Update app.py

app.py

@@ -1,81 +1,89 @@
 import os
 import pickle
-os.environ["Hugging_Face_API_KEY"] = "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"
-
-urls = [
- 'https://zollege.in/exams/bitsat',
- 'https://zollege.in/exams/cat',
- 'https://zollege.in/exams/gate',
- 'https://zollege.in/exams/neet'
- 'https://zollege.in/exams/lsat',
- 'https://zollege.in/exams/jee-advanced',
- 'https://zollege.in/exams/aipmcet']
-
 from langchain.document_loaders import UnstructuredURLLoader
-loaders = UnstructuredURLLoader(urls=urls)
-data = loaders.load()
-
 from langchain.text_splitter import CharacterTextSplitter
-
-text_splitter = CharacterTextSplitter(separator='\n',
-                                      chunk_size=500,
-                                      chunk_overlap=20)
-
-
-docs = text_splitter.split_documents(data)
-
-
 from InstructorEmbedding import INSTRUCTOR
 from langchain.vectorstores import FAISS
-
 from langchain.embeddings import HuggingFaceInstructEmbeddings
-
-instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="sembeddings/model_gpt_trained"
-                        )  # nice working with model = sentence-transformers/all-MiniLM-L6-v2
-
-db_instructEmbedd = FAISS.from_documents(docs, instructor_embeddings)
-retriever = db_instructEmbedd.as_retriever(search_kwargs={"k": 3})
-
-with open("db_instructEmbedd.pkl", "wb") as f:
-    pickle.dump(db_instructEmbedd, f)
-
-with open("db_instructEmbedd.pkl", "rb") as f:
-    retriever = pickle.load(f)
-
-retriever = retriever.as_retriever(search_kwargs={"k": 3})
-
-
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = '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'
-
 from langchain.chains import RetrievalQA
 from langchain import HuggingFaceHub
-
-
-llm = llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.1}) #repo_id = gpt2 works goodd
-
-qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
-
-
 import streamlit as st
-
-st.title('Chatbot Trained on Indian Exam Articles')
-st.header("Hi!! How Can I Help You ?")
-
-query = st.chat_input('> ')
-result = qa({'query': query})
-st.write(result['result'])
-st.button('Not Satisfied! Talk to our Expert Here..')
-
-import pprint
-
-os.environ["SERPER_API_KEY"] = "a69857e460dd51585e009a43743711b110b6beee"
-
 from langchain.utilities import GoogleSerperAPIWrapper
 
-search = GoogleSerperAPIWrapper()
-
-search.run(result['query'])
-
-
+class Chatbot:
+    def __init__(self):
+        os.environ["Hugging_Face_API_KEY"] = "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"
+        os.environ["HUGGINGFACEHUB_API_TOKEN"] = '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'
+        os.environ["SERPER_API_KEY"] = "a69857e460dd51585e009a43743711b110b6beee"
+        
+    def load_data(self):
+        urls = [
+            'https://zollege.in/exams/bitsat',
+            'https://zollege.in/exams/cat',
+            'https://zollege.in/exams/gate',
+            'https://zollege.in/exams/neet',
+            'https://zollege.in/exams/lsat',
+            'https://zollege.in/exams/jee-advanced',
+            'https://zollege.in/exams/aipmcet'
+        ]
+
+        loaders = UnstructuredURLLoader(urls=urls)
+        data = loaders.load()
+
+        return data
+
+    def split_documents(self, data):
+        text_splitter = CharacterTextSplitter(separator='\n', chunk_size=500, chunk_overlap=20)
+        docs = text_splitter.split_documents(data)
+
+        return docs
+
+    def create_embeddings(self, docs):
+        instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="sembeddings/model_gpt_trained")
+        db_instructEmbedd = FAISS.from_documents(docs, instructor_embeddings)
+        retriever = db_instructEmbedd.as_retriever(search_kwargs={"k": 3})
+
+        with open("db_instructEmbedd.pkl", "wb") as f:
+            pickle.dump(db_instructEmbedd, f)
+
+        return retriever
+
+    def load_embeddings(self):
+        with open("db_instructEmbedd.pkl", "rb") as f:
+            retriever = pickle.load(f)
+        
+        retriever = retriever.as_retriever(search_kwargs={"k": 3})
+        return retriever
+
+    def create_qa_model(self, retriever):
+        llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.1})
+        qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+        return qa
+
+    def run_chatbot(self):
+        st.title('Chatbot Trained on Indian Exam Articles')
+        st.header("Hi!! How Can I Help You ?")
+
+        query = st.text_input('> ')
+        result = self.qa({'query': query})
+        st.write(result['result'])
+        st.button('Not Satisfied! Talk to our Expert Here..')
+
+    def run_google_search(self, query):
+        search = GoogleSerperAPIWrapper()
+        search.run(query)
+        
 if __name__ == "__main__":
-    main()
+    chatbot = Chatbot()
+    data = chatbot.load_data()
+    docs = chatbot.split_documents(data)
+    retriever = chatbot.create_embeddings(docs)
+    qa = chatbot.create_qa_model(retriever)
+
+    st.title('Chatbot Trained on Indian Exam Articles')
+    st.header("Hi!! How Can I Help You ?")
+
+    query = st.text_input('> ')
+    result = qa({'query': query})
+    st.write(result['result'])
+    st.button('Not Satisfied! Talk to our Expert Here..')