Load ung dung

app.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from diffusers import StableDiffusionPipeline
+import torch
+import io
+from PIL import Image
+
+# Load the summarization model (lacos03/bart-base-finetuned-xsum)
+summarizer = pipeline("summarization", model="lacos03/bart-base-finetuned-xsum")
+
+# Load the prompt generation model (microsoft/Promptist)
+promptist_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
+promptist_tokenizer = AutoTokenizer.from_pretrained("microsoft/Promptist")
+
+# Load the base Stable Diffusion model
+model_id = "runwayml/stable-diffusion-v1-5"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Initialize the Stable Diffusion pipeline
+image_generator = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    use_auth_token=False
+).to(device)
+
+# Load LoRA weights
+lora_weights = "lacos03/std-1.5-lora-midjourney-1.0"
+image_generator.load_lora_weights(lora_weights)
+
+def summarize_and_generate_image(article_text):
+    # Step 1: Summarize the article and extract a title
+    summary = summarizer(article_text, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
+    title = summary.split(".")[0]  # Use the first sentence as the title
+
+    # Step 2: Generate a prompt from the title using Promptist
+    inputs = promptist_tokenizer(title, return_tensors="pt").to(device)
+    prompt_output = promptist_model.generate(**inputs, max_length=50, num_return_sequences=1)
+    generated_prompt = promptist_tokenizer.decode(prompt_output[0], skip_special_tokens=True)
+
+    # Step 3: Generate an image from the prompt
+    image = image_generator(
+        generated_prompt,
+        num_inference_steps=50,
+        guidance_scale=7.5
+    ).images[0]
+
+    # Step 4: Save the image to a BytesIO object for download
+    img_byte_arr = io.BytesIO()
+    image.save(img_byte_arr, format="PNG")
+    img_byte_arr.seek(0)
+
+    return title, generated_prompt, image, img_byte_arr
+
+# Define the Gradio interface
+def create_app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Article Summarizer and Image Generator")
+        gr.Markdown("Enter an article, get a summarized title, and generate an illustrative image.")
+        
+        # Input for article text
+        article_input = gr.Textbox(label="Input Article", lines=10, placeholder="Paste your article here...")
+        
+        # Output components
+        title_output = gr.Textbox(label="Generated Title")
+        prompt_output = gr.Textbox(label="Generated Prompt")
+        image_output = gr.Image(label="Generated Image")
+        
+        # Buttons
+        submit_button = gr.Button("Generate")
+        copy_button = gr.Button("Copy Title")
+        download_button = gr.File(label="Download Image")
+        
+        # Define actions
+        submit_button.click(
+            fn=summarize_and_generate_image,
+            inputs=article_input,
+            outputs=[title_output, prompt_output, image_output, download_button]
+        )
+        copy_button.click(
+            fn=lambda x: x,
+            inputs=title_output,
+            outputs=gr.State(value=title_output),  # For copying to clipboard
+            _js="function(x) { navigator.clipboard.writeText(x); return x; }"
+        )
+
+    return demo
+
+# Launch the app
+if __name__ == "__main__":
+    app = create_app()
+    app.launch()

requirements.txt

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+gradio==3.50.2 transformers==4.38.2 torch==2.0.1 diffusers==0.21.4 pillow==9.5.0 safetensors==0.4.0