Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import pipeline
+from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
+from diffusers.utils import export_to_gif
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+from gtts import gTTS
+from moviepy.editor import VideoFileClip, AudioFileClip
+
+# Load the text generation model
+generator = pipeline('text-generation', model='distilgpt2')
+
+def generate_text(prompt):
+    response = generator(prompt, max_length=150, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Text-to-speech conversion
+def text_to_speech(text, filename='output_audio.mp3'):
+    tts = gTTS(text)
+    tts.save(filename)
+    return filename
+
+# Generate animation using AnimateDiffPipeline
+def create_animation(prompt, output_file='animation.gif'):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.float16 if device == "cuda" else torch.float32
+    step = 4
+    repo = "ByteDance/AnimateDiff-Lightning"
+    ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
+    base = "emilianJR/epiCRealism"
+
+    # Load adapter and pipeline
+    adapter = MotionAdapter().to(device, dtype)
+    adapter.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
+    pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
+    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
+
+    # Generate animation based on prompt
+    output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step)
+    export_to_gif(output.frames[0], output_file)
+
+    return output_file
+
+# Combine animation and audio into a video
+def create_video(animation_file, audio_file, output_file='output_video.mp4'):
+    clip = VideoFileClip(animation_file)
+    audio = AudioFileClip(audio_file)
+    clip = clip.set_audio(audio)
+    clip.write_videofile(output_file, fps=24)
+
+def generate_educational_video(prompt):
+    # Step 1: Generate text from prompt
+    generated_text = generate_text(prompt)
+
+    # Step 2: Convert text to speech
+    audio_file = text_to_speech(generated_text)
+
+    # Step 3: Create animation based on prompt
+    animation_file = create_animation(prompt)
+
+    # Step 4: Assemble the video
+    create_video(animation_file, audio_file)
+
+    # Return the path to the video
+    return 'output_video.mp4'
+
+# Streamlit UI
+st.title("Educational Video Generator")
+
+# User input for prompt
+prompt = st.text_input("Enter your prompt here:")
+
+if st.button("Generate Video"):
+    if prompt:
+        st.write("Generating video, please wait...")
+        
+        # Generate the video
+        video_path = generate_educational_video(prompt)
+        
+        # Display the video in Streamlit
+        st.video(video_path)
+    else:
+        st.warning("Please enter a prompt to generate the video.")