Update app.py

app.py

@@ -1,56 +1,17 @@
+from langchain.tools import ElevenLabsText2SpeechTool
 import gradio as gr
-from PIL import Image
-import numpy as np
-import mlx.core as mx
-from stable_diffusion import StableDiffusion
 
-def generate_images(prompt, n_images=4, steps=50, cfg=7.5, negative_prompt="", n_rows=1):
-    sd = StableDiffusion()
+tts = ElevenLabsText2SpeechTool()
 
-    # Generate the latent vectors using diffusion
-    latents = sd.generate_latents(
-        prompt,
-        n_images=n_images,
-        cfg_weight=cfg,
-        num_steps=steps,
-        negative_text=negative_prompt,
-    )
-    for x_t in latents:
-        mx.simplify(x_t)
-        mx.simplify(x_t)
-        mx.eval(x_t)
+def generate_speech(text_to_speak):
+    speech_file = tts.run(text_to_speak)
+    return speech_file
 
-    # Decode them into images
-    decoded = []
-    for i in range(0, n_images):
-        decoded_img = sd.decode(x_t[i:i+1])
-        mx.eval(decoded_img)
-        decoded.append(decoded_img)
-
-    # Arrange them on a grid
-    x = mx.concatenate(decoded, axis=0)
-    x = mx.pad(x, [(0, 0), (8, 8), (8, 8), (0, 0)])
-    B, H, W, C = x.shape
-    x = x.reshape(n_rows, B // n_rows, H, W, C).transpose(0, 2, 1, 3, 4)
-    x = x.reshape(n_rows * H, B // n_rows * W, C)
-    x = (x * 255).astype(mx.uint8)
-
-    # Convert to PIL Image
-    return Image.fromarray(x.__array__())
-
-iface = gr.Interface(
-    fn=generate_images,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Slider(minimum=1, maximum=10, step=1, value=4, label="Number of Images"),
-        gr.Slider(minimum=20, maximum=100, step=1, value=50, label="Steps"),
-        gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=7.5, label="CFG Weight"),
-        gr.Textbox(default="", label="Negative Prompt"),
-        gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Rows")
-    ],
-    outputs="image",
-    title="Stable Diffusion Image Generator",
-    description="Generate images from a textual prompt using Stable Diffusion"
+demo = gr.Interface(
+    fn=generate_speech,
+    inputs=gr.Textbox(label="Enter Text"),
+    outputs=gr.Audio(label="Generated Speech"),
 )
 
-iface.launch()
+if __name__ == "__main__":
+    demo.launch()