Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the CogVideoX model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "THUDM/CogVideoX-5b"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Streamlit interface
+st.title("Text to Video Generator using CogVideoX-5b")
+
+# Input text prompt from user
+prompt = st.text_input("Enter a text prompt for video generation:", "")
+
+# Button to generate the video
+if st.button("Generate Video"):
+    if prompt:
+        with st.spinner("Generating video..."):
+            inputs = tokenizer(prompt, return_tensors="pt")
+            output = model.generate(**inputs)
+            
+            # Assuming video output is a tensor; simulate video path
+            video_path = "generated_video.mp4"
+            with open(video_path, "wb") as f:
+                f.write(output[0].cpu().numpy())  # Example write operation (modify this as per the actual model's output)
+
+            st.video(video_path)
+    else:
+        st.warning("Please enter a prompt before generating the video.")
+
+# Footer
+st.write("Powered by THUDM/CogVideoX-5b and Streamlit")