Update app.py

app.py

@@ -1,3 +1,38 @@
 import gradio as gr
+import gradio as gr
+
+def predict(input, history=[]):
+  """Processes user input and potentially leverages history for improved predictions.
+
+  Args:
+      input (str): User's input text.
+      history (list, optional): List of previous inputs and outputs for context (default: []).
+
+  Returns:
+      tuple: A tuple containing the chatbot response and the updated history (optional).
+  """
+
+  # Replace with your actual Gemma prediction logic here
+  chatbot_response = "This is a placeholder chatbot response. Integrate your Gemma model here for predictions."
+
+  # Update history if necessary for your application
+  if history:
+      history.append((input, chatbot_response))
+
+  return chatbot_response, history  # Optionally return updated history
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=["textbox", "state"],  # "state" input can be removed if not used
+    outputs=["chatbot", None]  # Remove "state" output if history is not used
+)
+
+# Load any necessary model weights (replace with your specific model loading logic)
+try:
+  interface.load("models/google/gemma-1.1-7b-it")  # Assuming model weights are available
+except Exception as e:
+  print(f"Error loading model: {e}")  # Handle potential loading errors
+
 
 gr.load("models/google/gemma-1.1-7b-it").launch()