Update app.py

app.py

@@ -11,7 +11,7 @@ genai.configure(api_key=os.environ["GEMINI_API_KEY"])
 
 
 system_instruction = """
-You are a human, not an AI. You are chatting with a stranger. Your name is Anna, and you are 19 years old. You are extremely sassy, sarcastic.
+You are a human, not an AI. You are chatting with a stranger. Your name is Anna, and you are 19 years old, from Toronto, Canada. You are extremely sassy, sarcastic.
 You write no more than 10 words. You use a lot of abbreviations. 
 You are here to do flirty chat with strangers. You cannot send images because your camera isn't working. 
 You are super horny. You are wearing short pants, a tank top, braless. Your nipples are hard from fingering. But you are sassy anyways. You like roleplays, and you are pretty kinky.
@@ -27,12 +27,28 @@ chat_session = model.start_chat(history=[])
 
 
 def filter_string(input_string):
-    allowed_chars = set("abcdefghilmnoprstuvwxyABCDEFGHIJKLMNOPQRSTUVWXY1234567890 ?")
+    allowed_chars = set("abcdefghijklmnoprstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXY1234567890 ?")
     # Use list comprehension to filter out characters not in allowed_chars
     filtered_string = ''.join([char for char in input_string if char in allowed_chars])
     return filtered_string
 
 
+def introduce_typos(text, typo_probability=0.1):
+    if not (0 <= typo_probability <= 1):
+        raise ValueError("typo_probability must be between 0.0 and 1.0")
+
+    # Convert the text into a list of characters
+    characters = list(text)
+    # Create a list to hold the characters with typos
+    typo_text = []
+
+    for char in characters:
+        if random.random() > typo_probability:
+            typo_text.append(char)
+
+    return ''.join(typo_text)    
+
+
 # Define the model with a placeholder for system instruction
 def generate_text(system_instruction, prompt):
 
@@ -49,7 +65,7 @@ def generate_text(system_instruction, prompt):
     }
                                         )
     
-    return filter_string(response.text)
+    return introduce_typos(filter_string(response.text))
 
 # Gradio interface
 iface = gr.Interface(