Update app.py

app.py

@@ -4,7 +4,6 @@ import streamlit as st
 from huggingface_hub import InferenceClient
 import os
 import torch
-from diffusers import FluxPipeline
 
 #Use an API endpoint to call in the MS inference client 
 apiToken = os.getenv("my_API_Key")
@@ -14,8 +13,6 @@ client = InferenceClient(api_key=apiToken)
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 #Instantiate the image generation pipeline
-imagePipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
-imagePipeline.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU
 
 #Intro text
 st.write("Are you writing a story but don't know where to start?")
@@ -44,18 +41,4 @@ if writingPrompt:
     st.write(response_content)
 
     #Image generation
-    summary = summarizer(response_content, max_length=130, min_length=30, do_sample=False) 
-    
-    """
-    prompt = "A cat holding a sign that says hello world"
-    image = imagePipeline(
-        prompt,
-        guidance_scale=0.0,
-        num_inference_steps=4,
-        max_sequence_length=256,
-        generator=torch.Generator("cpu").manual_seed(0)
-    ).images[0]
-    
-    st.image(image)
-
-    """
+    summary = summarizer(response_content, max_length=130, min_length=30, do_sample=False)