Create app.py

app.py

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+import gradio as gr
+import torch
+import json
+import time
+import subprocess
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Initialize DeepSeek
+model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.to("cpu")
+
+def generate_prompts_and_image(input_text):
+    start_time = time.time()
+
+    # Generate prompts with DeepSeek
+    prompt = f"""
+    Input: "{input_text}"
+    Task: Generate concise 'Positive' and 'Negative' AI image prompts for Stable Diffusion based on the input above. Output the prompts directly, no extra text or examples.
+    """
+    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
+    outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7, top_p=0.9, do_sample=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+    if response.startswith(prompt):
+        response = response[len(prompt):].strip()
+
+    # Split response into positive and negative (assuming two lines)
+    lines = [line.strip() for line in response.split("\n") if line.strip()]
+    positive = lines[0] if lines else "No positive prompt generated"
+    negative = lines[1] if len(lines) > 1 else "No negative prompt generated"
+
+    # Load and modify ComfyUI workflow
+    workflow_path = "workflow.json"
+    temp_workflow_path = "temp_workflow.json"
+    if not os.path.exists(workflow_path):
+        return {"error": "workflow.json not found", "time_taken": f"{time.time() - start_time:.2f} seconds"}
+
+    with open(workflow_path, "r") as f:
+        workflow = json.load(f)
+
+    # Inject prompts into nodes 6 (positive) and 7 (negative) from your workflow.json
+    workflow["6"]["widgets_values"][0] = positive  # Positive prompt (CLIPTextEncode)
+    workflow["7"]["widgets_values"][0] = negative  # Negative prompt (CLIPTextEncode)
+
+    # Save temporary workflow
+    with open(temp_workflow_path, "w") as f:
+        json.dump(workflow, f)
+
+    # Run ComfyUI via subprocess
+    comfyui_dir = os.path.join(os.path.dirname(__file__), "ComfyUI")
+    comfyui_main = os.path.join(comfyui_dir, "main.py")
+    output_dir = os.path.join(comfyui_dir, "output")  # ComfyUI default output dir
+    os.makedirs(output_dir, exist_ok=True)
+
+    try:
+        # Note: ComfyUI's main.py doesn't use --workflow directly; it reads from the workflow file
+        result = subprocess.run(
+            ["python", comfyui_main, "--input-directory", comfyui_dir, "--output-directory", output_dir],
+            cwd=comfyui_dir,
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        # ComfyUI saves images as ComfyUI_<timestamp>.png in output/
+        image_files = [f for f in os.listdir(output_dir) if f.startswith("ComfyUI") and f.endswith(".png")]
+        if not image_files:
+            image = f"ComfyUI ran but no image found: {result.stdout}\n{result.stderr}"
+        else:
+            image = os.path.join(output_dir, image_files[-1])  # Use the latest image
+    except subprocess.CalledProcessError as e:
+        image = f"ComfyUI failed: {e.stdout}\n{e.stderr}"
+
+    elapsed_time = time.time() - start_time
+    return {
+        "positive": positive,
+        "negative": negative,
+        "image": image,
+        "time_taken": f"{elapsed_time:.2f} seconds"
+    }
+
+def gradio_interface(input_text):
+    result = generate_prompts_and_image(input_text)
+    return result["image"], json.dumps({
+        "positive": result["positive"],
+        "negative": result["negative"],
+        "time_taken": result["time_taken"]
+    }, indent=2)
+
+demo = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(label="Input Text", lines=10, placeholder="Paste your input text here..."),
+    outputs=[
+        gr.Image(label="Generated Image"),
+        gr.Textbox(label="Generated Prompts (JSON)")
+    ],
+    title="Prompt and Image Generator"
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)