Update app.py

app.py

@@ -1,69 +1,15 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
 import gradio as gr
+import subprocess
 
-# Use GPU if available
-device = "cuda" if torch.cuda.is_available() else "cpu"
+def merge_model():
+    subprocess.run(["python", "merge_and_save_model.py"], check=True)
+    return "Model merged and saved successfully!"
 
-# Base model and adapter paths
-base_model_name = "microsoft/phi-2"  # Pull from HF Hub directly
-adapter_path = "Shriti09/Microsoft-Phi-QLora"  # Update with your Hugging Face repo path
+with gr.Blocks() as demo:
+    gr.Markdown("<h1>🧠 Phi-2 QLoRA Model Merger</h1>")
+    merge_button = gr.Button("Merge Model")
+    output = gr.Textbox(label="Merge Status")
 
-print("🔧 Loading base model...")
-# Load the base model
-base_model = AutoModelForCausalLM.from_pretrained(
-    base_model_name,
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
-)
+    merge_button.click(merge_model, [], output)
 
-print("🔧 Loading LoRA adapter...")
-# Load the LoRA adapter
-adapter_model = PeftModel.from_pretrained(base_model, adapter_path)
-
-print("🔗 Merging adapter into base model...")
-# Merge adapter into the base model
-merged_model = adapter_model.merge_and_unload()
-merged_model.eval()
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-print("✅ Model ready for inference!")
-
-# Text generation function
-def generate_text(prompt):
-    # Tokenize the input
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-    with torch.no_grad():
-        outputs = merged_model.generate(
-            **inputs,
-            max_new_tokens=150,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9,
-            pad_token_id=tokenizer.eos_token_id
-        )
-
-    # Decode and return the generated response
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("<h1>🧠 Phi-2 QLoRA Text Generator</h1>")
-    
-    # Textbox for user input
-    prompt = gr.Textbox(label="Enter your prompt:", lines=2)
-    
-    # Output textbox for generated text
-    output = gr.Textbox(label="Generated text:", lines=5)
-    
-    # Button to trigger text generation
-    generate_button = gr.Button("Generate Text")
-
-    # Set the button action to generate text
-    generate_button.click(generate_text, inputs=prompt, outputs=output)
-
-# Launch the app
 demo.launch(share=True)