Create app.py

app.py

@@ -0,0 +1,33 @@
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/Gemma-2B-Finetuined-pythonCode")
+model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/Gemma-2B-Finetuined-pythonCode")
+
+def generate_code(text):
+  prompt_template = f"""
+  <start_of_turn>user based on given instruction create a solution\n\nhere are the instruction {query}
+  <end_of_turn>\n<start_of_turn>model
+  """
+  prompt = prompt_template
+  encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
+
+  device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+  model.to(device)
+  inputs = encodeds.to(device)
+
+
+  # Increase max_new_tokens if needed
+  generated_ids = model.generate(inputs, max_new_tokens=500, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+  ans = ''
+  for i in tokenizer.decode(generated_ids[0], skip_special_tokens=True).split('<end_of_turn>')[:2]:
+      ans += i
+
+  # Extract only the model's answer
+  model_answer = ans.split("model")[1].strip()
+  return model_answer.split("user")[1]
+
+
+demo = gr.Interface(fn=generate_code, inputs='text',outputs='text',title='Text Summarization')
+demo.launch(debug=True,share=True)