Update app.py

app.py

@@ -2,15 +2,22 @@
 
 from transformers import pipeline
 import gradio as gr
-from diffusers import DiffusionPipeline
-from diffusers import FluxPipeline
+# from diffusers import DiffusionPipeline
+# from diffusers import FluxPipeline
 import torch
 import time
 
 # Load models
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-dra-en")
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
-image_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16).to("cpu")
+# image_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16).to("cpu")
+
+API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
+headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
 
 
 # Translate Tamil to English
@@ -28,10 +35,13 @@ def summarize_english_text(paragraph):
 
 
 # Generate image from English text
-def english_text_to_image(text):
-    image = image_pipe(text).images[0]
+def english_text_to_image(prompt):
+    image_bytes = query({
+        "inputs": prompt,
+    })
+    image = Image.open(io.BytesIO(image_bytes))
     return image
-
+  
 
 with gr.Blocks() as app:
     gr.Markdown("# Multifunctional Gradio App")