [
+        { "role": "user", "content": prompt }
+    ]
+    chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    # Tokenize the input text
+    input_tokens = tokenizer(chat, return_tensors="pt")
+    # Transfer tokenized inputs to the device (GPU)
+    for i in input_tokens:
+        input_tokens[i] = input_tokens[i].to("cuda")
+    # Generate output tokens
+    output_tokens = model.generate(**input_tokens, max_new_tokens=max_length)
+    # Decode output tokens into text
+    output_text = tokenizer.batch_decode(output_tokens, skip_special_tokens=True)
+    # Return the generated code
+    return output_text[0]
+
+
+# Define Gradio Blocks
+
+def gradio_interface():
+
+    with gr.Blocks() as interface:
+
+        gr.Markdown(title)
+        gr.Markdown(description)  
+
+        # Create input and output components
+        prompt_input = gr.Code(label="Enter your Coding Question", value=pythonexample, language='python', lines=10)
+        code_output = gr.Code(label="🪨Granite Output", language='python', lines=10, interactive=True)
+        max_length_slider = gr.Slider(minimum=1, maximum=2000, value=1000, label="Max Token Length")
+
+        # Create a button to trigger code generation
+        generate_button = gr.Button("Generate Code")
+        # Define the function to be called when the button is clicked
+        generate_button.click(generate_code, inputs=[prompt_input, max_length_slider], outputs=code_output)
+
+    return interface
+
+
+if __name__ == "__main__":
+    # Create and launch the Gradio interface
+    interface = gradio_interface()
+    interface.launch()