Update app.py

app.py

@@ -1,31 +1,18 @@
-!pip install accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import transformers
-import torch
-import streamlit as st
+pip install tensorflow keras_cv --upgrade --quiet
+import time
+import keras_cv
+from tensorflow import keras
+import matplotlib.pyplot as plt
+model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
+images = model.text_to_image("photograph of an astronaut riding a horse", batch_size=3)
 
-model = "tiiuae/falcon-7b"
 
-tokenizer = AutoTokenizer.from_pretrained(model)
-text_input= st.text_area("Enter some text: ")
-    if text_input: 
-        sequences = pipeline(
-        text_input,
-        max_length=200,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-    for seq in sequences:
-        print(f"Result: {seq['generated_text']}")
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    device_map="auto",
-)
+def plot_images(images):
+    plt.figure(figsize=(20, 20))
+    for i in range(len(images)):
+        ax = plt.subplot(1, len(images), i + 1)
+        plt.imshow(images[i])
+        plt.axis("off")
 
 
+plot_images(images)