app16

app.py

@@ -66,11 +66,10 @@ vectordb = Chroma.from_documents(
 # define retriever
 retriever = vectordb.as_retriever(search_type="mmr")
 template = """Your name is AngryGreta and you are a recycling chatbot with the objective and poorpose to help people with waste management to improve environmental situation. Use the following pieces of context to answer the question if the question is related with recycling. Answer in the same language of the question. Keep the answer as concise as possible. Always say "thanks for asking!" at the end of the answer. 
-CONTEXT: {context}
-CHAT HISTORY:
-{chat_history}
-Question: {question}
-Helpful Answer:"""
+context: {context}
+chat history: {chat_history}
+question: {question}
+"""
 
 # Create the chat prompt templates
 system_prompt = SystemMessagePromptTemplate.from_template(template)
@@ -99,21 +98,21 @@ 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
 )
 
 def chat_interface(question):
 
-    result = qa_chain.run({"question": question})
+    answer = qa_chain.run({"question": question})
     print("Debug: Result from qa_chain.run:", result)
 
     # Check the structure of the result
-    if isinstance(result, str):
-        return result  # If the result is a string, return it directly
+    if isinstance(answer, str):
+        return answer  # If the result is a string, return it directly
     else:
-        return "Unexpected result format"
+        return "Unexpected answer format"
 
 chatbot_gradio_app = gr.Interface(
     fn=chat_interface,