Update app.py

app.py

@@ -1,19 +1,34 @@
+import os
 import gradio as gr
-from openai import OpenAI
 import pandas as pd
 import ast
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
-
+from openai import OpenAI
 
 # Get value OPEN_API_KEY
-OPENAI_API_KEY = "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"
-client = OpenAI(api_key=OPENAI_API_KEY)
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+login = os.environ.get("login")
+password = os.environ.get("password")
 
 # Load embedding dataset
 data = pd.read_csv("embeddings.csv")
 data["embedding"] = data["embedding"].apply(ast.literal_eval)
 
+# Initialize Gradio Interface
+iface = gr.Interface()
+
+# Dropdown for selecting the model
+model_dropdown = gr.DropDown(models=["gpt-3.5-turbo", "gpt-4-1106-preview"], label="Select Model")
+iface.add(model_dropdown, "model")
+
+# Slider for temperature
+temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, default=0.5, label="Temperature")
+iface.add(temperature_slider, "temperature")
+
+# Slider for top_p
+top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, default=0.5, label="Top P")
+iface.add(top_p_slider, "top_p")
 
 def search_reviews(df_original, product_description, without_newlines=False, n=1):
     df = df_original.copy()
@@ -35,12 +50,13 @@ def search_reviews(df_original, product_description, without_newlines=False, n=1
     res = df.sort_values("similarities", ascending=False).head(n)
     return res.reset_index(drop=True)
 
-
-def generate_response(text):
+def generate_response(text, model, temperature, top_p):
     reference = search_reviews(data, text, without_newlines=False)["Content"][0]
 
     completion = client.chat.completions.create(
-        model="gpt-3.5-turbo",
+        model=model,
+        temperature=temperature,
+        top_p=top_p,
         messages=[
             {
                 "role": "system",
@@ -52,6 +68,10 @@ def generate_response(text):
     result = completion.choices[0].message.content
     return result
 
+# Set the function for Gradio Interface
+iface.fn = generate_response
+iface.inputs = ["text", "model", "temperature", "top_p"]
+iface.outputs = "text"
 
-iface = gr.Interface(fn=generate_response, inputs="text", outputs="text")
-iface.launch(auth=("chris", "pass1234chris"))
+# Launch the Gradio Interface
+iface.launch(auth=(login, password))