Update app.py

app.py

@@ -1,153 +0,0 @@
-try:
-  from langchain_community.vectorstores import Chroma
-except:
-  from langchain_community.vectorstores import Chroma
-
-from langchain.chains import ConversationChain
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-
-
-# Import the necessary libraries.
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-
-# Initialize a ChatGroq object with a temperature of 0 and the "mixtral-8x7b-32768" model.
-llm = ChatGroq(temperature=0, model_name="llama3-70b-8192",api_key='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')
-
-from langchain_community.embeddings import SentenceTransformerEmbeddings
-
-embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"trust_remote_code":True}) 
-
-
-
-
-
-memory = ConversationBufferWindowMemory(
-    memory_key="history", k=3, return_only_outputs=True
-)
-
-
-
-
-
-
-query_text="what did alice say to rabbit"
-
-# Prepare the DB.
-#embedding_function = OpenAIEmbeddings() # main
-
-CHROMA_PATH = "chroma8"
-# call the chroma generated in a directory
-db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
-
-# Search the DB for similar documents to the query.
-results = db.similarity_search_with_relevance_scores(query_text, k=2)
-if len(results) == 0 or results[0][1] < 0.5:
-    print(f"Unable to find matching results.")
-
-
-
-
-
-
-
-
-
-from langchain import PromptTemplate
-query_text = "when did alice see mad hatter"
-
-results = db.similarity_search_with_relevance_scores(query_text, k=3)
-if len(results) == 0 or results[0][1] < 0.5:
-    print(f"Unable to find matching results.")
-
-
-context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
-
-template = """
-The following is a conversation between a human an AI. Answer  question based only on the conversation.
-
-Current conversation:
-{history}
-
-"""
-
-
-
-s="""
-
-\n question: {input}
-
-\n answer:""".strip()
-
-
-prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
-
-
-
-
-chain = ConversationChain(
-    llm=llm,
-    
-    prompt=prompt,
-    memory=memory,
-    verbose=True,
-)
-
-
-
-
-# Generate a response from the Llama model
-def get_llama_response(message: str, history: list) -> str:
-    """
-    Generates a conversational response from the Llama model.
-
-    Parameters:
-        message (str): User's input message.
-        history (list): Past conversation history.
-
-    Returns:
-        str: Generated response from the Llama model.
-    """
-    query_text =message
-
-    results = db.similarity_search_with_relevance_scores(query_text, k=2)
-    if len(results) == 0 or results[0][1] < 0.5:
-        print(f"Unable to find matching results.")
-
-
-    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
-
-    template = """
-    The following is a conversation between a human an AI. Answer  question based only on the conversation.
-
-    Current conversation:
-    {history}
-
-    """
-
-
-
-    s="""
-
-    \n question: {input}
-
-    \n answer:""".strip()
-
-
-    prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
-
-    #print(template)
-    chain.prompt=prompt
-    res = chain.predict(input=query_text)
-    return res
-        #return response.strip()
-
-
-
-import gradio as gr
-iface = gr.Interface(fn=get_llama_response, inputs=gr.Textbox(),
-             outputs="textbox")
-iface.launch(share=True)
-
-
-