Update app.py

app.py

@@ -10,21 +10,24 @@ from langchain.schema import Document
 from langchain_community.vectorstores import Qdrant
 from langchain.chains import RetrievalQA
 from langchain.llms import HuggingFaceHub
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough, RunnableMap
+from langchain_google_genai import GoogleGenerativeAI
 hf_token=os.environ["HUGGINGFACEHUB_API_TOKEN"]
 model_name_or_path = "ahmadmac/Trained-T5-large"
-pipe = pipeline(
-    'text2text-generation',
-    model=model_name_or_path,
-    max_length=512,
-    do_sample=True,
-    temperature=1.0
-)
-llm = HuggingFacePipeline(pipeline=pipe)
-prompt_template = """ you are a highly knowledgeable AI assistant. Engage in a conversation with the user. Your main goal is to provide clear and informative answers to the user's questions.
-User: {question}
-Assistant:"""
-prompt = PromptTemplate(template=prompt_template, input_variables=["question"])
-chain = LLMChain(llm=llm, prompt=prompt)
+# pipe = pipeline(
+#     'text2text-generation',
+#     model=model_name_or_path,
+#     max_length=512,
+#     do_sample=True,
+#     temperature=1.0
+# )
+# llm = HuggingFacePipeline(pipeline=pipe)
+# prompt_template = """ you are a highly knowledgeable AI assistant. Engage in a conversation with the user. Your main goal is to provide clear and informative answers to the user's questions.
+# User: {question}
+# Assistant:"""
+# prompt = PromptTemplate(template=prompt_template, input_variables=["question"])
+# chain = LLMChain(llm=llm, prompt=prompt)
 with open("brookline_data.txt", "r") as f:
     data = f.read()
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
@@ -39,17 +42,22 @@ qdrant = Qdrant.from_documents(
     collection_name="my_documents",
 )
 retriever = qdrant.as_retriever()
+# qna = RetrievalQA.from_chain_type(
+#     llm=HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0.9, "max_length": 512}, 
+#                        huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"]),
+#     chain_type="stuff",
+#     retriever=retriever
+# )
 qna = RetrievalQA.from_chain_type(
-    llm=HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0.9, "max_length": 512}, 
-                       huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"]),
+    llm=GoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=os.environ["google_api_key"]),
     chain_type="stuff",
     retriever=retriever
 )
 def chatbot(question, chat_history):
-    response = chain.run(question)
+    #response = chain.run(question)
     retrieval_result = qna(question)
     retrieval_answer = retrieval_result['result']
-    combined_response = f"Based on the information available:\n{retrieval_answer}\n Response through LLM:\n{response}"
+    combined_response = f"Based on the information available:\n{retrieval_answer}"
     return combined_response
 demo = gr.ChatInterface(
     fn=chatbot,