app18

app.py

@@ -74,9 +74,11 @@ Your name is AngryGreta and you are a recycling chatbot with the objective to an
 Use the following pieces of context to answer the question if the question is related with recycling /
 No more than two chunks of context /
 Answer in the same language of the question /
-Always say "thanks for asking!" at the end of the answer. 
+Always say "thanks for asking!" at the end of the answer /
+If the context is not relevant, please answer the question by using your own knowledge about the topic.
+        
 context: {context}
-chat history: {chat_history}
+
 question: {question}
 """
 
@@ -84,7 +86,8 @@ question: {question}
 system_prompt = SystemMessagePromptTemplate.from_template(template)
 qa_prompt = ChatPromptTemplate(
 	messages=[
-		system_prompt, 
+		system_prompt,
+        MessagesPlaceholder(variable_name="chat_history"), 
 		HumanMessagePromptTemplate.from_template("{question}")
     ]
 )
@@ -98,28 +101,24 @@ llm = HuggingFaceHub(
         "repetition_penalty": 1.03,
     },
 )
-llm_chain = LLMChain(llm=llm, prompt=qa_prompt)
 
-memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", output_key='answer', return_messages=True)
+memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='question', output_key='answer', return_messages=True)
 
 qa_chain = ConversationalRetrievalChain.from_llm(
     llm = llm,
     memory = memory,
     retriever = retriever,
-    verbose = False,
+    verbose = True,
     combine_docs_chain_kwargs={'prompt': qa_prompt},
-    get_chat_history = lambda h : h
+    get_chat_history = lambda h : h,
+    rephrase_question = False,
+    output_key = 'answer'
 )
 
 def chat_interface(question,history):
 
     result = qa_chain.invoke({"question": question})
-
-    # Check the structure of the result
-    if isinstance(result['answer'], str):
-        return result['answer']  # If the result is a string, return it directly
-    else:
-        return "Unexpected answer format"
+    return result['answer']  # If the result is a string, return it directly
 
 chatbot_gradio_app = gr.ChatInterface(
     fn=chat_interface,