Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import io
+import torch
+from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
+
+# Function to generate images based on user input
+def generate_images(text, size, num_images, quality):
+    # Placeholder function, replace with your actual implementation
+    generated_images = []
+    for _ in range(num_images):
+        # Use your model to generate an image based on the input text
+        # Example:
+        # image = model.generate_image(text, size, quality)
+        # generated_images.append(image)
+        pass
+    return generated_images
+
+# Function to convert PIL image to bytes
+def pil_to_bytes(image):
+    img_byte_array = io.BytesIO()
+    image.save(img_byte_array, format='PNG')
+    return img_byte_array.getvalue()
+
+# Streamlit interface
+st.title('Text-to-Image AI-based Chatbot')
+
+# Text input box
+user_input = st.text_input("Enter your text here:")
+
+# Select models
+model_opts = ["amused/amused-256", "amused/amused-512", "runwayml/stable-diffusion-v1-5"]
+selected_model = st.selectbox("Select model:", model_opts)
+
+if selected_model == 'amused/amused-256':
+    print("Model selected: 256")
+    pipe = DiffusionPipeline.from_pretrained('amused/amused-256', variant="fp16", torch_dtype=torch.float16)
+elif selected_model == 'amused/amused-512':
+    print("Model selected: 512")
+    pipe = DiffusionPipeline.from_pretrained('amused/amused-512', variant="fp16", torch_dtype=torch.float16)
+else: 
+    print("Model selected: diffusion")
+    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16)
+
+# # Image size input
+# image_size_options = ["1080x1080", "1280x720", "1920x1080"]
+# selected_size = st.selectbox("Select image size:", image_size_options)
+
+# Convert selected size to width and height
+# width, height = map(int, selected_size.split('x'))
+
+# Number of images input
+num_images = st.number_input("Number of images to generate:", min_value=1, max_value=10, value=3, step=1)
+# Image quality input
+image_quality = st.slider("Select image quality:", min_value=1, max_value=100, value=50, step=1)
+
+# Generate button
+if st.button("Generate Images"):
+    if user_input:
+       
+        # Example image
+        # example_image = Image.open("example_img.jpg")
+        # st.image(example_image, caption='Example Image', use_column_width=True)
+        # img_bytes = pil_to_bytes(example_image)
+        # st.download_button(label=f"Download Image ", data=img_bytes, file_name=f"generated_image_example.png", mime='image/png')
+        pipe = pipe.to('cuda')
+
+        with st.spinner('Generating...'):
+            image = pipe(
+                user_input, 
+                num_images_per_prompt=num_images, 
+                num_inference_steps=image_quality,
+                generator=torch.Generator('cuda').manual_seed(8)
+                ).images
+        st.image(image  , use_column_width=True)
+        # img_bytes = pil_to_bytes(image)
+        # st.download_button(label=f"Download Image ", data=img_bytes, file_name=f"generated_image_example.png", mime='image/png')
+        
+
+        # # Generate images based on user input
+        # generated_images = generate_images(user_input, (width, height), num_images, image_quality)
+        # if generated_images:
+        #     for i, img in enumerate(generated_images):
+        #         st.image(img, caption=f"Generated Image {i+1}", use_column_width=True)
+        #         # Download link for each image
+        #         img_bytes = pil_to_bytes(img)
+        #         st.download_butt