Update app.py

app.py

@@ -1,5 +1,54 @@
+# import gradio as gr
+
+# demo = gr.load("models/NSTiwari/fine_tuned_science_gemma2b-it")
+
+# demo.launch()
+
 import gradio as gr
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import time
+
+# Replace with your fine-tuned model ID from Hugging Face Hub
+model_id = "models/NSTiwari/fine_tuned_science_gemma2b-it"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+def inference(input_text):
+  """
+  Performs inference on the science question and returns answer and latency.
+  """
+  start_time = time.time()
+  input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
+  input_length = input_ids["input_ids"].shape[1]
+  outputs = model.generate(
+      input_ids=input_ids["input_ids"],
+      max_length=512,  # Adjust max_length as needed
+      do_sample=False
+  )
+  generated_sequence = outputs[:, input_length:].tolist()
+  response = tokenizer.decode(generated_sequence[0])
+  end_time = time.time()
+  return {"answer": response, "latency": f"{end_time - start_time:.2f} seconds"}
+
+def gradio_interface(question):
+  """
+  Gradio interface function that calls inference and returns answer/latency.
+  """
+  result = inference(question)
+  return result["answer"], result["latency"]
 
-demo = gr.load("models/NSTiwari/fine_tuned_science_gemma2b-it")
+# Gradio interface definition
+iface = gr.Interface(
+  fn=gradio_interface,
+  inputs=gr.Textbox(label="Science Question", lines=4),
+  outputs=[gr.Textbox(label="Answer"), gr.Textbox(label="Latency")],
+  title="Science Q&A with Fine-tuned Model",
+  description="Ask a science question and get an answer from the fine-tuned model.",
+)
 
-demo.launch()
+if __name__ == "__main__":
+  iface.launch()