Update app.py

app.py

@@ -1,64 +1,85 @@
-import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-import torch
-import faiss
-import chromadb
+from fastapi import FastAPI, HTTPException
+import requests
+import oci
+import json
+from datetime import datetime
 
-# Load the SentenceTransformer model for vector embeddings
-model = SentenceTransformer('all-MiniLM-L6-v2')
+app = FastAPI()
 
-# FAQ dataset (this can be expanded)
-faq_data = [
-    ("What is Hugging Face?", "Hugging Face is a company specializing in AI and machine learning, known for their open-source models and datasets."),
-    ("What is AI?", "Artificial Intelligence (AI) is the simulation of human intelligence in machines.")
-    # Add more FAQ pairs...
-]
+# Oracle Cloud Configurations (Ensure your OCI config file is set up correctly)
+config = oci.config.from_file("~/.oci/config", "DEFAULT")
+signer = oci.auth.signers.get_resource_principals_signer()
 
-corpus = [item[0] for item in faq_data]  # Questions only
-answers = {item[0]: item[1] for item in faq_data}  # Map questions to answers
-corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
+# Endpoint URL for Oracle AI Service (Replace region appropriately)
+oci_endpoint = "https://anomalydetection.aiservice.{{region}}.oci.oraclecloud.com/20210101/aiPrivateEndpoints"
 
-# Initialize FAISS Index
-index = faiss.IndexFlatL2(corpus_embeddings.shape[1])
-index.add(corpus_embeddings.cpu().numpy())
+# Request headers including authentication (ensure the correct signing/authentication process)
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": "Bearer {access_token}"  # Use the appropriate method to authenticate
+}
 
-# Initialize Chroma vector store
-client = chromadb.Client()
-collection = client.create_collection(name="faq_data")
-for i, text in enumerate(corpus):
-    collection.add(
-        ids=[f"faq_{i}"],  # Unique ID for each document (using the index i)
-        documents=[text],
-        metadatas=[{"source": f"faq_{i}"}],
-        embeddings=[corpus_embeddings[i].cpu().numpy()],
+@app.post("/create-private-endpoint")
+async def create_private_endpoint(compartment_id: str, subnet_id: str, display_name: str, dns_zones: list):
+    """Creates a private reverse connection endpoint in Oracle Cloud"""
+    
+    # Prepare the request body
+    request_body = {
+        "compartmentId": compartment_id,
+        "subnetId": subnet_id,
+        "displayName": display_name,
+        "dnsZones": dns_zones,
+        "definedTags": {},
+        "freeformTags": {}
+    }
+    
+    response = requests.post(
+        oci_endpoint,
+        headers=headers,
+        data=json.dumps(request_body)
     )
-
-# Retrieval function using FAISS and Chroma
-def retrieve(query):
-    query_embedding = model.encode(query, convert_to_tensor=True).cpu().numpy()
-
-    # Use FAISS for nearest neighbor search
-    faiss_results = index.search(query_embedding, k=1)
-    faiss_top_result_idx = faiss_results[1][0][0]
-    faiss_top_score = faiss_results[0][0][0]
-
-    # Use Chroma for semantic search
-    chroma_results = collection.query(query_embeddings=[query_embedding], n_results=1)
-    chroma_top_result = chroma_results['documents'][0]
-
-    # Combining results from FAISS and Chroma
-    if faiss_top_score > 0.5:
-        return answers[corpus[faiss_top_result_idx]]
+    
+    if response.status_code == 202:
+        return {"message": "Private endpoint created successfully", "status": response.status_code}
     else:
-        return chroma_top_result or "Sorry, I didn’t understand that. Could you try asking something else?"
+        raise HTTPException(status_code=response.status_code, detail=response.json())
 
-# Gradio interface to interact with the bot
-iface = gr.Interface(fn=retrieve, 
-                     inputs="text", 
-                     outputs="text", 
-                     live=True, 
-                     title="RAG AI Bot with OCI AI Skills", 
-                     description="Ask me anything related to Hugging Face, Oracle OCI AI, or general knowledge!")
+@app.put("/update-private-endpoint/{endpoint_id}")
+async def update_private_endpoint(endpoint_id: str, display_name: str, dns_zones: list):
+    """Updates a private reverse connection endpoint"""
+    
+    update_url = f"{oci_endpoint}/{endpoint_id}"
+    
+    request_body = {
+        "displayName": display_name,
+        "dnsZones": dns_zones,
+        "definedTags": {},
+        "freeformTags": {}
+    }
+    
+    response = requests.put(
+        update_url,
+        headers=headers,
+        data=json.dumps(request_body)
+    )
+    
+    if response.status_code == 202:
+        return {"message": "Private endpoint updated successfully", "status": response.status_code}
+    else:
+        raise HTTPException(status_code=response.status_code, detail=response.json())
 
-# Launch the Gradio interface
-iface.launch()
+@app.get("/get-private-endpoint/{endpoint_id}")
+async def get_private_endpoint(endpoint_id: str):
+    """Retrieves a private reverse connection endpoint by its ID"""
+    
+    get_url = f"{oci_endpoint}/{endpoint_id}"
+    
+    response = requests.get(
+        get_url,
+        headers=headers
+    )
+    
+    if response.status_code == 200:
+        return response.json()
+    else:
+        raise HTTPException(status_code=response.status_code, detail=response.json())