Create app.py
Browse files
app.py
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import streamlit as st
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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from diffusers import StableDiffusionPipeline, AutoencoderKL
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from torchvision import models
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tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi")
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model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi")
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pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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video_model = models.resnet50(pretrained=True)
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st.title("FallnAI Inference App")
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st.subheader("Coding Model")
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user_input = st.text_input("Enter your code:")
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if st.button("Generate"):
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result = pipeline("text-generation", model=model, tokenizer=tokenizer)(user_input)
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st.write(result[0]['generated_text'])
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st.subheader("Stable Diffusion Model")
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prompt = st.text_input("Enter your prompt:")
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if st.button("Generate"):
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image = pipe(prompt).images[0]
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st.image(image)
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st.subheader("Video Model")
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video_file = st.file_uploader("Upload a video file:", type=["mp4", "avi"])
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if video_file is not None:
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video_bytes = video_file.getvalue()
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st.video(video_bytes)
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video_transformed = video_model(video_bytes)
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