Upload app.py

app.py

@@ -0,0 +1,81 @@
+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'])
+
+
+if __name__ == "__main__":
+    main()