push changes for api

README.md

@@ -16,6 +16,14 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
 
 ## Run on docker
 
+### UI
+
+docker run -it -p 8501:8501 --platform=linux/amd64 \
+	-e LAUNCHDARKLY_SDK_KEY="sdk-142d656c-d430-4f8c-b2f1-7275f2ec65ff" \
+	registry.hf.space/asgface-sentimentai:latest streamlit run app.py
+
+### API
+
 docker run -it -p 8501:8501 --platform=linux/amd64 \
 	-e LAUNCHDARKLY_SDK_KEY="sdk-142d656c-d430-4f8c-b2f1-7275f2ec65ff" \
-	registry.hf.space/asgface-sentimentai:latest streamlit run app.py
+	registry.hf.space/asgface-sentimentai:latest python api.py

api.py

@@ -0,0 +1,67 @@
+from flask import Flask, request, jsonify
+import streamlit as st
+from transformers import pipeline
+import os
+from ldclient import LDClient, Config, Context
+
+app = Flask(__name__)
+
+# Retrieve the LaunchDarkly SDK key from environment variables
+ld_sdk_key = os.getenv("LAUNCHDARKLY_SDK_KEY")
+
+# Initialize LaunchDarkly client with the correct configuration
+ld_client = LDClient(Config(ld_sdk_key))
+
+# Function to get the AI model configuration from LaunchDarkly
+
+
+def get_model_config(user_name):
+    flag_key = "model-swap"  # Replace with your flag key
+
+    # Create a context using Context Builder—it can be anything, but for this use case, I’m just defaulting to myself.
+
+    context = Context.builder(
+        f"context-key-{user_name}").name(user_name).build()
+    flag_variation = ld_client.variation(flag_key, context, default={})
+
+    model_id = flag_variation.get("modelID", "distilbert-base-uncased")
+    return model_id
+
+# Function to translate sentiment labels to user-friendly terms
+
+
+def translate_label(label):
+    label_mapping = {
+        "LABEL_0": "🤬 Negative",
+        "LABEL_1": "😶 Neutral",
+        "LABEL_2": "😃 Positive",
+        "1 star": "🤬 Negative",
+        "2 stars": "🤬 Negative",
+        "3 stars": "😶 Neutral",
+        "4 stars": "😃 Positive",
+        "5 stars": "😃 Positive"
+    }
+    return label_mapping.get(label, "Unknown")
+
+
+@app.route('/analyze', methods=['POST'])
+def analyze_sentiment():
+    data = request.json
+    name = data.get('name', 'Anonymous')
+    user_input = data.get('text', '')
+
+    if not user_input:
+        return jsonify({"error": "No text provided for analysis"}), 400
+
+    model_id = get_model_config(name)
+    model = pipeline("sentiment-analysis", model=model_id)
+
+    results = model(user_input)
+    translated_results = [{"Sentiment": translate_label(
+        result['label']), "Confidence": result['score'], "User_input": user_input} for result in results]
+
+    return jsonify({"name": name, "results": translated_results})
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 from transformers import pipeline
 from ldclient import LDClient, Config, Context
 import os
-import torch
 
 # Retrieve the LaunchDarkly SDK key from environment variables
 ld_sdk_key = os.getenv("LAUNCHDARKLY_SDK_KEY")

requirements.txt

@@ -2,3 +2,4 @@ streamlit
 transformers
 launchdarkly-server-sdk
 torch
+Flask