Delete chatbot.py

chatbot.py

@@ -1,95 +0,0 @@
-from llama_cpp import Llama
-import streamlit as st
-from langchain.llms.base import LLM
-from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext, PromptHelper
-from typing import Optional, List, Mapping, Any
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-import pandas as pd
-
-# Set the page config as the first command
-st.set_page_config(page_title='Mental Heallth chatbot', page_icon=':robot_face:', layout='wide')
-
-# Define constants
-MODEL_NAME = 'TheBloke/MelloGPT-GGUF'
-MODEL_PATH = 'TheBloke/MelloGPT-GGUF'
-KNOWLEDGE_BASE_FILE = "mentalhealth.csv"
-
-# Configuration
-NUM_THREADS = 8
-MAX_INPUT_SIZE = 2048
-NUM_OUTPUT = 256
-CHUNK_OVERLAP_RATIO = 0.10
-
-# Initialize prompt helper with fallback on exception
-try:
-    prompt_helper = PromptHelper(MAX_INPUT_SIZE, NUM_OUTPUT, CHUNK_OVERLAP_RATIO)
-except Exception as e:
-    CHUNK_OVERLAP_RATIO = 0.2
-    prompt_helper = PromptHelper(MAX_INPUT_SIZE, NUM_OUTPUT, CHUNK_OVERLAP_RATIO)
-
-embed_model = LangchainEmbedding(HuggingFaceEmbeddings())
-
-class CustomLLM(LLM):
-    model_name = MODEL_NAME
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        p = f"Human: {prompt} Assistant: "
-        prompt_length = len(p)
-        llm = Llama(model_path=MODEL_PATH, n_threads=NUM_THREADS)
-        try:
-            output = llm(p, max_tokens=512, stop=["Human:"], echo=True)['choices'][0]['text']
-            response = output[prompt_length:]
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            st.session_state.messages.append({"role": "assistant", "content": response})
-        except Exception as e:
-            st.error("An error occurred while processing your request. Please try again.")
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        return {"name_of_model": self.model_name}
-
-    @property
-    def _llm_type(self) -> str:
-        return "custom"
-
-# Cache functions using the new methods
-@st.cache_resource
-def load_model():
-    return CustomLLM()
-
-@st.cache_data
-def load_knowledge_base():
-    df = pd.read_csv(KNOWLEDGE_BASE_FILE)
-    return dict(zip(df['Questions'].str.lower(), df['Answers']))
-
-def clear_convo():
-    st.session_state['messages'] = []
-
-def init():
-    if 'messages' not in st.session_state:
-        st.session_state['messages'] = []
-
-# Main function
-if __name__ == '__main__':
-    init()
-    knowledge_base = load_knowledge_base()
-    llm = load_model()
-
-    clear_button = st.sidebar.button("Clear Conversation")
-    if clear_button:
-        clear_convo()
-
-    user_input = st.text_input("Enter your query:", key="user_input")
-    if user_input:
-        user_input = user_input.lower()
-        answer = knowledge_base.get(user_input)
-        if answer:
-            st.session_state.messages.append({"role": "user", "content": user_input})
-            st.session_state.messages.append({"role": "assistant", "content": answer})
-        else:
-            llm._call(prompt=user_input)
-
-    for message in st.session_state.messages:
-        with st.container():
-            st.markdown(f"**{message['role'].title()}**: {message['content']}")
-