Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import os
+from transformers import GemmaTokenizer, AutoModelForCausalLM
+
+# Set an environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it")
+model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it").to("cuda:0")
+
+# sample input
+input_text = "Write a Python function to calculate the nth fibonacci number.\n"
+
+def codegemma(message, history, temperature, max_new_tokens,):
+  input_ids = tokenizer(message, return_tensors="pt").to("cuda:0")
+  outputs = model.generate(**input_ids,
+                           temperature=temperature,
+                           max_new_tokens=max_new_tokens,
+  )
+  response = tokenizer.decode(outputs[0])
+  return response
+    
+placeholder = """
+<img src="https://huggingface.co/spaces/ysharma/CodeGemma/resolve/main/gemma_lockup_vertical_full-color_rgb.png" style="width:40%">
+<b>CodeGemma-7B-IT</b>
+"""
+
+with gr.Blocks(fill_height=True) as demo:
+      gr.Markdown("# GEMMA-7b-IT")
+      #with gr.Tab('CodeGemma Chatbot'):
+      gr.ChatInterface(codegemma, 
+                       examples=[["Write a Python function to calculate the nth fibonacci number."]], 
+                       fill_height=True,
+                       additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+                       additional_inputs=[
+                           gr.Slider(0, 1, 0.95, label="Temperature", render=False),
+                           gr.Slider(128, 4096, 512, label="Max new tokens", render=False ),
+                           ],
+                       )
+    
+if __name__ == "__main__":
+    demo.launch(debug=False)