Update app.py

app.py

@@ -3,30 +3,32 @@ import torch
 from diffusers import AutoPipelineForText2Image
 import base64
 from io import BytesIO
-
+from generate_propmts.py import generate_prompt
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-def generate_image(prompt):
+def generate_image(text, sentence_mapping, character_dict, selected_style):
     try:
+        prompt,_ = generate_prompt(text, sentence_mapping, character_dict, selected_style)
         image = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
-        return image, None
+        buffered = BytesIO()
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        if isinstance(result, img_str):
+            image_bytes = base64.b64decode(result)
+            return image_bytes
     except Exception as e:
-        return None, str(e)
+        return None
 
 def inference(prompt):
-    print(f"Received prompt: {prompt}")
+    # Dictionary to store images results
+    images = {}
+    print(f"Received grouped_sentences: {grouped_sentences}")
     # Debugging statement
-    image, error = generate_image(prompt)
-    if error:
-        print(f"Error generating image: {error}")
-        # Debugging statement
-        return "Error: " + error
-    
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return img_str
+    with concurrent.images.ThreadPoolExecutor() as executor:
+        for paragraph_number, sentences in grouped_sentences.items():
+            combined_sentence = " ".join(sentences)
+            images[paragraph_number] = executor.submit(generate_image, combined_sentence, sentence_mapping, general_descriptions, selected_style)
+    repr images
 
 gradio_interface = gr.Interface(
     fn=inference,