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Update app.py
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app.py
CHANGED
@@ -2,25 +2,20 @@ from flask import Flask, request, jsonify, render_template
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from flask_cors import CORS
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from dotenv import load_dotenv
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import os
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import re
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_core.prompts import PromptTemplate
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from langchain.chains import RetrievalQA
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app = Flask(__name__)
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CORS(app)
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# Load environment variables
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load_dotenv()
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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if not GOOGLE_API_KEY:
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raise ValueError("GOOGLE_API_KEY not found in environment variables.")
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# Lazy globals
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qa_chain = None
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def get_qa_chain():
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global qa_chain
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if qa_chain is None:
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@@ -30,7 +25,6 @@ def get_qa_chain():
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google_api_key=GOOGLE_API_KEY,
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convert_system_message_to_human=True
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)
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# Embeddings and vector store
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embedding_model = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
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vectordb = Chroma(
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@@ -39,7 +33,6 @@ def get_qa_chain():
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collection_name="pdf_search_chroma"
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)
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retriever = vectordb.as_retriever(search_kwargs={"k": 6})
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# Prompt
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prompt_template = PromptTemplate.from_template("""
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You are an intelligent assistant for students asking about their university.
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@@ -52,7 +45,6 @@ def get_qa_chain():
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{question}
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Answer:
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""")
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# Create chain
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qa_chain = RetrievalQA.from_chain_type(
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llm=llm,
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@@ -61,35 +53,20 @@ def get_qa_chain():
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chain_type_kwargs={"prompt": prompt_template}
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)
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return qa_chain
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def convert_links_to_html(text):
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"""
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Convert links in the format [Text][URL] to HTML <a> tags.
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Example: [Profile][https://example.com] becomes <a href="https://example.com">Profile</a>
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"""
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pattern = r'\[(.*?)\]\[(.*?)\]'
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return re.sub(pattern, r'<a href="\2">\1</a>', text)
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@app.route("/")
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def home():
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return render_template("index.html")
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@app.route("/get", methods=["POST"])
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def get_response():
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data = request.get_json()
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query = data.get("message", "")
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if not query:
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return jsonify({"response": {"response": "No message received."}}), 400
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chain = get_qa_chain()
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try:
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response = chain.run(query)
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response_with_html_links = convert_links_to_html(response)
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return jsonify({"response": {"response": response_with_html_links}})
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except Exception as e:
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return jsonify({"response": {"response": f"Error: {str(e)}"}}), 500
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if __name__ == "__main__":
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app.run(debug=True)
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from flask_cors import CORS
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from dotenv import load_dotenv
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import os
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_core.prompts import PromptTemplate
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from langchain.chains import RetrievalQA
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app = Flask(name)
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CORS(app)
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# Load environment variables
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load_dotenv()
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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if not GOOGLE_API_KEY:
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raise ValueError("GOOGLE_API_KEY not found in environment variables.")
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# Lazy globals
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qa_chain = None
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def get_qa_chain():
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global qa_chain
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if qa_chain is None:
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google_api_key=GOOGLE_API_KEY,
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convert_system_message_to_human=True
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)
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# Embeddings and vector store
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embedding_model = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
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vectordb = Chroma(
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collection_name="pdf_search_chroma"
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)
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retriever = vectordb.as_retriever(search_kwargs={"k": 6})
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# Prompt
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prompt_template = PromptTemplate.from_template("""
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You are an intelligent assistant for students asking about their university.
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{question}
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Answer:
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""")
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# Create chain
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qa_chain = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type_kwargs={"prompt": prompt_template}
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)
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return qa_chain
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@app.route("/")
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def home():
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return render_template("index.html")
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@app.route("/get", methods=["POST"])
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def get_response():
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data = request.get_json()
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query = data.get("message", "")
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if not query:
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return jsonify({"response": {"response": "No message received."}}), 400
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chain = get_qa_chain()
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try:
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response = chain.run(query)
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return jsonify({"response": {"response": response}})
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except Exception as e:
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return jsonify({"response": {"response": f"Error: {str(e)}"}}), 500
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if name == "main":
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app.run(debug=True)
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