add files

app.py

@@ -5,24 +5,18 @@ st.set_page_config(
     page_icon="👋",
 )
 
-st.write("# Welcome to Streamlit! 👋")
+st.write("# Welcome to Khaled Space! 👋")
 
-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)
-"""
+    Welcome to **Khaled's AI Learning Hub**! 🚀
+
+    This platform is dedicated to showcasing AI development projects, all designed to help you explore and understand the power of artificial intelligence. 🤖💡
+
+    **👈 Select a project from the sidebar** to see hands-on examples ranging from data processing to model deployment. Each project page will guide you through different aspects of AI development, helping you gain practical insights.
+
+ 
+    """
 )

helper/image_helper.py

@@ -0,0 +1,6 @@
+import base64
+
+def to_base64(uploaded_file):
+    file_buffer = uploaded_file.read()
+    b64 = base64.b64encode(file_buffer).decode()
+    return f"data:image/png;base64,{b64}"

pages/blip-image-captioning.py

@@ -1,22 +1,39 @@
-# import gradio as gr
 import streamlit as st
 from io import StringIO
+from PIL import Image
+import numpy as np
+
 
 from transformers import pipeline
 
+from helper.image_helper import to_base64
+
 pipe = pipeline("image-to-text",
                 model="Salesforce/blip-image-captioning-base")
 
 def process_file():
-    st.write(launch(uploaded_file))
-    
+    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
+    txt = launch(stringio) 
+    st.write(txt)
+
 def launch(input):
     out = pipe(input)
     return out[0]['generated_text']
 
-uploaded_file = st.file_uploader("Choose a file", on_change=process_file)
 
+# uploaded_file = st.file_uploader("Choose a file", on_change=process_file)
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    # st.image(uploaded_file)
+    image = Image.open(uploaded_file) 
+ 
+    # bytes_data = uploaded_file.getvalue()
+    base64 = to_base64(uploaded_file)
+    st.image(base64)
 
+    txt = launch(base64) 
+    st.write(txt)
 
 # iface = gr.Interface(launch,
 #                      inputs=gr.Image(type='pil'),

pages/noon.py

@@ -1,25 +1,28 @@
-# from transformers import BloomTokenizerFast, BloomForCausalLM, pipeline
+import streamlit as st
 
+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')
+text="اكتب مقالا من عدة أسطر عن الذكاء الصناعي وتطوراته"
+prompt = f'Instruction:\n{text}\n\nResponse:'
 
-# tokenizer = BloomTokenizerFast.from_pretrained('Naseej/noon-7b')
+model = BloomForCausalLM.from_pretrained('Naseej/noon-7b')
 
-# generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+tokenizer = BloomTokenizerFast.from_pretrained('Naseej/noon-7b')
 
-# # 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'] 
+generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# print(response)
+# 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)
+st.write(response)