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| import os | |
| import json | |
| import requests | |
| import streamlit as st | |
| import pandas as pd | |
| from sentence_transformers import CrossEncoder | |
| import numpy as np | |
| st.image("demo.jpeg") | |
| np.set_printoptions(suppress=True, precision=4) | |
| model = CrossEncoder('vectara/hallucination_evaluation_model') | |
| pd.set_option('display.width', 100) | |
| pd.set_option('display.max_colwidth', None) # Use None to display full content without truncation | |
| def vectara_query(query: str, config: dict) -> None: | |
| corpus_key = [ | |
| { | |
| "customerId": config["customer_id"], | |
| "corpusId": config["corpus_id"], | |
| "lexicalInterpolationConfig": {"lambda": config["lambda_val"]}, | |
| } | |
| ] | |
| data = { | |
| "query": [ | |
| { | |
| "query": query, | |
| "start": 0, | |
| "numResults": config["top_k"], | |
| "contextConfig": { | |
| "sentencesBefore": 2, | |
| "sentencesAfter": 2, | |
| }, | |
| "corpusKey": corpus_key, | |
| "summary": [ | |
| { | |
| "responseLang": "eng", | |
| "maxSummarizedResults": 5, | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| headers = { | |
| "x-api-key": config["api_key"], | |
| "customer-id": config["customer_id"], | |
| "Content-Type": "application/json", | |
| } | |
| response = requests.post( | |
| headers=headers, | |
| url="https://api.vectara.io/v1/query", | |
| data=json.dumps(data), | |
| ) | |
| if response.status_code != 200: | |
| print( | |
| "Query failed %s", | |
| f"(code {response.status_code}, reason {response.reason}, details " | |
| f"{response.text})", | |
| ) | |
| return [] | |
| result = response.json() | |
| responses = result["responseSet"][0]["response"] | |
| documents = result["responseSet"][0]["document"] | |
| summary = result["responseSet"][0]["summary"][0]["text"] | |
| res = [[r['text'], r['score']] for r in responses] | |
| return res, summary | |
| # Set the environment variables | |
| os.environ['VECTARA_API_KEY'] = 'zwt_MD0gpPStP7DARQICFDZ4XIolYlRvi7qYm61HcA' | |
| os.environ['VECTARA_CORPUS_ID'] = '5' | |
| os.environ['VECTARA_CUSTOMER_ID'] = '809312420' | |
| # Load config from environment variables | |
| api_key = os.environ.get("VECTARA_API_KEY", "") | |
| customer_id = os.environ.get("VECTARA_CUSTOMER_ID", "") | |
| corpus_id = os.environ.get("VECTARA_CORPUS_ID", "") | |
| config = { | |
| "api_key": str(api_key), | |
| "customer_id": str(customer_id), | |
| "corpus_id": str(corpus_id), | |
| "lambda_val": 0.025, | |
| "top_k": 10, | |
| } | |
| # Streamlit app | |
| st.title("KitchenCreators App") | |
| # Input for the query | |
| query = st.text_input("Enter your query:", "What does Kitchen Creators do?") | |
| # Button to trigger the query | |
| if st.button("Run Query"): | |
| results, summary = vectara_query(query, config) | |
| # Display summary | |
| st.header("Summary") | |
| st.write(summary) | |
| # Additional processing | |
| st.header("Additional Processing") | |
| # Get texts and scores | |
| texts = [r[0] for r in results[:5]] | |
| scores = [model.predict([text, summary]) for text in texts] | |
| # Create DataFrame | |
| df = pd.DataFrame({'fact': texts, 'HHEM score': scores}) | |
| # Display DataFrame | |
| st.write(df) | |