Update app.py

app.py

@@ -1,6 +1,27 @@
-from diffusers import DiffusionPipeline
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from PIL import Image
+import base64
+import io
 
-pipe = DiffusionPipeline.from_pretrained("cloudqi/cqi_text_to_image_pt_v0")
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("cloudqi/cqi_text_to_image_pt_v0")
+tokenizer = AutoTokenizer.from_pretrained("cloudqi/cqi_text_to_image_pt_v0")
 
-prompt = "Gato em alta qualidade na neve\n"
-image = pipe(prompt).images[0]
+def generate_image(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    output_ids = model.generate(**inputs, max_length=256)
+    output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # Decode base64 to image
+    if "data:image/png;base64," in output_str:
+        b64_img = output_str.split("data:image/png;base64,")[1]
+        image_data = base64.b64decode(b64_img)
+        image = Image.open(io.BytesIO(image_data))
+        return image
+    return "No image found in output."
+
+demo = gr.Interface(fn=generate_image, inputs="text", outputs="image", title="CQI Text-to-Image")
+
+demo.launch()