Update app.py

app.py

@@ -4,32 +4,27 @@ import pandas as pd
 import ast
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
-from openai import OpenAI
+from openai import OpenAI, ChatCompletion
 
-# Get value OPEN_API_KEY
+# Получаем значения OPEN_API_KEY, login, и password
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 login = os.environ.get("login")
 password = os.environ.get("password")
 
-# Load embedding dataset
+# Инициализируем OpenAI API клиент
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# Загружаем данные embeddings
 data = pd.read_csv("embeddings.csv")
 data["embedding"] = data["embedding"].apply(ast.literal_eval)
 
-# Initialize Gradio Interface
-iface = gr.Interface(fn=None, inputs=None, outputs=None)
-
-# 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, maximum=1.0, default=0, 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")
+# Элементы Gradio интерфейса
+model_dropdown = gr.Dropdown(labels=["gpt-3.5-turbo", "gpt-4-1106-preview"], label="Select Model")
+temperature_slider = gr.Slider(minimum=0, maximum=1.0, value=0, label="Temperature")
+top_p_slider = gr.Slider(minimum=0.01, maximum=1.0, value=1, label="Top P")
+textbox_input = gr.Textbox(label="Enter text here")
 
+# Функция поиска отзывов
 def search_reviews(df_original, product_description, without_newlines=False, n=1):
     df = df_original.copy()
     if without_newlines:
@@ -45,33 +40,31 @@ def search_reviews(df_original, product_description, without_newlines=False, n=1
     df["similarities"] = df["embedding"].apply(
         lambda x: cosine_similarity(
             np.array(x).reshape(1, -1), np.array(embedding).reshape(1, -1)
-        )
+        )[0][0]
     )
     res = df.sort_values("similarities", ascending=False).head(n)
     return res.reset_index(drop=True)
 
+# Функция генерации ответа
 def generate_response(text, model, temperature, top_p):
-    reference = search_reviews(data, text, without_newlines=False)["Content"][0]
+    reference = search_reviews(data, text, without_newlines=True)["Content"][0]
 
-    completion = client.chat.completions.create(
-        model=model,
+    completion = client.Completion.create(
+        engine=model,
+        prompt=f"Rewrite the following text while ensuring logical composition and avoiding over-exaggeration or imaginative elements: {reference}\n\n{text}",
         temperature=temperature,
-        top_p=top_p,
-        messages=[
-            {
-                "role": "system",
-                "content": f"Generate high-quality rewritten articles, ensuring logical composition, avoiding over-exaggeration, and refraining from any imaginative elements. Use the provided sample text as a reference for the desired writing style:{reference}",
-            },
-            {"role": "user", "content": f"{text}"},
-        ],
+        max_tokens=150,  # Вы можете изменить это значение
+        top_p=top_p
     )
-    result = completion.choices[0].message.content
+    result = completion.choices[0].text.strip()
     return result
 
-# Set the function for Gradio Interface
-iface.fn = generate_response
-iface.inputs = ["text", "model", "temperature", "top_p"]
-iface.outputs = "text"
+# Создаём интерфейс Gradio
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[textbox_input, model_dropdown, temperature_slider, top_p_slider],
+    outputs=gr.Textbox()
+)
 
-# Launch the Gradio Interface
+# Запускаем интерфейс Gradio
 iface.launch(auth=(login, password))