Create app.py

app.py

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+import gradio as gr
+from gtts import gTTS
+from moviepy.editor import TextClip, AudioFileClip
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+import torch
+import tempfile
+
+# Initialize RAG model components
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True)
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+def generate_response(input_text):
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+    generated = model.generate(input_ids)
+    response = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
+    return response
+
+def text_to_speech(text):
+    tts = gTTS(text)
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_audio_file:
+        tts.save(temp_audio_file.name)
+        return temp_audio_file.name
+
+def text_to_video(text, audio_filename):
+    text_clip = TextClip(text, fontsize=50, color='white', bg_color='black', size=(640, 480))
+    text_clip = text_clip.set_duration(10)
+    audio_clip = AudioFileClip(audio_filename)
+    video_clip = text_clip.set_audio(audio_clip)
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_video_file:
+        video_clip.write_videofile(temp_video_file.name, codec='libx264')
+        return temp_video_file.name
+
+def process_text(input_text):
+    response = generate_response(input_text)
+    audio_file = text_to_speech(response)
+    video_file = text_to_video(response, audio_file)
+    return response, audio_file, video_file
+
+iface = gr.Interface(
+    fn=process_text,
+    inputs=gr.Textbox(label="Enter your text:"),
+    outputs=[gr.Textbox(label="RAG Model Response"), gr.Audio(label="Audio"), gr.Video(label="Video")],
+    live=True
+)
+
+iface.launch()