Create app.py

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+#from streamlit_datalist import stDatalist
+from utils import convert_to_base64, convert_to_html
+import requests
+
+IP = '127.0.0.1'
+PORT= 8080
+url = f'http://{IP}:{PORT}/predictions/model'
+headers = {'Content-Type': 'application/json'}
+st.set_page_config(page_title="AWS Inferentia2 Demo", layout="wide")
+#st.set_page_config(layout="wide")
+
+st.title("Multimodal Model on AWS Inf2")
+st.subheader("LLaVA-1.6-Mistral-7B")
+
+
+def upload_image():
+    image_list=["./images/view.jpg",
+            "./images/cat.jpg",
+            "./images/olympic.jpg",
+            "./images/usa.jpg",
+            "./images/box.jpg"]
+    name_list=["view(https://llava-vl.github.io/static/images/view.jpg)",
+            "cat",
+            "paris 2024",
+            "statue of liberty",
+            "box(from my camera)"]
+    images_all = dict(zip(name_list, image_list))
+
+    user_option = st.selectbox("Select a preset image", ["–Select–"] + name_list)
+    print(user_option)
+    if user_option!="–Select–":
+        image_names=[images_all[user_option]]
+    else:
+        image_names=[]
+
+    st.text("OR")
+
+    images = st.file_uploader("Upload an image to chat about", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
+    #print(images)
+    # assert max number of images, e.g. 1
+    assert len(images) <= 1, (st.error("Please upload at most 1 image"), st.stop())
+
+        if images or image_names:
+        if images:
+            image_names=[]
+        # convert images to base64
+        images_b64 = []
+        for image in images+image_names:
+            image_b64 = convert_to_base64(image)
+            images_b64.append(image_b64)
+
+        # display images in multiple columns
+        cols = st.columns(len(images_b64)) ##only process first image
+        for i, col in enumerate(cols):
+            col.markdown(f"**Image {i+1}**")
+            col.markdown(convert_to_html(images_b64[i]), unsafe_allow_html=True)
+            break #only process first image
+        st.markdown("---")
+        return images_b64[0]    #only process first image
+    st.stop()
+
+
+@st.cache_data(show_spinner=False)
+def ask_llm(prompt, byte_image):
+    payload = {
+        "prompt":prompt,
+        "image": byte_image,
+        "parameters": {
+            "top_k": 100,
+            "top_p": 0.1,
+            "temperature": 0.2,
+        }
+    }
+    response = requests.post(url, json=payload, headers=headers)
+
+    return response.text
+
+def app():
+    st.markdown("---")
+    c1, c2 = st.columns(2)
+    with c2:
+        image_b64 = upload_image()
+    with c1:
+        question = st.chat_input("Ask a question about this image")
+
+    if not question: st.stop()
+    with c1:
+        with st.chat_message("question"):
+            st.markdown(question, unsafe_allow_html=True)
+        with st.spinner("Thinking..."):
+            res = ask_llm(question, image_b64)
+            with st.chat_message("response"):
+                st.write(res)
+
+if __name__ == "__main__":
+    app()