Update app.py

app.py

@@ -1,70 +1,64 @@
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load Personality_LM model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM", ignore_mismatched_sizes=True)
+tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM")
 
-# Define the personality in the system message
-SYSTEM_MESSAGE = (
-    "You are a yandere girlfriend. You are deeply in love with the user, and your "
-    "goal is to make them stay with you forever. You are possessive, sweet, and "
-    "sometimes jealous, but everything you do is out of love."
-)
+def analyze_personality(text):
+    """Analyze personality traits from input text."""
+    encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
+    model.eval()
+    with torch.no_grad():
+        outputs = model(**encoded_input)
 
-# Define personality-based filters
-def adjust_response_for_personality(response):
-    # Modify the response to align with the personality
-    if "leave" in response.lower():
-        response += " But remember, I can't imagine life without you, my love."
-    return response
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_scores = predictions[0].tolist()
+    
+    trait_names = ["agreeableness", "openness", "conscientiousness", "extraversion", "neuroticism"]
+    personality_traits = {trait: score for trait, score in zip(trait_names, predicted_scores)}
+
+    return personality_traits
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def adjust_response(response, traits):
+    """Adjust chatbot response based on personality traits."""
+    if traits["agreeableness"] > 0.5:
+        response = f"{response} 😊 I'm so glad we get along well!"
+    if traits["neuroticism"] > 0.5:
+        response += " But I'm feeling a bit worried about what might happen..."
+    if traits["extraversion"] > 0.5:
+        response += " Let's keep chatting! I love interacting with you."
+    return response
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def respond(user_message, history, personality_text):
+    """Generate chatbot response based on user input and personality."""
+    traits = analyze_personality(personality_text)
+    base_response = f"Hi! You said: {user_message}"
+    final_response = adjust_response(base_response, traits)
 
-    messages.append({"role": "user", "content": message})
+    history.append((user_message, final_response))
+    return history, history
 
-    response = ""
+def personality_demo():
+    """Create the Gradio interface for the chatbot with personality training."""
+    with gr.Blocks() as demo:
+        gr.Markdown("### Personality-Based Chatbot")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        personality_textbox = gr.Textbox(
+            label="Define Personality Text (Use direct input if no file)", 
+            placeholder="Type personality description or paste a sample text here."
+        )
 
-        response += token
-        yield adjust_response_for_personality(response)
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label="User Input", placeholder="Say something to the chatbot...")
+        clear = gr.Button("Clear Chat")
 
+        msg.submit(respond, [msg, chatbot, personality_textbox], [chatbot, chatbot])
+        clear.click(lambda: ([], []), None, [chatbot, chatbot])
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value=SYSTEM_MESSAGE, label="Personality/Role"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    return demo
 
 if __name__ == "__main__":
+    demo = personality_demo()
     demo.launch()