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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
adapter_model = "Sanjay002/falcon-7b-mental-health-finetuned"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map=None, # Don't map to GPU
torch_dtype=torch.float32 if device == "cpu" else torch.bfloat16,
trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_model)
model.to(device)
model.eval()
def chat(message):
inputs = tokenizer(message, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=150, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
gr.Interface(fn=chat, inputs="text", outputs="text", title="🧠 Mental Health Chatbot").queue().launch()
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