Upload app.py

app.py

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+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 = 'mellogpt.Q3_K_S.gguf'
+MODEL_PATH = 'D:\python\Mantal Health 1\python\chatbot\mellogpt.Q3_K_S.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']}")
+