Update app.py

app.py

@@ -1,76 +1,47 @@
 import gradio as gr
 import torch
 from diffusers import StableDiffusionPipeline
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
-import openai
-import cv2
-import numpy as np
-
-# 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,
+from transformers import pipeline
+from gtts import gTTS
+import os
+
+def generate_character(description, emotion, physics, dialogue):
+    # Load Stable Diffusion Model (Optimized for Speed)
+    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+    pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # Modify description based on emotion
+    emotion_map = {"Happy": "smiling", "Angry": "furious", "Sad": "teary-eyed"}
+    description = f"{description}, {emotion_map.get(emotion, '')}"
+    
+    # Generate Image
+    image = pipe(description).images[0]
+    image.save("output.png")
+    
+    # Generate Voiceover
+    try:
+        tts_model = pipeline("text-to-speech", model="facebook/mms-tts-eng")
+        speech_output = tts_model(dialogue)
+        speech_path = "output.wav"
+        with open(speech_path, "wb") as f:
+            f.write(speech_output["audio"])
+    except Exception:
+        tts = gTTS(dialogue)
+        speech_path = "output.mp3"
+        tts.save(speech_path)
+    
+    return image, speech_path
+
+gui = gr.Interface(
+    fn=generate_character,
     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")
-    ]
+    outputs=[gr.Image(label="Generated Character"), gr.Audio(label="Voiceover")],
+    allow_flagging="never"
 )
 
-iface.launch()
+gui.launch(debug=True)