easyread.py

app.py

@@ -0,0 +1,54 @@
+#!pip install -qqq datasets==3.5.0
+
+import gradio as gr
+import torch
+from transformers import pipeline
+from datasets import load_dataset
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device
+
+#from google.colab import userdata
+#userdata.get('llama_easyread')
+
+# map the image (description text) -> block of text
+
+# <block>paragraph 1</block>
+# <block>paragraph 2</block>
+
+
+# description text of image -> (pass to embedding model) -> get vector embedding | => Compute cosine similarity -> we get similarity score 0-1 (1 means the same 0 means not the same)
+# paragraph 1 -> (pass to embedding model) -> get vector embedding               |
+
+model_id = "meta-llama/Llama-3.2-1B-Instruct"
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    device=device,
+    torch_dtype=torch.bfloat16 if "cuda" in device else torch.float32,
+)
+
+messages = [
+    {"role":"system", "content": "You're a helpful EasyRead Assistant the simplifies complex documents or content. Follow the easy read guidelines. Only provide the simiplied content, for complex terms in the simplified text, always add a footnote for definitions."}
+]
+
+def add_and_generate(history, text):
+    messages.append({"role":"user","content": text})
+    prompt = pipe.tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    # print(prompt)
+    out = pipe(prompt, max_new_tokens=150, do_sample=True, temperature=0.7, top_p=0.9)
+    reply = out[0]["generated_text"][len(prompt):]
+    messages.append({"role":"assistant","content":reply})
+    history.append((text, reply))
+
+    return history, ""
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    txt = gr.Textbox(placeholder="Type here...")
+    txt.submit(add_and_generate, [chatbot, txt], [chatbot, txt])
+
+demo.launch(debug=True)