Update pages/bot.py

pages/bot.py

@@ -12,6 +12,7 @@ from transformers import AutoModel
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema import StrOutputParser
 from langchain.schema.runnable import RunnablePassthrough
+from langchain.chains import ConversationalRetrievalChain
 
 ###########
 #pip install faiss-cpu
@@ -76,7 +77,19 @@ def get_vectorstore():
     vectorstoreDB = FAISS.load_local(save_directory, embeddings)
     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
+
+
+
+#####    
 def main():
     #if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
     user_question = st.text_area("Stell mir eine Frage: ")
@@ -92,13 +105,13 @@ def main():
         st.text(user_question)
 
         ##IDEE Text Generation
-        generator = pipeline('text-generation', model = 'gpt2')
-        answer = generator(context, max_length = 30, num_return_sequences=3)
+        #generator = pipeline('text-generation', model = 'gpt2')
+        #answer = generator(context, max_length = 30, num_return_sequences=3)
         
-        st.text("FORMATIERTE ANTWORT:")
+        #st.text("FORMATIERTE ANTWORT:")
         #st.text_area()
-        st.text(answer)
-        st.text(type(answer))
+        #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")
@@ -110,12 +123,18 @@ def main():
         st.text("Basisantwort:")
         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