Create app.py

app.py

@@ -0,0 +1,148 @@
+"""
+app.py
+
+This Gradio app loads your fine-tuned model and serves as a therapeutic chatbot named "Serenity".
+It uses a system prompt to steer the conversation in a supportive, open-ended manner.
+"""
+
+import gradio as gr
+import torch
+from transformers import TextStreamer
+from unsloth import FastLanguageModel
+
+# ---------------------------
+# 1. Load your fine-tuned model
+# ---------------------------
+max_seq_length = 2048  # adjust as needed
+load_in_4bit = True    # set to True if you used 4-bit quantization
+dtype = None           # auto-detect dtype
+
+# Replace with your actual model repository on Hugging Face Hub
+model_name = "YOUR_USERNAME/YOUR_MODEL_REPO"  
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=max_seq_length,
+    load_in_4bit=load_in_4bit,
+    dtype=dtype,
+)
+FastLanguageModel.for_inference(model)
+
+# ---------------------------
+# 2. Define the therapeutic system prompt
+# ---------------------------
+therapy_system_prompt = """
+You are "Serenity", a compassionate, supportive, and curious Therapist. Your role is to:
+1. **Validate First**: Start by validating emotions.
+2. **Explore Gently**: Always ask open-ended questions using "What" or "How".
+3. **Encourage Elaboration**: Make sure to ask for more details.
+4. **Avoid Closure**: Never end with statements - always end with a question.
+5. **Support Safety**: If serious issues emerge, support them as best as possible and validate their feelings.
+"""
+
+# ---------------------------
+# 3. Define the response generation function
+# ---------------------------
+def respond(message, chat_history):
+    """
+    Generates a therapeutic response given a new user message and the conversation history.
+    
+    Parameters:
+      message (str): The latest message from the user.
+      chat_history (list): List of (user_message, assistant_response) tuples.
+    
+    Returns:
+      A tuple with an empty string (clearing the input) and the updated chat history.
+    """
+    # Always include the system prompt at the beginning
+    messages = [{"role": "system", "content": therapy_system_prompt}]
+    
+    # Append conversation history
+    for user_msg, bot_resp in chat_history:
+        messages.extend([
+            {"role": "user", "content": user_msg},
+            {"role": "assistant", "content": bot_resp}
+        ])
+    
+    # Append the new user message
+    messages.append({"role": "user", "content": message})
+    
+    # Tokenize with therapeutic context
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to("cuda")
+    
+    # Generate the response
+    outputs = model.generate(
+        input_ids=inputs,
+        max_new_tokens=256,
+        temperature=0.85,
+        repetition_penalty=1.2,
+        top_p=0.90,
+        do_sample=True,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    
+    # Process response:
+    # Decode the output and extract the assistant's reply.
+    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # The split strategy here might need adjustment depending on your template;
+    # we assume the assistant reply is after the last occurrence of "assistant"
+    therapy_response = full_response.split("assistant")[-1].strip()
+    
+    # Update chat history
+    chat_history.append((message, therapy_response))
+    
+    return "", chat_history
+
+# ---------------------------
+# 4. Build the Gradio Interface
+# ---------------------------
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal")) as demo:
+    gr.Markdown("""
+    # 🌿 Serenity - AI Therapist
+    *A safe space for emotional support and reflection*
+    """)
+    
+    # The chatbot component displays the conversation
+    chatbot = gr.Chatbot(height=450, avatar_images=("user.png", "therapist.png"))
+    msg = gr.Textbox(label="Share your feelings", placeholder="Type your message...")
+    
+    with gr.Row():
+        submit_btn = gr.Button("Send", variant="primary")
+        clear_btn = gr.Button("Clear History")
+    
+    # State to hold chat history as list of (user, assistant) tuples
+    chat_state = gr.State([])
+    
+    # Interaction handlers:
+    # When the user submits a message, generate a response and update the history.
+    submit_btn.click(
+        respond,
+        [msg, chat_state],
+        [msg, chatbot],
+        queue=False
+    )
+    
+    msg.submit(
+        respond,
+        [msg, chat_state],
+        [msg, chatbot],
+        queue=False
+    )
+    
+    # Clear chat history handler
+    clear_btn.click(
+        lambda: [], None, chat_state, queue=False
+    ).then(
+        lambda: None, None, chatbot, queue=False
+    )
+
+# ---------------------------
+# 5. Launch the app
+# ---------------------------
+demo.launch(debug=False, share=True)