Update app.py

app.py

@@ -1,106 +1,40 @@
 import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import InferenceClient
 
-# --- Model Initialization ---
+# Initialize the InferenceClient with your custom model hosted on Hugging Face.
+client = InferenceClient(model="pro-grammer/MindfulAI")
 
-# Paths for tokenizer and your model checkpoint
-tokenizer_path = "facebook/opt-1.3b"
-model_path = "transfer_learning_therapist.pth"
-
-# Load tokenizer and set pad token if needed
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-# Set device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# Load the base model and then update with your checkpoint
-model = AutoModelForCausalLM.from_pretrained(tokenizer_path)
-checkpoint = torch.load(model_path, map_location=device)
-model_dict = model.state_dict()
-pretrained_dict = {k: v for k, v in checkpoint['model_state_dict'].items() if k in model_dict}
-model_dict.update(pretrained_dict)
-model.load_state_dict(model_dict)
-model.to(device)
-model.eval()
-
-# --- Inference Function ---
-
-def generate_response(prompt, max_new_tokens=150, temperature=0.7, top_p=0.9, repetition_penalty=1.2):
-    """Generates a response from your model based on the prompt."""
-    model.eval()
-    model.config.use_cache = True
-
-    prompt = prompt.strip()
-    if not prompt:
-        return "Please provide a valid input."
-
-    # Tokenize the input prompt
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    
-    try:
-        with torch.no_grad():
-            outputs = model.generate(
-                inputs.input_ids,
-                attention_mask=inputs.attention_mask,
-                max_new_tokens=max_new_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                do_sample=True,
-                pad_token_id=tokenizer.pad_token_id,
-                eos_token_id=tokenizer.eos_token_id,
-                repetition_penalty=repetition_penalty,
-                num_beams=1,             # greedy decoding
-                no_repeat_ngram_size=3,  # avoid repeated phrases
-            )
-    except Exception as e:
-        return f"Error generating response: {e}"
-    finally:
-        model.config.use_cache = False
-    
-    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    # If your prompt is formatted with role markers (e.g., "Therapist:"), extract only that part:
-    if "Therapist:" in full_response:
-        therapist_response = full_response.split("Therapist:")[-1].strip()
-    else:
-        therapist_response = full_response.strip()
-    return therapist_response
-
-# --- Gradio Interface Function ---
-
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    """
-    Build the conversation context by combining the system message and the dialogue history,
-    then generate a new response from the model.
-    """
-    # Create a conversation prompt with your desired role labels.
-    conversation = f"System: {system_message}\n"
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    # Build conversation context
+    messages = [{"role": "system", "content": system_message}]
     for user_msg, assistant_msg in history:
-        conversation += f"Human: {user_msg}\nTherapist: {assistant_msg}\n"
-    conversation += f"Human: {message}\nTherapist:"
-    
-    response = generate_response(
-        conversation,
-        max_new_tokens=max_tokens,
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    # Use the chat_completion method to stream the model's response
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
         temperature=temperature,
         top_p=top_p,
-    )
-    
-    history.append((message, response))
-    return history, history
-
-# --- Gradio ChatInterface Setup ---
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
 
+# Customize the ChatInterface with additional input controls
 demo = gr.ChatInterface(
     fn=respond,
     title="MindfulAI Chat",
-    description="Chat with MindfulAI – an AI Therapist powered by your custom model.",
+    description="Chat with MindfulAI – your hosted AI Therapist.",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly AI Therapist.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max new tokens"),
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        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)"),
     ],