Update app.py

app.py

@@ -1,91 +1,45 @@
 import streamlit as st
-from connection import PineconeConnection
-from sentence_transformers import SentenceTransformer
+import matplotlib.pyplot as plt
+import numpy as np
 
+def plot_polygon(edges, vertices):
+    if len(edges) != len(vertices):
+        st.warning("Number of edges and vertices should be the same.")
+        return
 
-@st.cache_resource
-def init_retriever():
-    return SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+    fig, ax = plt.subplots()
+    ax.set_aspect('equal', adjustable='box')
+    ax.set_title('Polygon Plot')
+    ax.grid(True)
 
+    edges = np.array(edges)
+    vertices = np.array(vertices)
 
-def card(thumbnail, title, url, context):
-    return st.markdown(
-        f"""
-    <div class="container-fluid">
-        <div class="row align-items-start">
-            <div class="col-md-4 col-sm-4">
-                 <div class="position-relative">
-                     <a href={url}><img src={thumbnail} class="img-fluid" style="width: 192px; height: 106px"></a>
-                 </div>
-             </div>
-             <div  class="col-md-8 col-sm-8">
-                 <a href={url}>{title}</a>
-                 <br>
-                 <span style="color: #808080;">
-                     <small>{context[:200].capitalize()+"...."}</small>
-                 </span>
-             </div>
-        </div>
-     </div>
-        """,
-        unsafe_allow_html=True,
-    )
+    for i in range(len(edges)):
+        ax.plot([vertices[i][0], vertices[(i + 1) % len(edges)][0]],
+                [vertices[i][1], vertices[(i + 1) % len(edges)][1]], 'b-')
 
+    ax.scatter(vertices[:, 0], vertices[:, 1], color='red')
 
-st.markdown(
-    """
-# YouTube Q&A
+    plt.xlim(np.min(vertices[:, 0]) - 1, np.max(vertices[:, 0]) + 1)
+    plt.ylim(np.min(vertices[:, 1]) - 1, np.max(vertices[:, 1]) + 1)
 
-The app matches the natural language question to the video transcripts and finds you similar videos. It will give you links to YouTube Videos that match your search question.
+    st.pyplot(fig)
 
-The app will query a vector database (Pinecone) and perform Semantic Search. 
+def main():
+    st.title("Polygon Plotter")
+    st.write("Enter the number of edges and vertices for the polygon, then input the coordinates.")
 
-The dataset was taken from HuggingFace: [Youtube Video Transcriptions](https://huggingface.co/datasets/pinecone/yt-transcriptions).
+    num_edges = st.number_input("Number of Edges:", min_value=3, step=1)
+    vertices = []
 
-This app is an official submission to the Streamlit Connections Hackathon.
-- [Hackathon Link](https://discuss.streamlit.io/t/connections-hackathon/47574)
-- [GitHub Repo](https://github.com/awinml/st-pinecone-connection)
-- [Pinecone Vector Database](https://www.pinecone.io/)
+    for i in range(num_edges):
+        x = st.number_input(f"Vertex {i + 1} - x:", key=f"x{i}")
+        y = st.number_input(f"Vertex {i + 1} - y:", key=f"y{i}")
+        vertices.append((x, y))
 
-Some URLs and Images may not show up when searching for certain keywords, due to missing values in the original dataset. It does affect the performance of the vector database or the app.
+    if st.button("Plot Polygon"):
+        plot_polygon(range(num_edges), vertices)
 
-Some topics that you can search for: 
-- Reinforcement Learning
-- GANs
-- Tensorflow
-"""
-)
-
-st.markdown(
-    """
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.0.0/dist/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
-""",
-    unsafe_allow_html=True,
-)
-
-# Connect app to Pinecone Client
-conn = st.experimental_connection(
-    "PineconeVectorDB",
-    type=PineconeConnection,
-    environment="us-west1-gcp-free",
-    api_key="1ec08295-4f82-472a-b324-f2f21663acf3",
-)
-cursor = conn.cursor()
-
-retriever = init_retriever()
-
-query_str = st.text_input("Please enter Search Query:", "")
-
-if query_str != "":
-    xq = retriever.encode([query_str]).tolist()
-    xc = conn.query(
-        index_name="youtube-search", query_vector=xq, top_k=5, include_metadata=True
-    )
-
-    for context in xc:
-        card(
-            context["metadata"]["thumbnail"],
-            context["metadata"]["title"],
-            context["metadata"]["url"],
-            context["metadata"]["text"],
-        )
+if __name__ == "__main__":
+    main()