Create app.py

app.py

@@ -0,0 +1,69 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from langchain.llms import HuggingFacePipeline
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import WikipediaLoader
+from transformers import pipeline
+
+# Load T5-small model and tokenizer
+model_name = "google-t5/t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Create a text generation pipeline
+text_generation_pipeline = pipeline(
+    "text2text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=512,
+    temperature=0.7
+)
+
+# Create a LangChain LLM from the pipeline
+llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
+
+# Load and process documents
+#loader = TextLoader("https://en.wikipedia.org/wiki/Artificial_neuron")
+
+
+# Load content from Wikipedia
+loader = WikipediaLoader(query="Artificial neuron", load_max_docs=1)
+documents = loader.load()
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+# Create embeddings and vector store
+embeddings = HuggingFaceEmbeddings()
+db = Chroma.from_documents(texts, embeddings)
+
+# Create a retriever
+retriever = db.as_retriever()
+
+# Create a prompt template
+template = """Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+Context: {context}
+Question: {question}
+Answer:"""
+prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+# Create the RetrievalQA chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    return_source_documents=True,
+    chain_type_kwargs={"prompt": prompt}
+)
+
+# Example query
+query = "What is an artificial neuron?"
+result = qa_chain({"query": query})
+
+print("Question:", query)
+print("Answer:", result["result"])