Update app.py

app.py

@@ -1,154 +1,176 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # Example for LLM
+# from diffusers import StableDiffusionPipeline # Example for Diffusion
+from peft import PeftModel
+import accelerate # Often needed for device_map='auto'
+import os
+import time # For basic timing/feedback
+
+# --- Global Placeholder (Alternative: Use gr.State for cleaner state management) ---
+# We will use gr.State in the Blocks interface, which is generally preferred.
+# loaded_model = None
+# loaded_tokenizer = None
+
+# --- Model Loading Function ---
+def load_models(base_model_id, lora_model_id, progress=gr.Progress(track_tqdm=True)):
+    """Loads the base model and applies the LoRA adapter."""
+    global loaded_model, loaded_tokenizer # If not using gr.State
+    model = None
+    tokenizer = None
+    status = "Starting model loading..."
+    progress(0, desc=status)
+    print(status)
+
+    if not base_model_id or not lora_model_id:
+        return None, None, "Error: Base Model ID and LoRA Model ID cannot be empty."
+
+    try:
+        # --- Load Base Model Tokenizer (for LLMs) ---
+        status = f"Loading tokenizer for {base_model_id}..."
+        progress(0.1, desc=status)
+        print(status)
+        tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+        if tokenizer.pad_token is None:
+            print("Setting pad_token to eos_token")
+            tokenizer.pad_token = tokenizer.eos_token
+
+        # --- Load Base Model ---
+        # Add quantization or other configs if needed
+        status = f"Loading base model: {base_model_id}..."
+        progress(0.3, desc=status)
+        print(status)
+        base_model = AutoModelForCausalLM.from_pretrained(
+            base_model_id,
+            torch_dtype=torch.bfloat16, # Or float16
+            device_map="auto",
+            trust_remote_code=True
+        )
+        progress(0.7, desc="Base model loaded.")
+        print("Base model loaded.")
+
+        # --- Load LoRA Adapter ---
+        status = f"Loading LoRA adapter: {lora_model_id}..."
+        progress(0.8, desc=status)
+        print(status)
+        model = PeftModel.from_pretrained(
+            base_model,
+            lora_model_id,
+        )
+        progress(0.95, desc="LoRA adapter applied.")
+        print("PEFT LoRA model loaded.")
+
+        model.eval() # Set model to evaluation mode
+        status = "Models loaded successfully!"
+        progress(1.0, desc=status)
+        print(status)
+        # Return the loaded model and tokenizer to be stored in gr.State
+        return model, tokenizer, status
+
+    except Exception as e:
+        error_msg = f"Error loading models: {str(e)}"
+        print(error_msg)
+        # Ensure we return None for model/tokenizer on error
+        return None, None, error_msg
+
+# --- Inference Function ---
+def generate_text(
+    state_model, state_tokenizer, # Receive model/tokenizer from gr.State
+    prompt, max_new_tokens, temperature,
+    progress=gr.Progress(track_tqdm=True)
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+    """Generates text using the loaded model."""
+    if state_model is None or state_tokenizer is None:
+        return "Error: Models not loaded. Please load models first."
+
+    status = "Tokenizing prompt..."
+    progress(0.1, desc=status)
+    print(status)
+    try:
+        inputs = state_tokenizer(prompt, return_tensors="pt").to(state_model.device)
+
+        status = "Generating text..."
+        progress(0.3, desc=status)
+        print(status)
+        with torch.no_grad():
+            outputs = state_model.generate(
+                **inputs,
+                max_new_tokens=int(max_new_tokens), # Ensure it's int
+                temperature=float(temperature),   # Ensure it's float
+                pad_token_id=state_tokenizer.pad_token_id
+                # Add other parameters like top_k, top_p if desired
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
+        status = "Decoding output..."
+        progress(0.9, desc=status)
+        print(status)
+        result = state_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        progress(1.0, desc="Generation complete.")
+        print("Generation complete.")
+        return result
+
+    except Exception as e:
+        error_msg = f"Error during generation: {str(e)}"
+        print(error_msg)
+        return error_msg
+
+
+# --- Gradio Interface Definition ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    # Using gr.State to hold the loaded model and tokenizer objects
+    # This state persists within the user's session
+    model_state = gr.State(None)
+    tokenizer_state = gr.State(None)
+
+    gr.Markdown("# 🎛️ Dynamic LoRA Model Loader & Generator (Gradio)")
+    gr.Markdown(
+        "Enter the Hugging Face IDs for the base model and your LoRA adapter repository. "
+        "Then, load the models and generate text."
+        "\n**Note:** Ensure your LoRA file is named appropriately (e.g., `adapter_model.safetensors` or specify filename if loader supports it) and your Space has adequate hardware (GPU recommended)."
+    )
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## Configuration")
+            base_model_input = gr.Textbox(
+                label="Base Model ID (Hugging Face)",
+                placeholder="e.g., meta-llama/Meta-Llama-3-8B",
+                value="meta-llama/Meta-Llama-3-8B" # Example default
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+            lora_model_input = gr.Textbox(
+                label="LoRA Model ID (Hugging Face - where lora.safetensors is)",
+                placeholder="e.g., YourUsername/YourLoraRepo"
             )
+            load_button = gr.Button("Load Models", variant="primary")
+            status_output = gr.Textbox(label="Loading Status", interactive=False)
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
+        with gr.Column(scale=2):
+            gr.Markdown("## Inference")
+            prompt_input = gr.Textbox(label="Enter Prompt:", lines=5, placeholder="Once upon a time...")
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+                max_tokens_slider = gr.Slider(label="Max New Tokens", minimum=10, maximum=1024, value=200, step=10)
+                temp_slider = gr.Slider(label="Temperature", minimum=0.1, maximum=1.5, value=0.7, step=0.05)
+            generate_button = gr.Button("Generate Text", variant="primary")
+            generated_output = gr.Textbox(label="Generated Output", lines=10, interactive=False)
+
+    # --- Connect Actions ---
+    load_button.click(
+        fn=load_models,
+        inputs=[base_model_input, lora_model_input],
+        # Outputs: model state, tokenizer state, status message textbox
+        outputs=[model_state, tokenizer_state, status_output],
+        show_progress="full" # Show progress bar
+    )
 
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+    generate_button.click(
+        fn=generate_text,
+        # Inputs: model state, tokenizer state, prompt, sliders
+        inputs=[model_state, tokenizer_state, prompt_input, max_tokens_slider, temp_slider],
+        outputs=[generated_output], # Output: generated text textbox
+        show_progress="full" # Show progress bar
     )
 
+# --- Launch the Gradio App ---
+# HF Spaces automatically runs this when deploying app.py
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch() # Use share=True for a public link if running locally