Add application file

gradio_app.py

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+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
+
+
+# Get value OPEN_API_KEY
+OPENAI_API_KEY = "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"
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# Load embedding dataset
+data = pd.read_csv("embeddings.csv")
+data["embedding"] = data["embedding"].apply(ast.literal_eval)
+
+
+def search_reviews(df_original, product_description, without_newlines=False, n=1):
+    df = df_original.copy()
+    if without_newlines:
+        product_description = product_description.replace("\n", " ")
+
+    embedding = (
+        client.embeddings.create(
+            input=[product_description], model="text-embedding-ada-002"
+        )
+        .data[0]
+        .embedding
+    )
+    df["similarities"] = df["embedding"].apply(
+        lambda x: cosine_similarity(
+            np.array(x).reshape(1, -1), np.array(embedding).reshape(1, -1)
+        )
+    )
+    res = df.sort_values("similarities", ascending=False).head(n)
+    return res.reset_index(drop=True)
+
+
+def generate_response(text):
+    reference = search_reviews(data, text, without_newlines=False)["Content"][0]
+
+    completion = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        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}"},
+        ],
+    )
+    result = completion.choices[0].message.content
+    return result
+
+
+iface = gr.Interface(fn=generate_response, inputs="text", outputs="text")
+iface.launch(share=True)

requirements.txt

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+annotated-types==0.6.0
+anyio==3.7.1
+appnope==0.1.3
+asttokens==2.4.1
+certifi==2023.11.17
+comm==0.2.0
+contourpy==1.2.0
+cycler==0.12.1
+debugpy==1.8.0
+decorator==5.1.1
+distro==1.8.0
+dotenv==0.0.5
+exceptiongroup==1.2.0
+executing==2.0.1
+fonttools==4.46.0
+h11==0.14.0
+httpcore==1.0.2
+httpx==0.25.2
+idna==3.6
+ipykernel==6.27.1
+ipython==8.18.1
+jedi==0.19.1
+joblib==1.3.2
+jupyter_client==8.6.0
+jupyter_core==5.5.0
+kiwisolver==1.4.5
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+nest-asyncio==1.5.8
+numpy==1.26.2
+openai==1.3.7
+packaging==23.2
+pandas==2.1.3
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+platformdirs==4.1.0
+prompt-toolkit==3.0.41
+psutil==5.9.6
+psycopg2-binary==2.9.9
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pydantic==2.5.2
+pydantic_core==2.14.5
+Pygments==2.17.2
+pyparsing==3.1.1
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+pytz==2023.3.post1
+pyzmq==25.1.2
+scikit-learn==1.3.2
+scipy==1.11.4
+six==1.16.0
+sniffio==1.3.0
+stack-data==0.6.3
+threadpoolctl==3.2.0
+tornado==6.4
+tqdm==4.66.1
+traitlets==5.14.0
+typing_extensions==4.8.0
+tzdata==2023.3
+wcwidth==0.2.12