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# app.py
import os
import gradio as gr
import keras
import keras_hub
# Import the specific downloader function
from huggingface_hub import hf_hub_download
# Set Keras backend
os.environ["KERAS_BACKEND"] = "jax"
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "1.00"
# --- 1. LOAD THE MERGED MODEL FROM THE HUB (CORRECTED METHOD) ---
# Define your repository and the filename of the model
repo_id = "Tarive/lora_research_abstracts"
model_filename = "model.keras" # The name we used during the upload step
print(f"Downloading model file '{model_filename}' from Hub repo: {repo_id}")
# Step 1: Explicitly download the .keras file and get its local path in the cache
model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
print(f"Loading merged model from local path: {model_path}")
# Step 2: Load the model directly from the specific file path
# This avoids the directory format error.
gemma_lm = keras.models.load_model(model_path)
# Compile the model with a sampler for generation
gemma_lm.compile(sampler=keras_hub.samplers.TopKSampler(k=5))
print("Model loaded and compiled successfully.")
# --- 2. DEFINE THE INFERENCE FUNCTION (No changes needed here) ---
def revise_abstract(draft_abstract, grant_type):
if not draft_abstract or not grant_type:
return "Error: Please provide both a draft abstract and a grant type."
template = (
"Instruction:\n"
"You are an expert grant writer. Rewrite the following draft abstract to be more impactful and clear, "
"following the specific conventions of a {activity_code} grant. Ensure the most compelling claims are front-loaded.\n\n"
"Input Draft:\n"
"{unoptimized_abstract}\n\n"
"Revised Abstract:"
)
prompt = template.format(unoptimized_abstract=draft_abstract, activity_code=grant_type)
output = gemma_lm.generate(prompt, max_length=1024)
parts = output.split("Revised Abstract:")
return parts[1].strip() if len(parts) > 1 else output.strip()
# --- 3. CREATE THE GRADIO INTERFACE (No changes needed here) ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# Grant Abstract Revision Tool (Fine-Tuned on Gemma)")
gr.Markdown("Enter a draft abstract and select its grant type. The model will rewrite it to be more impactful, based on patterns from successfully funded NIH grants.")
with gr.Row():
draft_input = gr.Textbox(lines=15, label="Input Draft Abstract", placeholder="Paste your draft abstract here...")
grant_type_input = gr.Dropdown(
["R01", "R21", "F32", "T32", "P30", "R41", "R43", "R44", "K99"],
label="Grant Type (Activity Code)",
info="Select the grant mechanism you are targeting."
)
submit_button = gr.Button("Revise Abstract", variant="primary")
revised_output = gr.Textbox(lines=15, label="Model's Revised Abstract", interactive=False)
submit_button.click(fn=revise_abstract, inputs=[draft_input, grant_type_input], outputs=revised_output)
gr.Examples(
examples=[
["SUMMARY \nA pressing concern exists regarding lead poisoning...This study aimed to optimize and validate a dried blood spot collection device...", "R41"],
["This project is about figuring out how macrophages and S. flexneri interact...Our study will look at this in vitro and in vivo...", "R21"]
],
inputs=[draft_input, grant_type_input]
)
print("Launching Gradio app...")
demo.launch() |