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| import streamlit as st | |
| import requests | |
| secret_key = st.secrets["secret_key"] | |
| def call_api(url, keyword, wl_key, description_narrative): | |
| api_url = "https://wl-quality-rating.eastus2.inference.ml.azure.com/score" | |
| payload = { | |
| "url": url, | |
| "keyword": keyword, | |
| "wl_key": wl_key, | |
| "description_narrative": description_narrative | |
| } | |
| headers = { | |
| "Content-Type": "application/json", | |
| "User-Agent": "insomnia/8.2.0", | |
| "Authorization": "Bearer " + secret_key | |
| } | |
| response = requests.request("POST", api_url, json=payload, headers=headers) | |
| return response.json() # assuming API responds with JSON | |
| # User inputs | |
| url = st.text_input("Enter the URL of the webpage:") | |
| query = st.text_input("Enter the query the content aims at ranking for:") | |
| narrative = st.text_area("Enter the descriptive narrative of the searcher:") | |
| wordlift_key = st.text_input("Enter the WordLift Key:") | |
| # Button to execute analysis | |
| if st.button("Analyze"): | |
| if url and query and narrative and wordlift_key: | |
| response = call_api(url, query, wordlift_key, narrative) | |
| # Display JSON response | |
| st.json(response) | |
| # Additional visual representation & analysis based on API response. | |
| # You will need to adjust this part based on the actual structure of the API response. | |
| try: | |
| M = response["analyze"][0]["M"] | |
| T = response["analyze"][0]["T"] | |
| # Display traffic light system | |
| if M == 2 and T == 2: | |
| st.write("🟢 Content is highly relevant and trustworthy") | |
| elif M == 1 or T == 1: | |
| st.write("🟡 Content is partly relevant/helpful") | |
| else: | |
| st.write("🔴 Content is not relevant") | |
| except KeyError: | |
| st.error("Invalid API response format. Unable to extract M and T values.") | |
| else: | |
| st.warning("Please provide all inputs!") | |