Update app.py

app.py

@@ -97,96 +97,3 @@ if st.button("Query Vectara"):
     else:
         st.write("No results found.")
 
-
-
-
-
-"""
-import streamlit as st
-import requests
-import json
-import os
-import pandas as pd
-
-# Assuming the environment variables are already set, we directly use them.
-# However, in a Streamlit app, you might want to set them up within the script for demonstration purposes
-# or securely use secrets management for API keys and other sensitive information.
-
-# Define the Vectara query function
-def vectara_query(query: str, config: dict):
-# Query Vectara and return the results.
-    corpus_key = [{
-        "customerId": config["customer_id"],
-        "corpusId": config["corpus_id"],
-        "lexicalInterpolationConfig": {"lambda": config.get("lambda_val", 0.5)},
-    }]
-    data = {
-        "query": [{
-            "query": query,
-            "start": 0,
-            "numResults": config.get("top_k", 10),
-            "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:
-        st.error(f"Query failed (code {response.status_code}, reason {response.reason}, details {response.text})")
-        return [], ""
-
-    result = response.json()
-    responses = result["responseSet"][0]["response"]
-    summary = result["responseSet"][0]["summary"][0]["text"]
-
-    res = [[r['text'], r['score']] for r in responses]
-    return res, summary
-
-# Streamlit interface
-st.title("Vectara Content Query Interface")
-
-# User inputs
-query = st.text_input("Enter your query here", "What does Vectara do?")
-lambda_val = st.slider("Lambda Value", min_value=0.0, max_value=1.0, value=0.5)
-top_k = st.number_input("Top K Results", min_value=1, max_value=50, value=10)
-
-if st.button("Query Vectara"):
-    config = {
-        "api_key": os.environ.get("VECTARA_API_KEY", ""),
-        "customer_id": os.environ.get("VECTARA_CUSTOMER_ID", ""),
-        "corpus_id": os.environ.get("VECTARA_CORPUS_ID", ""),
-        "lambda_val": lambda_val,
-        "top_k": top_k,
-    }
-
-    results, summary = vectara_query(query, config)
-
-    if results:
-        st.subheader("Summary")
-        st.write(summary)
-        
-        st.subheader("Top Results")
-        df = pd.DataFrame(results, columns=['Text', 'Score'])
-        st.dataframe(df)
-    else:
-        st.write("No results found.")
-
-# Note: The integration of the model for HHEM scores is omitted as it requires the specific model details and implementation.
-
-"""