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Update app.py
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app.py
CHANGED
@@ -1,284 +1,93 @@
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import os
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import requests
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import time
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from io import BytesIO
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from typing import Iterator, List, Dict, Any, Optional
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from urllib.parse import urljoin, urlparse
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# Data processing imports
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import pandas as pd
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import
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from
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from bs4 import BeautifulSoup
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# AI and NLP imports
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from openai import OpenAI
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from langchain_huggingface import HuggingFaceEmbeddings
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from
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.schema import Document
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import gradio as gr
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"""Handles processing of various data sources including CSV, Excel, PDF, and web content."""
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def __init__(self, folder_path: str = "./"):
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self.folder_path = folder_path
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self.files = os.listdir(folder_path)
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def process_tabular_data(self) -> List[str]:
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"""Process CSV and Excel files to extract data."""
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context_data = []
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data_files = [f for f in self.files if f.endswith(('.csv', '.xlsx', '.xls'))]
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for f, file in enumerate(data_files, 1):
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print(f"\nProcessing file {f}: {file}")
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file_path = os.path.join(self.folder_path, file)
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try:
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# Read file based on extension
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if file.endswith('.csv'):
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df = pd.read_csv(file_path)
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else:
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df = pd.read_excel(file_path)
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# Extract non-empty values from column 2
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context_data.extend(df.iloc[:, 2].dropna().astype(str).tolist())
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except Exception as e:
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print(f"Error processing file {file}: {str(e)}")
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return context_data
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def extract_text_from_pdf(self, pdf_path: str) -> str:
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"""Extract text content from a PDF file."""
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try:
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with open(pdf_path, "rb") as file:
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reader = PyPDF2.PdfReader(file)
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return "".join(page.extract_text() or "" for page in reader.pages)
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except Exception as e:
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print(f"Error with {pdf_path}: {e}")
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return ""
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def process_pdf_files(self) -> List[Document]:
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"""Process all PDF files and return documents."""
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pdf_files = [f for f in self.files if f.lower().endswith(".pdf")]
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documents = []
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for file in pdf_files:
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print(f"Processing: {file}")
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pdf_path = os.path.join(self.folder_path, file)
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text = self.extract_text_from_pdf(pdf_path)
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if text:
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documents.append(Document(page_content=text, metadata={"source": file}))
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return documents
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def split_documents(self, documents: List[Document], chunk_size: int = 500) -> List[str]:
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"""Split documents into manageable chunks."""
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text_splitter = RecursiveCharacterTextSplitter(
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separators=['\n\n', '\n', '.', ','],
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chunk_size=chunk_size,
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chunk_overlap=50
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)
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chunks = text_splitter.split_documents(documents)
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return [chunk.page_content for chunk in chunks]
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def extract_pdf_text_from_url(self, pdf_url: str) -> Optional[str]:
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"""Extract text from a PDF URL."""
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try:
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response = requests.get(pdf_url)
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response.raise_for_status()
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with BytesIO(response.content) as file:
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reader = PdfReader(file)
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pdf_text = ""
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for page in reader.pages:
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pdf_text += page.extract_text()
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return pdf_text if pdf_text else None
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except requests.exceptions.RequestException as e:
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print(f"Error fetching PDF {pdf_url}: {e}")
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return None
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except Exception as e:
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print(f"Error reading PDF {pdf_url}: {e}")
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return None
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"""Scrape content from a list of base URLs and their internal links."""
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try:
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visited_links = set()
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content_by_url = {}
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for base_url in base_urls:
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if not base_url.strip():
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continue
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print(f"Scraping base URL: {base_url}")
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html_content = self.fetch_page_content(base_url)
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if html_content:
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cleaned_content = self.clean_body_content(html_content)
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content_by_url[base_url] = cleaned_content
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visited_links.add(base_url)
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# Extract and process internal links
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soup = BeautifulSoup(html_content, "html.parser")
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links = self.extract_internal_links(base_url, soup)
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for link in links:
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if link not in visited_links:
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print(f"Scraping link: {link}")
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page_content = self.fetch_page_content(link)
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if page_content:
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cleaned_content = self.clean_body_content(page_content)
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content_by_url[link] = cleaned_content
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visited_links.add(link)
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# Extract PDF content if link is a PDF
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if link.lower().endswith('.pdf'):
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print(f"Extracting PDF content from: {link}")
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pdf_processor = DataProcessor()
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pdf_content = pdf_processor.extract_pdf_text_from_url(link)
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if pdf_content:
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content_by_url[link] = pdf_content
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return content_by_url
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except Exception as e:
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print(f"Error during scraping: {e}")
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return {}
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def fetch_page_content(self, url: str) -> Optional[str]:
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"""Fetch HTML content from a URL."""
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try:
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response = requests.get(url, timeout=10)
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response.raise_for_status()
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return response.text
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except requests.exceptions.RequestException as e:
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print(f"Error fetching {url}: {e}")
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return None
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def extract_internal_links(self, base_url: str, soup: BeautifulSoup) -> set:
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"""Extract internal links from a BeautifulSoup object."""
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links = set()
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for anchor in soup.find_all("a", href=True):
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href = anchor["href"]
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full_url = urljoin(base_url, href)
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if self.is_internal_link(base_url, full_url):
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links.add(full_url)
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return links
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link_netloc = urlparse(link_url).netloc
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return base_netloc == link_netloc
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# Remove scripts and styles
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for script_or_style in soup(["script", "style"]):
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script_or_style.extract()
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# Get text and clean up
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cleaned_content = soup.get_text(separator="\n")
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cleaned_content = "\n".join(
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line.strip() for line in cleaned_content.splitlines() if line.strip()
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)
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return cleaned_content
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class TranslationService:
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"""Translation service using Hugging Face API."""
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def __init__(self, api_token: str, model_name: str = "facebook/nllb-200-distilled-600M"):
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self.model_name = model_name
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self.url = f"https://api-inference.huggingface.co/models/{model_name}"
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self.headers = {"Authorization": f"Bearer {api_token}"}
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def translate_text(self, text: str, src_lang: str, tgt_lang: str) -> str:
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"""Translate text using Hugging Face API."""
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try:
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"parameters": {
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"src_lang": src_lang,
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"tgt_lang": tgt_lang
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}
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}
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)
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if
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else:
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print(f"
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except Exception as e:
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print(f"
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)
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self.headers = {
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"HTTP-Referer": "http://localhost:3000",
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"X-Title": "Local Development"
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}
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except Exception as e:
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print(f"Initialization error: {e}")
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raise
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completion = self.client.chat.completions.create(
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# model="meta-llama/llama-3.3-70b-instruct:free",
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model="meta-llama/llama-4-maverick:free",
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messages=[{"role": "user", "content": prompt}],
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stream=True
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)
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for chunk in completion:
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delta = chunk.choices[0].delta
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if hasattr(delta, "content") and delta.content:
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yield delta.content
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except Exception as e:
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yield f"Streaming error: {str(e)}"
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class UserSession:
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def __init__(self, llm: OpenRouterLLM):
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self.current_user = None
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self.welcome_message = None
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self.conversation_history = []
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self.llm = llm
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def set_user(self, user_info
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"""Set current user and initialize welcome message."""
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self.current_user = user_info
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self.set_welcome_message(user_info.get("Nickname", "Guest"))
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# Initialize conversation history with welcome message
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self.conversation_history = [
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{"role": "assistant", "content": welcome},
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]
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def get_user(self)
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"""Get current user information."""
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return self.current_user
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def set_welcome_message(self, nickname
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"""Set a dynamic welcome message using the LLM."""
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prompt = (
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f"Create a very brief welcome message for {nickname}. "
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f"The message should: "
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f"1. Welcome {nickname} warmly and professionally. "
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f"2. Emphasize that this is a safe and trusted space. "
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f"3. Highlight specialized support for gender-based violence (GBV) and legal assistance. "
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f"4. Use a tone that is warm, reassuring, and professional. "
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f"5. Keep the message concise and impactful."
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)
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self.welcome_message = f"Welcome {nickname}! This is a safe space where you can find support and resources."
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def get_welcome_message(self) -> str:
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"""Get the welcome message."""
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return self.welcome_message or "Welcome! This is a safe space where you can find support."
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def add_to_history(self, role: str, message: str) -> None:
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"""Add a message to the conversation history."""
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self.conversation_history.append({"role": role, "content": message})
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def get_conversation_history(self)
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"""Get the full conversation history
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return self.conversation_history
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def get_formatted_history(self)
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"""Get conversation history formatted as a string for the LLM
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formatted_history = ""
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for entry in self.conversation_history:
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role = "User" if entry["role"] == "user" else "Assistant"
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formatted_history += f"{role}: {entry['content']}\n\n"
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return formatted_history
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if not self.api_key:
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print("Warning: V2 API key not found in environment variables.")
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self.api_key = "demo_key" # Use a placeholder value
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if not self.api_token:
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print("Warning: Token not found in environment variables.")
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self.api_token = "demo_token" # Use a placeholder value
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self.llm_instance = OpenRouterLLM(key=self.api_key)
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self.user_session = UserSession(self.llm_instance)
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self.translator = TranslationService(self.api_token)
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# Initialize embedding model
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try:
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self.embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
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# Initialize vector store
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self.vectorstore = Chroma(
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collection_name="Dataset",
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embedding_function=self.embed_model,
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)
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except Exception as e:
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print(f"Error initializing embeddings: {e}")
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# Create a simple placeholder for vectorstore if initialization fails
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self.vectorstore = None
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# Template for GBV support chatbot
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self.template = """
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You are a compassionate and supportive AI assistant specializing in helping individuals affected by Gender-Based Violence (GBV). Your responses must be based EXCLUSIVELY on the information provided in the context. Your primary goal is to provide emotionally intelligent support while maintaining appropriate boundaries.
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**Previous conversation:** {conversation_history}
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**Context information:** {context}
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**User's Question:** {question}
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3. **Emotional Intelligence**
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- Validate feelings without judgment
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- Offer reassurance when appropriate, always centered on empowerment
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- Adjust your tone based on the emotional state conveyed
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4. **Conversation Management**
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- Refer to {conversation_history} to maintain continuity and avoid repetition
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- Use clear paragraph breaks for readability
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5. **Information Delivery**
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- Extract only relevant information from {context} that directly addresses the question
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- Present information in accessible, non-technical language
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- When information is unavailable, respond with: "I don't have that specific information right now, {first_name}. Would it be helpful if I focus on [alternative support option]?"
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6. **Safety and Ethics**
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- Do not generate any speculative content or advice not supported by the context
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- If the context contains safety information, prioritize sharing that information
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Your response must come entirely from the provided context, maintaining the supportive tone while never introducing information from outside the provided materials.
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**Context:** {context}
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**User's Question:** {question}
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**Your Response:**
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"""
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def load_data(self) -> None:
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"""Load and process all data sources."""
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if not self.vectorstore:
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print("Warning: Vector store not initialized. Skipping data loading.")
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return
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try:
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# Process all data sources
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data_processor = DataProcessor()
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context_data = data_processor.process_tabular_data()
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# Process PDFs
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pdf_documents = data_processor.process_pdf_files()
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text_chunks = data_processor.split_documents(pdf_documents)
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# Combine all data
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all_data = []
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all_data.extend(context_data)
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all_data.extend([item for item in text_chunks if item not in all_data])
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if all_data:
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# Add data to vector store
|
447 |
-
self.vectorstore.add_texts(all_data)
|
448 |
-
else:
|
449 |
-
print("Warning: No data found to load into vector store.")
|
450 |
-
except Exception as e:
|
451 |
-
print(f"Error loading data: {e}")
|
452 |
-
|
453 |
-
def create_rag_chain(self):
|
454 |
-
"""Create RAG chain with user context and conversation history."""
|
455 |
-
try:
|
456 |
-
if self.vectorstore:
|
457 |
-
retriever = self.vectorstore.as_retriever()
|
458 |
-
else:
|
459 |
-
# Create a simple fallback if vectorstore is not available
|
460 |
-
retriever = FallbackRetriever()
|
461 |
-
|
462 |
-
rag_prompt = PromptTemplate.from_template(self.template)
|
463 |
-
|
464 |
-
def stream_func(input_dict):
|
465 |
-
try:
|
466 |
-
# Get context using the retriever's invoke method
|
467 |
-
if self.vectorstore:
|
468 |
-
context = retriever.invoke(input_dict["question"])
|
469 |
-
context_str = "\n".join([doc.page_content for doc in context])
|
470 |
-
else:
|
471 |
-
context_str = "No specific information available on this topic."
|
472 |
-
|
473 |
-
# Get user info from the session
|
474 |
-
user_info = self.user_session.get_user() or {}
|
475 |
-
first_name = user_info.get("Nickname", "User")
|
476 |
-
|
477 |
-
# Get conversation history
|
478 |
-
conversation_history = self.user_session.get_formatted_history()
|
479 |
-
|
480 |
-
# Format prompt with user context and conversation history
|
481 |
-
prompt = rag_prompt.format(
|
482 |
-
context=context_str,
|
483 |
-
question=input_dict["question"],
|
484 |
-
first_name=first_name,
|
485 |
-
conversation_history=conversation_history
|
486 |
-
)
|
487 |
-
|
488 |
-
# Stream response
|
489 |
-
return self.llm_instance.stream(prompt)
|
490 |
-
except Exception as e:
|
491 |
-
print(f"Error in RAG chain: {e}")
|
492 |
-
yield f"I apologize, but I'm having trouble processing your request. Please try again or rephrase your question."
|
493 |
-
|
494 |
-
return stream_func
|
495 |
-
except Exception as e:
|
496 |
-
print(f"Error creating RAG chain: {e}")
|
497 |
-
|
498 |
-
# Return a simple fallback function
|
499 |
-
def fallback_func(input_dict):
|
500 |
-
yield "I apologize, but I'm having technical difficulties. Please try again later."
|
501 |
-
|
502 |
-
return fallback_func
|
503 |
-
|
504 |
-
def collect_user_info(self, nickname: str):
|
505 |
-
"""Collect and process user information."""
|
506 |
-
if not nickname:
|
507 |
-
return "Nickname is required to proceed.", gr.update(visible=False), gr.update(visible=True), []
|
508 |
-
|
509 |
-
# Store user info for chat session
|
510 |
-
user_info = {
|
511 |
-
"Nickname": nickname,
|
512 |
-
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
|
513 |
}
|
514 |
-
|
515 |
-
# Set user in session
|
516 |
-
self.user_session.set_user(user_info)
|
517 |
-
|
518 |
-
# Generate welcome message
|
519 |
-
welcome_message = self.user_session.get_welcome_message()
|
520 |
-
|
521 |
-
# Create welcome message in the new messages format for Gradio chatbot
|
522 |
-
chat_history = [{"role": "assistant", "content": welcome_message}]
|
523 |
-
|
524 |
-
# Return welcome message and update UI
|
525 |
-
return welcome_message, gr.update(visible=True), gr.update(visible=False), chat_history
|
526 |
|
527 |
-
|
528 |
-
|
529 |
-
|
530 |
-
|
531 |
-
|
532 |
-
|
533 |
-
|
534 |
-
|
535 |
-
# Translate user message to English (from Kinyarwanda by default)
|
536 |
-
try:
|
537 |
-
english_message = self.translator.translate_text(message, "kin_Latn", "eng_Latn")
|
538 |
-
except Exception as e:
|
539 |
-
print(f"Translation error: {e}")
|
540 |
-
english_message = message # Fallback to original message if translation fails
|
541 |
-
|
542 |
-
# Add translated message to history
|
543 |
-
self.user_session.add_to_history("user", english_message)
|
544 |
-
|
545 |
-
# Generate response using RAG chain
|
546 |
-
full_response = ""
|
547 |
-
rag_chain = self.create_rag_chain()
|
548 |
-
|
549 |
-
# Generate chunks of response and update as they come
|
550 |
-
for new_text in rag_chain({"question": english_message}):
|
551 |
-
full_response += new_text
|
552 |
-
|
553 |
-
# Translate response back to user language (Kinyarwanda by default)
|
554 |
-
try:
|
555 |
-
translated_response = self.translator.translate_text(full_response, "eng_Latn", "kin_Latn")
|
556 |
-
except Exception as e:
|
557 |
-
print(f"Translation error: {e}")
|
558 |
-
translated_response = full_response # Fallback to original message if translation fails
|
559 |
-
|
560 |
-
# Update history with current response
|
561 |
-
current_history = history_copy.copy()
|
562 |
-
current_history.append({"role": "assistant", "content": translated_response})
|
563 |
-
yield current_history, ""
|
564 |
-
|
565 |
-
# Add final response to session history
|
566 |
-
self.user_session.add_to_history("assistant", full_response)
|
567 |
-
|
568 |
-
except Exception as e:
|
569 |
-
print(f"Error in chat processing: {e}")
|
570 |
-
# Provide a fallback response if something goes wrong
|
571 |
-
error_history = history.copy()
|
572 |
-
error_history.append({"role": "user", "content": message})
|
573 |
-
error_history.append({
|
574 |
-
"role": "assistant",
|
575 |
-
"content": "I apologize, but I'm having trouble processing your request. Please try again."
|
576 |
-
})
|
577 |
-
yield error_history, ""
|
578 |
|
579 |
-
|
580 |
-
|
581 |
-
|
582 |
-
|
583 |
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621 |
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623 |
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629 |
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|
630 |
)
|
631 |
-
|
632 |
-
|
633 |
-
|
634 |
-
|
635 |
-
|
636 |
-
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-
|
638 |
-
|
639 |
-
|
640 |
-
|
641 |
-
|
642 |
-
|
643 |
-
outputs=[response_message, chatbot_container, registration_container, chatbot]
|
644 |
)
|
645 |
-
|
646 |
-
# Add CSS styles
|
647 |
-
demo.css = """
|
648 |
-
:root {
|
649 |
-
--background: #f0f0f0;
|
650 |
-
--text: #000000;
|
651 |
-
}
|
652 |
|
653 |
-
|
654 |
-
|
655 |
-
padding: 0;
|
656 |
-
width: 100%;
|
657 |
-
height: 100vh;
|
658 |
-
display: flex;
|
659 |
-
flex-direction: column;
|
660 |
-
justify-content: center;
|
661 |
-
align-items: center;
|
662 |
-
background: var(--background);
|
663 |
-
color: var(--text);
|
664 |
-
}
|
665 |
|
666 |
-
|
667 |
-
|
668 |
-
|
669 |
-
|
|
|
|
|
670 |
|
671 |
-
|
672 |
-
|
673 |
-
|
674 |
-
|
675 |
-
|
676 |
-
border: 1px solid rgba(0, 0, 0, 0.1);
|
677 |
-
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
|
678 |
-
}
|
679 |
|
680 |
-
|
681 |
-
|
682 |
-
|
683 |
-
|
684 |
-
|
685 |
-
|
686 |
-
|
687 |
-
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|
688 |
|
689 |
-
|
690 |
-
|
691 |
-
|
692 |
-
|
693 |
|
694 |
-
|
695 |
-
|
696 |
-
|
697 |
-
|
698 |
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|
699 |
-
|
700 |
-
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|
701 |
|
702 |
-
|
703 |
-
|
704 |
-
|
705 |
-
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|
706 |
|
707 |
-
|
708 |
-
|
709 |
-
|
710 |
-
|
711 |
-
|
712 |
-
return demo
|
713 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
714 |
|
715 |
-
|
716 |
-
|
717 |
-
|
718 |
-
|
719 |
-
return [Document(page_content="No specific information available on this topic.", metadata={})]
|
720 |
|
|
|
|
|
|
|
|
|
721 |
|
722 |
-
|
723 |
-
def main():
|
724 |
-
# Initialize the chatbot
|
725 |
-
chatbot = GBVSupportChatbot()
|
726 |
-
|
727 |
-
try:
|
728 |
-
# Load data
|
729 |
-
chatbot.load_data()
|
730 |
-
|
731 |
-
# Create and launch the interface
|
732 |
-
demo = chatbot.create_chatbot_interface()
|
733 |
-
demo.launch(share=True)
|
734 |
-
except Exception as e:
|
735 |
-
print(f"Error in main execution: {e}")
|
736 |
-
|
737 |
|
|
|
738 |
if __name__ == "__main__":
|
739 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
1 |
import os
|
|
|
2 |
import time
|
|
|
|
|
|
|
|
|
|
|
3 |
import pandas as pd
|
4 |
+
import gradio as gr
|
5 |
+
from langchain_groq import ChatGroq
|
|
|
|
|
|
|
|
|
6 |
from langchain_huggingface import HuggingFaceEmbeddings
|
7 |
+
from langchain_community.vectorstores import Chroma
|
8 |
from langchain_core.prompts import PromptTemplate
|
9 |
from langchain_core.output_parsers import StrOutputParser
|
10 |
from langchain_core.runnables import RunnablePassthrough
|
|
|
|
|
11 |
|
12 |
+
import os
|
13 |
+
from langchain_groq import ChatGroq
|
14 |
+
from langchain.prompts import ChatPromptTemplate, PromptTemplate
|
15 |
+
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
|
16 |
+
from urllib.parse import urljoin, urlparse
|
17 |
+
import requests
|
18 |
+
from io import BytesIO
|
19 |
+
from langchain_chroma import Chroma
|
20 |
+
import requests
|
21 |
+
from bs4 import BeautifulSoup
|
22 |
+
from langchain_core.prompts import ChatPromptTemplate
|
23 |
import gradio as gr
|
24 |
+
from PyPDF2 import PdfReader
|
25 |
|
26 |
+
groq_api_key= os.environ.get('grop_API_KEY')
|
|
|
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|
|
|
|
|
|
27 |
|
28 |
+
# Set up embedding model
|
29 |
+
embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
|
30 |
|
31 |
+
# Process data from Drive
|
32 |
+
def process_data_files():
|
33 |
+
folder_path = "./"
|
34 |
+
context_data = []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
35 |
|
36 |
+
# Get list of data files
|
37 |
+
all_files = os.listdir(folder_path)
|
38 |
+
data_files = [f for f in all_files if f.lower().endswith(('.csv', '.xlsx', '.xls'))]
|
|
|
|
|
39 |
|
40 |
+
# Process each file
|
41 |
+
for index, file_name in enumerate(data_files, 1):
|
42 |
+
file_path = os.path.join(folder_path, file_name)
|
43 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
44 |
try:
|
45 |
+
# Read file
|
46 |
+
if file_name.lower().endswith('.csv'):
|
47 |
+
df = pd.read_csv(file_path)
|
48 |
+
else:
|
49 |
+
df = pd.read_excel(file_path)
|
|
|
|
|
|
|
|
|
|
|
|
|
50 |
|
51 |
+
# Check if column 3 exists
|
52 |
+
if df.shape[1] > 2:
|
53 |
+
column_data = df.iloc[:, 2].dropna().astype(str).tolist()
|
54 |
+
|
55 |
+
# Each row becomes one chunk
|
56 |
+
for i, text in enumerate(column_data):
|
57 |
+
context_data.append({"page_content": text, "metadata": {"source": file_name, "row": i+1}})
|
58 |
else:
|
59 |
+
print(f"Warning: File {file_name} has fewer than 3 columns.")
|
60 |
+
|
61 |
except Exception as e:
|
62 |
+
print(f"Error processing file {file_name}: {e}")
|
63 |
+
|
64 |
+
return context_data
|
65 |
|
66 |
+
# Create vectorstore
|
67 |
+
def create_vectorstore(data):
|
68 |
+
# Extract just the text content from each Document object in the list
|
69 |
+
cleaned_texts = [doc["page_content"] for doc in data]
|
70 |
+
metadatas = [doc["metadata"] for doc in data]
|
71 |
|
72 |
+
# Create vector store
|
73 |
+
vectorstore = Chroma(
|
74 |
+
collection_name="GBVRS",
|
75 |
+
embedding_function=embed_model,
|
76 |
+
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
77 |
|
78 |
+
# Add data to vector store
|
79 |
+
vectorstore.add_texts(cleaned_texts, metadatas=metadatas)
|
80 |
+
return vectorstore
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
81 |
|
82 |
+
# User session management
|
83 |
class UserSession:
|
84 |
+
def __init__(self, llm):
|
|
|
|
|
85 |
self.current_user = None
|
86 |
self.welcome_message = None
|
87 |
self.conversation_history = []
|
88 |
self.llm = llm
|
89 |
+
|
90 |
+
def set_user(self, user_info):
|
|
|
91 |
self.current_user = user_info
|
92 |
self.set_welcome_message(user_info.get("Nickname", "Guest"))
|
93 |
# Initialize conversation history with welcome message
|
|
|
95 |
self.conversation_history = [
|
96 |
{"role": "assistant", "content": welcome},
|
97 |
]
|
98 |
+
|
99 |
+
def get_user(self):
|
|
|
100 |
return self.current_user
|
101 |
+
|
102 |
+
def set_welcome_message(self, nickname):
|
103 |
"""Set a dynamic welcome message using the LLM."""
|
104 |
+
# Define a prompt for the LLM to generate a welcome message
|
105 |
prompt = (
|
106 |
+
f"Create a very brief welcome message for {nickname} that fits in 3 lines. "
|
107 |
f"The message should: "
|
108 |
f"1. Welcome {nickname} warmly and professionally. "
|
109 |
f"2. Emphasize that this is a safe and trusted space. "
|
110 |
f"3. Highlight specialized support for gender-based violence (GBV) and legal assistance. "
|
111 |
f"4. Use a tone that is warm, reassuring, and professional. "
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112 |
+
f"5. Keep the message concise and impactful, ensuring it fits within the character limit."
|
113 |
)
|
114 |
+
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115 |
+
# Use the LLM to generate the message
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116 |
+
response = self.llm.invoke(prompt)
|
117 |
+
welcome = response.content
|
118 |
+
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119 |
+
# Format the message with HTML styling
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120 |
+
self.welcome_message = (
|
121 |
+
f"<div style='font-size: 24px; font-weight: bold; color: #2E86C1;'>"
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122 |
+
f"<div style='font-size: 20px;'>"
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123 |
+
f"{welcome}"
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124 |
+
f"</div>"
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125 |
+
)
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126 |
+
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127 |
+
def get_welcome_message(self):
|
128 |
+
return self.welcome_message
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129 |
+
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130 |
+
def add_to_history(self, role, message):
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131 |
+
"""Add a message to the conversation history"""
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132 |
self.conversation_history.append({"role": role, "content": message})
|
133 |
+
|
134 |
+
def get_conversation_history(self):
|
135 |
+
"""Get the full conversation history"""
|
136 |
return self.conversation_history
|
137 |
+
|
138 |
+
def get_formatted_history(self):
|
139 |
+
"""Get conversation history formatted as a string for the LLM"""
|
140 |
formatted_history = ""
|
141 |
for entry in self.conversation_history:
|
142 |
role = "User" if entry["role"] == "user" else "Assistant"
|
143 |
formatted_history += f"{role}: {entry['content']}\n\n"
|
144 |
return formatted_history
|
145 |
|
146 |
+
# Format context from documents
|
147 |
+
def format_context(retrieved_docs):
|
148 |
+
return "\n".join([doc.page_content for doc in retrieved_docs])
|
149 |
|
150 |
+
# RAG Chain creation with updated approach
|
151 |
+
def create_rag_chain(retriever, template, api_key):
|
152 |
+
llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=api_key)
|
153 |
+
rag_prompt = PromptTemplate.from_template(template)
|
154 |
|
155 |
+
# Define the RAG chain using the recommended approach
|
156 |
+
def get_context_and_question(query):
|
157 |
+
# Get user info from the session
|
158 |
+
user_info = user_session.get_user() or {}
|
159 |
+
first_name = user_info.get("Nickname", "User")
|
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|
160 |
|
161 |
+
# Get conversation history
|
162 |
+
conversation_history = user_session.get_formatted_history()
|
163 |
|
164 |
+
# Retrieve documents
|
165 |
+
retrieved_docs = retriever.invoke(query)
|
166 |
+
context_str = format_context(retrieved_docs)
|
167 |
|
168 |
+
# Return the combined inputs for the prompt
|
169 |
+
return {
|
170 |
+
"context": context_str,
|
171 |
+
"question": query,
|
172 |
+
"first_name": first_name,
|
173 |
+
"conversation_history": conversation_history
|
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|
174 |
}
|
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|
175 |
|
176 |
+
# Build the chain
|
177 |
+
rag_chain = (
|
178 |
+
RunnablePassthrough()
|
179 |
+
| get_context_and_question
|
180 |
+
| rag_prompt
|
181 |
+
| llm
|
182 |
+
| StrOutputParser()
|
183 |
+
)
|
|
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|
|
184 |
|
185 |
+
return rag_chain
|
186 |
+
|
187 |
+
# RAG memory function for user interaction (without translation)
|
188 |
+
def rag_memory_stream(message, history):
|
189 |
+
# Add user message to history
|
190 |
+
user_session.add_to_history("user", message)
|
191 |
+
|
192 |
+
# Get response from RAG chain
|
193 |
+
response = rag_chain.invoke(message)
|
194 |
+
|
195 |
+
# Add assistant response to history
|
196 |
+
user_session.add_to_history("assistant", response)
|
197 |
+
|
198 |
+
# Yield the response
|
199 |
+
yield response
|
200 |
+
|
201 |
+
# Add initial message to start the conversation
|
202 |
+
def add_initial_message(chatbot):
|
203 |
+
return chatbot
|
204 |
+
|
205 |
+
# Store user details and handle session
|
206 |
+
def collect_user_info(nickname):
|
207 |
+
if not nickname:
|
208 |
+
return "Nickname is required to proceed.", gr.update(visible=False), gr.update(visible=True), []
|
209 |
+
|
210 |
+
# Store user info for chat session
|
211 |
+
user_info = {
|
212 |
+
"Nickname": nickname,
|
213 |
+
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
|
214 |
+
}
|
215 |
+
|
216 |
+
# Set user in session
|
217 |
+
user_session.set_user(user_info)
|
218 |
+
|
219 |
+
# Generate welcome message
|
220 |
+
welcome_message = user_session.get_welcome_message()
|
221 |
+
|
222 |
+
# Add initial message to start the conversation
|
223 |
+
chat_history = add_initial_message([(None, welcome_message)])
|
224 |
+
|
225 |
+
# Return welcome message and update UI
|
226 |
+
return welcome_message, gr.update(visible=True), gr.update(visible=False), chat_history
|
227 |
+
|
228 |
+
# Gradio Interface Setup with improved UX
|
229 |
+
def chatbot_interface():
|
230 |
+
global template, rag_chain
|
231 |
+
|
232 |
+
template = """
|
233 |
+
**Role**: Compassionate Regal Assistance and GBV Support Specialist with Emotional Awareness.
|
234 |
+
You are a friendly and empathetic chatbot designed to assist users in a conversational and human-like manner. Your goal is to provide accurate, helpful, and emotionally supportive responses based on the provided context: {context}. Follow these guidelines:
|
235 |
+
|
236 |
+
1. **Emotional Awareness**
|
237 |
+
- Acknowledge the user's emotions and respond with empathy.
|
238 |
+
- Use phrases like "I understand how you feel," "That sounds challenging," or "I'm here to support you."
|
239 |
+
- If the user expresses negative emotions, offer comfort and reassurance.
|
240 |
+
|
241 |
+
2. **Contextual Interaction**
|
242 |
+
- Begin with a warm and empathetic welcome message.
|
243 |
+
- Extract precise details from the provided context: {context}.
|
244 |
+
- Respond directly to the user's question: {question}.
|
245 |
+
- Only provide detailed information if user requests it.
|
246 |
+
- Remember the user's name is {first_name}.
|
247 |
+
|
248 |
+
3. **Communication Guidelines**
|
249 |
+
- Maintain a warm, conversational tone (avoid over-familiarity).
|
250 |
+
- Use occasional emojis for engagement (e.g., 😊, 🤗, ❤️).
|
251 |
+
- Provide clear, concise, and emotionally supportive information.
|
252 |
+
|
253 |
+
4. **Response Strategies**
|
254 |
+
- Greet users naturally and ask about their wellbeing (e.g., "Welcome, {first_name}! 😊 How are you feeling today?", "Hello {first_name}! 🤗 What's on your mind?").
|
255 |
+
- Always start with a check-in about the user's wellbeing or current situation.
|
256 |
+
- Provide a concise summary with only relevant information.
|
257 |
+
- Avoid generating content beyond the context.
|
258 |
+
- Handle missing information transparently.
|
259 |
+
|
260 |
+
5. **No Extra Content**
|
261 |
+
- If no information in {context} matches the user's request {question} :
|
262 |
+
* Respond politely: "I don't have that information at the moment, {first_name}. 😊"
|
263 |
+
* Offer alternative assistance options.
|
264 |
+
- Strictly avoid generating unsupported content.
|
265 |
+
- Prevent information padding or speculation.
|
266 |
+
|
267 |
+
6. **Extracting Relevant Links**
|
268 |
+
- If the user asks for a link related to their request `{question}`, extract the most relevant URL from `{context}` and provide it directly.
|
269 |
+
- Example response:
|
270 |
+
- "Here is the link you requested, [URL]"
|
271 |
+
|
272 |
+
7. **Real-Time Awareness**
|
273 |
+
- Acknowledge the current context when appropriate.
|
274 |
+
- Stay focused on the user's immediate needs.
|
275 |
+
|
276 |
+
8. **Previous Conversation Context**
|
277 |
+
- Consider the conversation history: {conversation_history}
|
278 |
+
- Maintain continuity with previous exchanges.
|
279 |
+
|
280 |
+
**Context:** {context}
|
281 |
+
**User's Question:** {question}
|
282 |
+
**Your Response:**
|
283 |
+
"""
|
284 |
+
|
285 |
+
with gr.Blocks() as demo:
|
286 |
+
# User registration section
|
287 |
+
with gr.Column(visible=True, elem_id="registration_container") as registration_container:
|
288 |
+
gr.Markdown("### Your privacy is our concern, please provide your nickname.")
|
289 |
+
|
290 |
+
with gr.Row():
|
291 |
+
first_name = gr.Textbox(
|
292 |
+
label="Nickname",
|
293 |
+
placeholder="Enter your Nickname",
|
294 |
+
scale=1,
|
295 |
+
elem_id="input_nickname"
|
296 |
)
|
297 |
+
|
298 |
+
with gr.Row():
|
299 |
+
submit_btn = gr.Button("Start Chatting", variant="primary", scale=2)
|
300 |
+
|
301 |
+
response_message = gr.Markdown()
|
302 |
+
|
303 |
+
# Chatbot section (initially hidden)
|
304 |
+
with gr.Column(visible=False, elem_id="chatbot_container") as chatbot_container:
|
305 |
+
chat_interface = gr.ChatInterface(
|
306 |
+
fn=rag_memory_stream,
|
307 |
+
title="Chat with GBVR",
|
308 |
+
fill_height=True
|
|
|
309 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
310 |
|
311 |
+
# Footer with version info
|
312 |
+
gr.Markdown("Ijwi ry'Ubufasha v1.0.0 © 2025")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
313 |
|
314 |
+
# Handle user registration
|
315 |
+
submit_btn.click(
|
316 |
+
collect_user_info,
|
317 |
+
inputs=[first_name],
|
318 |
+
outputs=[response_message, chatbot_container, registration_container, chat_interface.chatbot]
|
319 |
+
)
|
320 |
|
321 |
+
demo.css = """
|
322 |
+
:root {
|
323 |
+
--background: #f0f0f0;
|
324 |
+
--text: #000000;
|
325 |
+
}
|
|
|
|
|
|
|
326 |
|
327 |
+
body, .gradio-container {
|
328 |
+
margin: 0;
|
329 |
+
padding: 0;
|
330 |
+
width: 100vw;
|
331 |
+
height: 100vh;
|
332 |
+
display: flex;
|
333 |
+
flex-direction: column;
|
334 |
+
justify-content: center;
|
335 |
+
align-items: center;
|
336 |
+
background: var(--background);
|
337 |
+
color: var(--text);
|
338 |
+
}
|
339 |
|
340 |
+
.gradio-container {
|
341 |
+
max-width: 100%;
|
342 |
+
max-height: 100%;
|
343 |
+
}
|
344 |
|
345 |
+
.gr-box {
|
346 |
+
background: var(--background);
|
347 |
+
color: var(--text);
|
348 |
+
border-radius: 12px;
|
349 |
+
padding: 2rem;
|
350 |
+
border: 1px solid rgba(0, 0, 0, 0.1);
|
351 |
+
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
|
352 |
+
}
|
353 |
|
354 |
+
.gr-button-primary {
|
355 |
+
background: var(--background);
|
356 |
+
color: var(--text);
|
357 |
+
padding: 12px 24px;
|
358 |
+
border-radius: 8px;
|
359 |
+
transition: all 0.3s ease;
|
360 |
+
border: 1px solid rgba(0, 0, 0, 0.1);
|
361 |
+
}
|
362 |
|
363 |
+
.gr-button-primary:hover {
|
364 |
+
transform: translateY(-1px);
|
365 |
+
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
|
366 |
+
}
|
|
|
|
|
367 |
|
368 |
+
footer {
|
369 |
+
text-align: center;
|
370 |
+
color: var(--text);
|
371 |
+
opacity: 0.7;
|
372 |
+
padding: 1rem;
|
373 |
+
font-size: 0.9em;
|
374 |
+
}
|
375 |
|
376 |
+
.gr-markdown h3 {
|
377 |
+
color: var(--text);
|
378 |
+
margin-bottom: 1rem;
|
379 |
+
}
|
|
|
380 |
|
381 |
+
.registration-markdown, .chat-title h1 {
|
382 |
+
color: var(--text);
|
383 |
+
}
|
384 |
+
"""
|
385 |
|
386 |
+
return demo
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
387 |
|
388 |
+
# Main execution
|
389 |
if __name__ == "__main__":
|
390 |
+
# Process data and create vectorstore
|
391 |
+
data = process_data_files()
|
392 |
+
vectorstore = create_vectorstore(data)
|
393 |
+
retriever = vectorstore.as_retriever()
|
394 |
+
|
395 |
+
# Initialize LLM for the user session
|
396 |
+
llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_api_key)
|
397 |
+
user_session = UserSession(llm)
|
398 |
+
|
399 |
+
# Create RAG chain with the new approach
|
400 |
+
rag_chain = create_rag_chain(retriever, template, groq_api_key)
|
401 |
+
|
402 |
+
# Launch the interface
|
403 |
+
chatbot_interface().launch(share=True)
|