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app.py
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@@ -5,16 +5,15 @@ import openai
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import csv
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from sentence_transformers import SentenceTransformer, util
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# OpenAIキー
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# ZIPを展開
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if not os.path.exists("articles"):
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with zipfile.ZipFile("articles.zip", "r") as zip_ref:
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zip_ref.extractall("articles")
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with open("articles/articles.csv", "r", encoding="cp932") as f:
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reader = csv.DictReader(f)
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for row in reader:
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@@ -22,11 +21,9 @@ with open("articles/articles.csv", "r", encoding="cp932") as f:
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documents.append(content)
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file_map[content] = {"title": row["title"], "url": row["url"]}
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# 埋め込みモデル
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model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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document_embeddings = model.encode(documents, convert_to_tensor=True)
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# 回答生成
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def generate_answer(query):
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query_embedding = model.encode(query, convert_to_tensor=True)
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hits = util.semantic_search(query_embedding, document_embeddings, top_k=2)[0]
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@@ -39,8 +36,7 @@ def generate_answer(query):
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context = "\n\n".join(documents[hit["corpus_id"]][:300] for hit in hits)
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prompt = (
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f"以下の内容をふまえて、転職面接に関する質問に自然で丁寧な日本語で答えてください。"
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f"できるだけ共感から始めて、最後は応援の言葉で締めくくってください。\n"
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f"質問: {query}\n"
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f"---\n{context}\n---"
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)
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@@ -52,12 +48,11 @@ def generate_answer(query):
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temperature=0.6,
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)
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answer = response.choices[0].message.content.strip()
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return f"{answer}
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except Exception as e:
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return f"エラーが発生しました: {str(e)}"
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# Gradio UI(Blocks形式)
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with gr.Blocks(title="面接で不安なことを入力して下さい") as demo:
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gr.Markdown("## 面接で不安なことを入力して下さい")
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@@ -68,12 +63,12 @@ with gr.Blocks(title="面接で不安なことを入力して下さい") as demo
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placeholder="例:面接で緊張してしまいます。どうすればよいですか?",
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lines=2,
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)
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submit_btn = gr.Button("転職面接ナビに回答してもらう",
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clear_btn = gr.Button("リセット")
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with gr.Column():
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answer = gr.
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submit_btn.click(fn=generate_answer, inputs=question, outputs=answer)
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clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[question, answer])
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demo.launch()
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import csv
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from sentence_transformers import SentenceTransformer, util
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openai.api_key = os.getenv("OPENAI_API_KEY")
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if not os.path.exists("articles"):
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with zipfile.ZipFile("articles.zip", "r") as zip_ref:
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zip_ref.extractall("articles")
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documents = []
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file_map = {}
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with open("articles/articles.csv", "r", encoding="cp932") as f:
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reader = csv.DictReader(f)
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for row in reader:
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documents.append(content)
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file_map[content] = {"title": row["title"], "url": row["url"]}
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model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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document_embeddings = model.encode(documents, convert_to_tensor=True)
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def generate_answer(query):
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query_embedding = model.encode(query, convert_to_tensor=True)
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hits = util.semantic_search(query_embedding, document_embeddings, top_k=2)[0]
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context = "\n\n".join(documents[hit["corpus_id"]][:300] for hit in hits)
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prompt = (
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f"以下の内容をふまえて、転職面接に関する質問に自然で丁寧な日本語で答えてください。\n"
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f"質問: {query}\n"
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f"---\n{context}\n---"
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)
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temperature=0.6,
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)
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answer = response.choices[0].message.content.strip()
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reference = "\n".join(sources)
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return f"{answer}\n\nもっと詳しく知りたい方は、以下もご覧ください:\n{reference}"
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except Exception as e:
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return f"エラーが発生しました: {str(e)}"
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with gr.Blocks(title="面接で不安なことを入力して下さい") as demo:
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gr.Markdown("## 面接で不安なことを入力して下さい")
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placeholder="例:面接で緊張してしまいます。どうすればよいですか?",
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lines=2,
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)
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submit_btn = gr.Button("転職面接ナビに回答してもらう", elem_classes="submit-btn")
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clear_btn = gr.Button("リセット")
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with gr.Column():
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answer = gr.Textbox(label="回答", lines=10)
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submit_btn.click(fn=generate_answer, inputs=question, outputs=answer)
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clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[question, answer])
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demo.launch()
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