Update app.py

app.py

@@ -1,43 +1,76 @@
-import torch
 import gradio as gr
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from transformers import CLIPImageProcessor
-from PIL import Image
+import torch
+from diffusers import StableDiffusionPipeline
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+import openai
+import cv2
 import numpy as np
 
-def load_controlnet_model():
-    """Load Stable Diffusion ControlNet pipeline with IP-Adapter."""
-    controlnet = ControlNetModel.from_pretrained(
-        "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16
-    )
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        "runwayml/stable-diffusion-v1-5",
-        controlnet=controlnet,
-        torch_dtype=torch.float16
-    ).to("cuda")
-    return pipe
-
-def preprocess_image(image):
-    """Convert image to depth map for ControlNet input."""
-    image = image.convert("L")  # Convert to grayscale
-    image = np.array(image)
-    depth_map = np.clip(image, 0, 255)
-    return Image.fromarray(depth_map)
-
-def generate_image(input_image):
-    """Generate an image using Stable Diffusion ControlNet with IP-Adapter."""
-    pipe = load_controlnet_model()
-    processed_image = preprocess_image(input_image)
-    result = pipe(image=processed_image, num_inference_steps=30).images[0]
-    return result
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Image(type="pil", label="Upload reference image"),
-    outputs=gr.Image(type="pil", label="Generated Image"),
-    title="Stable Diffusion with ControlNet and IP-Adapter",
-    description="Generate images with precise object placement and consistent style using ControlNet and IP-Adapter.",
+# Load Stable Diffusion pipeline
+sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+if torch.cuda.is_available():
+    sd_pipe.to("cuda")
+
+# Load AI voiceover model
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+# OpenAI API key for voice and physics interactions
+openai.api_key = "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"
+
+def generate_image(prompt, emotion):
+    emotion_map = {"Happy": "smiling", "Angry": "frowning", "Sad": "crying"}
+    prompt = f"{prompt}, {emotion_map.get(emotion, '')}"
+    image = sd_pipe(prompt).images[0]
+    return image
+
+def generate_voiceover(text):
+    inputs = processor(text, return_tensors="pt")
+    speech = tts_model.generate(**inputs)
+    return speech
+
+def apply_physics(image, effect):
+    img_array = np.array(image)
+    if effect == "Wind":
+        kernel = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]])
+        img_array = cv2.filter2D(img_array, -1, kernel)
+    elif effect == "Falling":
+        img_array = np.roll(img_array, 10, axis=0)
+    return img_array
+
+def track_facial_expressions():
+    cap = cv2.VideoCapture(0)
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    while True:
+        ret, frame = cap.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+        for (x, y, w, h) in faces:
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        cv2.imshow('Face Tracking', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+
+def animate_scene(prompt, emotion, physics, text):
+    image = generate_image(prompt, emotion)
+    image = apply_physics(image, physics)
+    speech = generate_voiceover(text)
+    return image, speech
+
+iface = gr.Interface(
+    fn=animate_scene,
+    inputs=[
+        gr.Textbox(label="Character Description"),
+        gr.Radio(["Happy", "Angry", "Sad"], label="Emotion"),
+        gr.Radio(["None", "Wind", "Falling"], label="Physics Effect"),
+        gr.Textbox(label="Dialogue")
+    ],
+    outputs=[
+        gr.Image(label="Generated Scene"),
+        gr.Audio(label="Voiceover")
+    ]
 )
 
-demo.launch(share=True)
+iface.launch()