update ai agent

.gitignore

@@ -0,0 +1 @@
+.env

ai_agent.py

@@ -0,0 +1,137 @@
+from dotenv import load_dotenv, find_dotenv
+import os
+import bs4
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.prompts import ChatPromptTemplate
+
+from langdetect import detect
+from deep_translator import GoogleTranslator
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.agents import initialize_agent, AgentType
+from langchain_openai import ChatOpenAI
+
+from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper, DuckDuckGoSearchAPIWrapper
+from langchain_community.tools import WikipediaQueryRun, ArxivQueryRun, DuckDuckGoSearchRun
+
+
+load_dotenv(find_dotenv())
+
+wiki = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200))
+
+arxiv = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200))
+
+duckduckgo_search = DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper(region="in-en", time="y", max_results=2))
+
+tools = [wiki, arxiv, duckduckgo_search]
+
+def translate_to_english(text):
+    try:
+        detected_lang = detect(text)
+        if detected_lang == "en":
+            return text, "en"
+        translated_text = GoogleTranslator(source=detected_lang, target="en").translate(text)
+        return translated_text, detected_lang
+    except Exception:
+        return text, "unknown"
+
+def translate_back(text, target_lang):
+    try:
+        if target_lang == "en":
+            return text
+        return GoogleTranslator(source="en", target=target_lang).translate(text)
+    except Exception:
+        return text
+
+def load_llm():
+    load_dotenv()
+    return ChatGoogleGenerativeAI(
+        model="gemini-1.5-flash",
+        temperature=0.7,
+        google_api_key=os.environ.get("GEMINI_API_KEY")   # ✅ Truyền trực tiếp tại đây
+    )
+
+# def load_llm():
+#    return ChatOpenAI(
+#        model_name="llama3-70b-8192",
+#        temperature=1,
+#        openai_api_key=os.getenv("GROQ_API_KEY"),
+#        openai_api_base="https://api.groq.com/openai/v1"
+#    )
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+
+
+def get_conversational_agent():
+
+   llm = load_llm()
+
+   return initialize_agent(
+       tools=tools,
+       llm=llm,
+       agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+       verbose=False,
+       return_intermediate_steps=False,
+       max_iterations=5,
+       handle_parsing_errors=True
+
+   )
+
+def ask_gemini_to_evaluate(question, answer):
+    try:
+        agent = get_conversational_agent()
+
+        prompt = f"""
+            Câu hỏi: {question}
+            Câu trả lời từ AI: {answer}
+            Hãy trả lời duy nhất một từ: "Hợp lý" nếu câu trả lời tốt, hoặc "Không hợp lý" nếu câu trả lời sai hoặc thiếu thông tin.
+        """
+
+        response = agent.invoke(prompt)
+
+        if response["output"].strip() == "Hợp lý":
+            return True
+        return False
+    except:
+        return False
+class AIAgent:
+    def __init__(self):
+        self.loader = WebBaseLoader(
+            web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+            bs_kwargs=dict(
+                parse_only=bs4.SoupStrainer(
+                    class_=("post-content", "post-title", "post-header")
+                )
+            ),
+        )
+
+        self.text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            chunk_size=300,
+            chunk_overlap=50
+        )
+
+        self.embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        self.agent = get_conversational_agent()
+
+        self.prompt = ChatPromptTemplate.from_template(
+            "Answer the question based only on the following context:\n{context}\n\nQuestion: {question}"
+        )
+
+    def ai_agent(self, question, answer):
+        try:
+            if ask_gemini_to_evaluate(question, answer):
+                return answer
+
+            translated_question, original_lang = translate_to_english(question)
+
+            answer = self.agent.invoke(translated_question)
+
+            answer = translate_back(answer['output'], original_lang)
+        except Exception:
+            return "Bạn đã hết lượt hỏi trong ngày, vui lòng quay lại sau."
+        return answer

app.py

@@ -29,7 +29,11 @@ def prediction():
     model = InferenceModel()
     answer = model.predict(question=question, extract=ner_results)
 
-    return jsonify({"answer": answer}), 200
+    # Gọi AI Agent để đánh giá câu trả lời
+    ai_agent = AIAgent()
+    results = ai_agent.ai_agent(question, answer)
+
+    return jsonify({"answer": results}), 200
 
 if __name__ == '__main__':
     app.run(host="0.0.0.0", port=7860)