Update pages/bot.py

pages/bot.py

@@ -79,15 +79,6 @@ def get_vectorstore():
     return vectorstoreDB
 
 ######
-"""
-def get_conversation_chain(vectorstore):
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=vectorstore.as_retriever()
-    )
-    return conversation_chain
-"""
 
 
 #####    
@@ -108,15 +99,6 @@ def main():
         st.text("Das ist der Kontext:")
         st.text(context)
 
-        ##IDEE Text Generation
-        #generator = pipeline('text-generation', model = 'gpt2')
-        #answer = generator(context, max_length = 30, num_return_sequences=3)
-        
-        #st.text("FORMATIERTE ANTWORT:")
-        #st.text_area()
-        #st.text(answer)
-        #st.text(type(answer))
-
         # Erstelle die Question Answering-Pipeline für Deutsch
         qa_pipeline = pipeline("question-answering", model="deutsche-telekom/bert-multi-english-german-squad2", tokenizer="deutsche-telekom/bert-multi-english-german-squad2")
 
@@ -128,47 +110,6 @@ def main():
         st.text(answer["answer"])
         st.text(answer)
 
-        ######
-
-        #newA = get_conversation_chain(get_vectorstore())
-        #st.text(newA)
-        
-        """
-        generator = pipeline('text-generation', model = 'tiiuae/falcon-40b')
-        generator(answer, max_length = 30, num_return_sequences=3)
-        st.text("Generierte Erweiterung:")
-        st.text(generator)
-        """
-            
-        """
-        #IDEE Retriever erweitern
-        template = Answer the question based only on the following context:
-
-        {context}
-        
-        Question: {question}
-        
-        prompt = ChatPromptTemplate.from_template(template)
-        model = AutoModel.from_pretrained("hkunlp/instructor-base")
-        
-        
-        def format_docs(docs):
-            return "\n\n".join([d.page_content for d in docs])
-        
-        
-        chain = (
-            {"context": retriever | format_docs, "question": RunnablePassthrough()}
-            | prompt
-            | model
-            | StrOutputParser()
-        )
-
-        ausgabetext = chain.invoke(user_question)
-        st.text(ausgabetext)
-        """
-
-
-
 
 if __name__ == '__main__':
     main()