Update app.py

app.py

@@ -3,23 +3,34 @@ import os
 import gradio as gr
 import keras
 import keras_hub
-from huggingface_hub import from_pretrained_keras
+# 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 ---
-# Make sure this repo_id is correct and does not have any hidden characters at the end
+# --- 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"
-print(f"Loading merged model from Hub: {repo_id}")
-gemma_lm = from_pretrained_keras(repo_id)
+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 ---
+
+# --- 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."
@@ -38,7 +49,7 @@ def revise_abstract(draft_abstract, grant_type):
     parts = output.split("Revised Abstract:")
     return parts[1].strip() if len(parts) > 1 else output.strip()
 
-# --- 3. CREATE THE GRADIO INTERFACE ---
+# --- 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.")