Update app.py

app.py

@@ -1,15 +1,50 @@
-from fastapi import FastAPI
-from app.routes.vector_routes import router as vector_router
-from app.utils.database import connect_to_mongo, close_mongo_connection
-
-app = FastAPI()
-
-app.include_router(vector_router)
-
-@app.on_event("startup")
-async def startup_db_client():
-    await connect_to_mongo()
-
-@app.on_event("shutdown")
-async def shutdown_db_client():
-    await close_mongo_connection()
+import streamlit as st
+from pymongo import MongoClient
+import os
+from pydantic import BaseModel
+from typing import List
+
+# Load environment variables
+MONGO_URI = os.getenv("MONGO_URI")
+MONGO_DB_NAME = os.getenv("MONGO_DB_NAME")
+
+# Connect to MongoDB
+client = MongoClient(MONGO_URI)
+db = client[MONGO_DB_NAME]
+vectors_collection = db["vectors"]
+
+# Define a Pydantic model for vectors
+class Vector(BaseModel):
+    vector: List[float]
+    metadata: dict
+
+# Streamlit app
+st.title("MongoDB Vector Search with Streamlit")
+
+# Form to insert a vector
+with st.form("vector_form"):
+    st.write("Insert a new vector")
+    vector_input = st.text_input("Enter vector (comma-separated floats):")
+    metadata_input = st.text_input("Enter metadata (JSON format):")
+    submitted = st.form_submit_button("Submit")
+
+    if submitted:
+        try:
+            # Parse the vector and metadata
+            vector = [float(x) for x in vector_input.split(",")]
+            metadata = eval(metadata_input)  # Convert JSON string to dict
+
+            # Create a Vector object
+            new_vector = Vector(vector=vector, metadata=metadata)
+
+            # Insert into MongoDB
+            result = vectors_collection.insert_one(new_vector.dict())
+            st.success(f"Vector inserted with ID: {result.inserted_id}")
+        except Exception as e:
+            st.error(f"Error: {e}")
+
+# Display all vectors
+st.write("## Stored Vectors")
+vectors = vectors_collection.find()
+for vector in vectors:
+    st.json(vector)