Spaces:
Build error
Build error
File size: 1,883 Bytes
3506b46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import gradio as gr
import os
import keras_nlp
from transformers import AutoModelForCausalLM
# Set Kaggle API credentials using values from environment variables
os.environ["KAGGLE_USERNAME"] = os.environ.get("KAGGLE_USERNAME")
os.environ["KAGGLE_KEY"] = os.environ.get("KAGGLE_KEY")
# Load LoRA weights if you have them
LoRA_weights_path = "fined-tuned-model.lora.h5"
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
gemma_lm.backbone.enable_lora(rank=4) # Enable LoRA with rank 4
gemma_lm.preprocessor.sequence_length = 512 # Limit sequence length
gemma_lm.backbone.load_lora_weights(LoRA_weights_path) # Load LoRA weights
# Define the response generation function
def generate_response(message):
# Create a prompt template
template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
# Create the prompt with the current message
prompt = template.format(instruction=message, response="")
print("Prompt:\n", prompt)
# Generate response from the model
response = gemma_lm.generate(prompt, max_length=256)
# Only keep the generated response
response = response.split("Response:")[-1].strip()
print("Generated Response:\n", response)
# Extract and return the generated response text
return response # Adjust this if your model's output structure differs
# Create the Gradio chat interface
interface = gr.Interface(
fn=generate_response, # Function that generates responses
inputs=gr.Textbox(placeholder="Hello, I am Sage, your mental health advisor", lines=2, scale=7),
outputs=gr.Textbox(),
title="Sage, your Mental Health Advisor",
# description="Chat with Sage, your mental health advisor.",
# live=True
)
proxy_prefix = os.environ.get("PROXY_PREFIX")
# Launch the Gradio app
interface.launch(server_name="0.0.0.0", server_port=8080, root_path=proxy_prefix, share=True)
|