Spaces:
Runtime error
Runtime error
eaglelandsonce
commited on
Commit
•
1947481
1
Parent(s):
61817f9
Update app.py
Browse files
app.py
CHANGED
@@ -129,42 +129,66 @@ def vectara_query(query: str, config: dict):
|
|
129 |
res = [[r['text'], r['score']] for r in responses]
|
130 |
return res, summary
|
131 |
|
132 |
-
# Streamlit UI setup
|
133 |
-
st.title("Vectara Content Query Interface")
|
134 |
-
|
135 |
-
# User inputs
|
136 |
-
query = st.text_input("Enter your query here", "")
|
137 |
-
lambda_val = st.slider("Lambda Value", min_value=0.0, max_value=1.0, value=0.5)
|
138 |
-
top_k = st.number_input("Top K Results", min_value=1, max_value=50, value=10)
|
139 |
-
|
140 |
-
if st.button("Query Vectara"):
|
141 |
-
config = {
|
142 |
-
|
143 |
-
"api_key": os.environ.get("VECTARA_API_KEY", ""),
|
144 |
-
"customer_id": os.environ.get("VECTARA_CUSTOMER_ID", ""),
|
145 |
-
"corpus_id": os.environ.get("VECTARA_CORPUS_ID", ""),
|
146 |
-
|
147 |
-
"lambda_val": lambda_val,
|
148 |
-
"top_k": top_k,
|
149 |
-
}
|
150 |
-
|
151 |
-
results, summary = vectara_query(query, config)
|
152 |
-
|
153 |
-
if results:
|
154 |
-
st.subheader("Summary")
|
155 |
-
st.write(summary)
|
156 |
-
|
157 |
-
st.subheader("Top Results")
|
158 |
-
|
159 |
-
# Extract texts from results
|
160 |
-
texts = [r[0] for r in results[:5]]
|
161 |
-
|
162 |
-
# Compute HHEM scores
|
163 |
-
scores = compute_hhem_scores(texts, summary)
|
164 |
-
|
165 |
-
# Prepare and display the dataframe
|
166 |
-
df = pd.DataFrame({'Fact': texts, 'HHEM Score': scores})
|
167 |
-
st.dataframe(df)
|
168 |
-
else:
|
169 |
-
st.write("No results found.")
|
170 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
129 |
res = [[r['text'], r['score']] for r in responses]
|
130 |
return res, summary
|
131 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
132 |
|
133 |
+
# Create the main app with three tabs
|
134 |
+
tab1, tab2, tab3 = st.tabs(["Synthetic Data", "Data Query", "HHEM-Victara Query Tuner"])
|
135 |
+
|
136 |
+
with tab1:
|
137 |
+
st.header("Synthetic Data")
|
138 |
+
# Placeholder for Synthetic Data functionality
|
139 |
+
st.write("Here you can generate or manage synthetic data.")
|
140 |
+
|
141 |
+
with tab2:
|
142 |
+
st.header("Data Query")
|
143 |
+
# Placeholder for Data Query functionality
|
144 |
+
st.write("Here you can perform data queries.")
|
145 |
+
# Example of a simple query input
|
146 |
+
query_input = st.text_input("Enter your query here")
|
147 |
+
if st.button("Execute Query"):
|
148 |
+
# Placeholder for query execution logic
|
149 |
+
st.write(f"Executing query: {query_input}")
|
150 |
+
|
151 |
+
with tab3:
|
152 |
+
st.header("HHEM-Victara Query Tuner")
|
153 |
+
|
154 |
+
|
155 |
+
# Streamlit UI setup
|
156 |
+
st.title("HHEM-Vectara Query Tuning")
|
157 |
+
|
158 |
+
# User inputs
|
159 |
+
query = st.text_input("Enter your query here", "")
|
160 |
+
lambda_val = st.slider("Lambda Value", min_value=0.0, max_value=1.0, value=0.5)
|
161 |
+
top_k = st.number_input("Top K Results", min_value=1, max_value=50, value=10)
|
162 |
+
|
163 |
+
|
164 |
+
if st.button("Query Vectara"):
|
165 |
+
config = {
|
166 |
+
|
167 |
+
"api_key": os.environ.get("VECTARA_API_KEY", ""),
|
168 |
+
"customer_id": os.environ.get("VECTARA_CUSTOMER_ID", ""),
|
169 |
+
"corpus_id": os.environ.get("VECTARA_CORPUS_ID", ""),
|
170 |
+
|
171 |
+
"lambda_val": lambda_val,
|
172 |
+
"top_k": top_k,
|
173 |
+
}
|
174 |
+
|
175 |
+
results, summary = vectara_query(query, config)
|
176 |
+
|
177 |
+
if results:
|
178 |
+
st.subheader("Summary")
|
179 |
+
st.write(summary)
|
180 |
+
|
181 |
+
st.subheader("Top Results")
|
182 |
+
|
183 |
+
# Extract texts from results
|
184 |
+
texts = [r[0] for r in results[:5]]
|
185 |
+
|
186 |
+
# Compute HHEM scores
|
187 |
+
scores = compute_hhem_scores(texts, summary)
|
188 |
+
|
189 |
+
# Prepare and display the dataframe
|
190 |
+
df = pd.DataFrame({'Fact': texts, 'HHEM Score': scores})
|
191 |
+
st.dataframe(df)
|
192 |
+
else:
|
193 |
+
st.write("No results found.")
|
194 |
+
|