Update app.py

app.py

@@ -10,7 +10,7 @@ model_path = "Zul001/HydroSense_Gemma_Finetuned_Model"
 gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(f"hf://{model_path}")
 
 
-
+reset_triggered = False
 
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Edu+AU+VIC+WA+NT+Dots:[email protected]&family=Give+You+Glory&family=Sofia&family=Sunshiney&family=Vujahday+Script&display=swap');
@@ -65,18 +65,23 @@ def post_process_output(prompt, result):
 
 
 def inference(prompt):
-  prompt_text = prompt
-  generated_text = gemma_lm.generate(prompt_text)
-
-  #Apply post-processing
-  formatted_output = post_process_output(prompt_text, generated_text)
-  print(formatted_output)
-
-  #adding a bit of delay
-  time.sleep(1)
-  result = formatted_output
-  # sessions = add_session(prompt_text)
-  return result
+    global reset_triggered
+    if reset_triggered:
+        #do nothing
+        return "", ""
+    
+    prompt_text = prompt
+    generated_text = gemma_lm.generate(prompt_text)
+    
+    #Apply post-processing
+    formatted_output = post_process_output(prompt_text, generated_text)
+    print(formatted_output)
+    
+    #adding a bit of delay
+    time.sleep(1)
+    result = formatted_output
+    # sessions = add_session(prompt_text)
+    return result
 
 
 # def inference(prompt):
@@ -106,6 +111,8 @@ def remember(prompt, result):
 #     return "\n".join(previous_sessions)
 
 def clear_fields():
+    global reset_triggered
+    reset_triggered = True
     return "", ""  # Return empty strings to clear the prompt and output fields