init

CMD.md

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+streamlit run main_page.py

main_page.py

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+import streamlit as st
+
+st.set_page_config(
+    page_title="Hello",
+    page_icon="👋",
+)
+
+st.write("# Welcome to Streamlit! 👋")
+
+st.sidebar.success("Select a demo above.")
+
+st.markdown(
+    """
+    Streamlit is an open-source app framework built specifically for
+    Machine Learning and Data Science projects.
+    **👈 Select a demo from the sidebar** to see some examples
+    of what Streamlit can do!
+    ### Want to learn more?
+    - Check out [streamlit.io](https://streamlit.io)
+    - Jump into our [documentation](https://docs.streamlit.io)
+    - Ask a question in our [community
+        forums](https://discuss.streamlit.io)
+    ### See more complex demos
+    - Use a neural net to [analyze the Udacity Self-driving Car Image
+        Dataset](https://github.com/streamlit/demo-self-driving)
+    - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)
+"""
+)

pages/blip-image-captioning.py

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+import gradio as gr
+from transformers import pipeline
+
+pipe = pipeline("image-to-text",
+                model="Salesforce/blip-image-captioning-base")
+
+
+def launch(input):
+    out = pipe(input)
+    return out[0]['generated_text']
+
+iface = gr.Interface(launch,
+                     inputs=gr.Image(type='pil'),
+                     outputs="text")
+
+iface.launch()

pages/noon.py

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+# from transformers import BloomTokenizerFast, BloomForCausalLM, pipeline
+
+
+# text="اكتب مقالا من عدة أسطر عن الذكاء الصناعي وتطوراته"
+# prompt = f'Instruction:\n{text}\n\nResponse:'
+
+# model = BloomForCausalLM.from_pretrained('Naseej/noon-7b')
+
+# tokenizer = BloomTokenizerFast.from_pretrained('Naseej/noon-7b')
+
+# generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# # We recommend the provided hyperparameters for generation
+# # But encourage you to try different values
+# response = generation_pipeline(prompt,
+#     pad_token_id=tokenizer.eos_token_id,
+#     do_sample=False,
+#     num_beams=4,
+#     max_length=500,
+#     top_p=0.1,
+#     top_k=20,
+#     repetition_penalty = 3.0,
+#     no_repeat_ngram_size=3)[0]['generated_text'] 
+
+# print(response)

requirements.txt

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+transformers
+torch
+gradio