File size: 8,170 Bytes
4ec0915
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f3bca8e
4ec0915
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f44a0c0
f3bca8e
 
 
 
f44a0c0
 
f3bca8e
 
4ec0915
 
 
f44a0c0
 
 
 
f3bca8e
 
4ec0915
 
 
f3bca8e
4ec0915
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f44a0c0
4ec0915
 
 
 
 
 
 
f44a0c0
4ec0915
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f3bca8e
4ec0915
 
 
 
 
 
 
f44a0c0
f3bca8e
 
4ec0915
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f44a0c0
4ec0915
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
import os
import logging
from langchain.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from langchain.schema import Document
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import chardet
import gradio as gr
import pandas as pd
import json
import re

# Enable logging for debugging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Function to clean the API key
def clean_api_key(key):
    return ''.join(c for c in key if ord(c) < 128)

# Load the GROQ API key from environment variables (set as a secret in the Space)
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
    logger.error("GROQ_API_KEY environment variable is not set. Please add it as a secret.")
    raise ValueError("GROQ_API_KEY environment variable is not set. Please add it as a secret.")
api_key = clean_api_key(api_key).strip()  # Clean and strip whitespace

# Function to clean text by removing non-ASCII characters
def clean_text(text):
    return text.encode("ascii", errors="ignore").decode()

# Function to load and clean documents from multiple file formats
def load_documents(file_paths):
    docs = []
    for file_path in file_paths:
        ext = os.path.splitext(file_path)[-1].lower()
        try:
            if ext == ".csv":
                # Handle CSV files
                with open(file_path, 'rb') as f:
                    result = chardet.detect(f.read())
                    encoding = result['encoding']
                data = pd.read_csv(file_path, encoding=encoding)
                for index, row in data.iterrows():
                    content = clean_text(row.to_string())
                    docs.append(Document(page_content=content, metadata={"source": file_path}))
            elif ext == ".json":
                # Handle JSON files
                with open(file_path, 'r', encoding='utf-8') as f:
                    data = json.load(f)
                    if isinstance(data, list):
                        for entry in data:
                            content = clean_text(json.dumps(entry))
                            docs.append(Document(page_content=content, metadata={"source": file_path}))
                    elif isinstance(data, dict):
                        content = clean_text(json.dumps(data))
                        docs.append(Document(page_content=content, metadata={"source": file_path}))
            elif ext == ".txt":
                # Handle TXT files
                with open(file_path, 'r', encoding='utf-8') as f:
                    content = clean_text(f.read())
                    docs.append(Document(page_content=content, metadata={"source": file_path}))
            else:
                logger.warning(f"Unsupported file format: {file_path}")
        except Exception as e:
            logger.error(f"Error processing file {file_path}: {e}")
    return docs

# Function to ensure the response ends with complete sentences
def ensure_complete_sentences(text):
    # Use regex to find all complete sentences
    sentences = re.findall(r'[^.!?]*[.!?]', text)
    if sentences:
        # Join all complete sentences to form the complete answer
        return ' '.join(sentences).strip()
    return text  # Return as is if no complete sentence is found

# Initialize the LLM using ChatGroq with GROQ's API
def initialize_llm(model, temperature, max_tokens):
    try:
        # Allocate a portion of tokens for the prompt, e.g., 20%
        prompt_allocation = int(max_tokens * 0.2)
        response_max_tokens = max_tokens - prompt_allocation
        if response_max_tokens <= 50:
            raise ValueError("max_tokens is too small to allocate for the response.")

        llm = ChatGroq(
            model=model,
            temperature=temperature,
            max_tokens=response_max_tokens,  # Adjusted max_tokens
            api_key=api_key  # Ensure the API key is passed correctly
        )
        logger.debug("LLM initialized successfully.")
        return llm
    except Exception as e:
        logger.error(f"Error initializing LLM: {e}")
        raise

# Create the RAG pipeline
def create_rag_pipeline(file_paths, model, temperature, max_tokens):
    try:
        llm = initialize_llm(model, temperature, max_tokens)
        docs = load_documents(file_paths)
        if not docs:
            logger.warning("No documents were loaded. Please check your file paths and formats.")
            return None, "No documents were loaded. Please check your file paths and formats."

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        splits = text_splitter.split_documents(docs)

        # Initialize the embedding model
        embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

        # Use a persistent database for Chroma
        vectorstore = Chroma.from_documents(
            documents=splits,
            embedding=embedding_model,
            persist_directory="./chroma_db"  # Specify persistent storage directory
        )
        vectorstore.persist()  # Save the database to disk
        logger.debug("Vectorstore initialized and persisted successfully.")

        retriever = vectorstore.as_retriever()

        custom_prompt_template = PromptTemplate(
            input_variables=["context", "question"],
            template="""
            You are an AI assistant with expertise in daily wellness. Your aim is to provide detailed and comprehensive solutions regarding daily wellness topics without unnecessary verbosity.

            Context:
            {context}

            Question:
            {question}

            Provide a thorough and complete answer, including relevant examples and a suggested schedule. Ensure that the response does not end abruptly.
            """
        )

        rag_chain = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=retriever,
            chain_type_kwargs={"prompt": custom_prompt_template}
        )
        logger.debug("RAG pipeline created successfully.")
        return rag_chain, "Pipeline created successfully."
    except Exception as e:
        logger.error(f"Error creating RAG pipeline: {e}")
        return None, f"Error creating RAG pipeline: {e}"

# Function to answer questions with post-processing
def answer_question(file_paths, model, temperature, max_tokens, question):
    rag_chain, message = create_rag_pipeline(file_paths, model, temperature, max_tokens)
    if rag_chain is None:
        return message
    try:
        answer = rag_chain.run(question)
        logger.debug("Question answered successfully.")
        # Post-process to ensure the answer ends with complete sentences
        complete_answer = ensure_complete_sentences(answer)
        return complete_answer
    except Exception as e:
        logger.error(f"Error during RAG pipeline execution: {e}")
        return f"Error during RAG pipeline execution: {e}"

# Gradio Interface
def gradio_interface(model, temperature, max_tokens, question):
    file_paths = ['AIChatbot.csv']  # Ensure this file is present in your Space root directory
    return answer_question(file_paths, model, temperature, max_tokens, question)

# Define Gradio UI
interface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(label="Model Name", value="llama3-8b-8192"),
        gr.Slider(label="Temperature", minimum=0, maximum=1, step=0.01, value=0.7),
        gr.Slider(label="Max Tokens", minimum=200, maximum=1024, step=1, value=500),
        gr.Textbox(label="Question")
    ],
    outputs="text",
    title="Daily Wellness AI",
    description="Ask questions about daily wellness and get detailed solutions."
)

# Launch Gradio app without share=True (not supported on Hugging Face Spaces)
if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860, debug=True)