Upload 3 files
Browse files- .streamlit/config.toml +9 -5
- streamapp.py +67 -17
- test-logo.png +0 -0
.streamlit/config.toml
CHANGED
@@ -9,10 +9,14 @@
|
|
9 |
#secondaryBackgroundColor = '#55B2FF' # Lighter Blue
|
10 |
|
11 |
|
12 |
-
primaryColor="#ff4b4b"
|
13 |
-
backgroundColor="#00325B"
|
14 |
-
secondaryBackgroundColor="#262730"
|
15 |
-
textColor="#fafafa"
|
16 |
-
font="monospace"
|
17 |
|
18 |
|
|
|
|
|
|
|
|
|
|
9 |
#secondaryBackgroundColor = '#55B2FF' # Lighter Blue
|
10 |
|
11 |
|
12 |
+
#primaryColor="#ff4b4b"
|
13 |
+
#backgroundColor="#00325B"
|
14 |
+
#secondaryBackgroundColor="#262730"
|
15 |
+
#textColor="#fafafa"
|
16 |
+
#font="monospace"
|
17 |
|
18 |
|
19 |
+
|
20 |
+
base="light"
|
21 |
+
primaryColor="#efa729"
|
22 |
+
textColor="#3a0aa6"
|
streamapp.py
CHANGED
@@ -32,8 +32,15 @@ import pandas as pd
|
|
32 |
# from sklearn import datasets
|
33 |
# from sklearn.ensemble import RandomForestClassifier
|
34 |
|
|
|
|
|
|
|
35 |
global trace_df
|
36 |
|
|
|
|
|
|
|
|
|
37 |
@st.cache_resource
|
38 |
def tracer_config():
|
39 |
#phoenix setup
|
@@ -42,15 +49,17 @@ def tracer_config():
|
|
42 |
tracer = OpenInferenceTracer()
|
43 |
# If no tracer is specified, a tracer is constructed for you
|
44 |
LangChainInstrumentor(tracer).instrument()
|
45 |
-
time.sleep(
|
46 |
print(session.url)
|
47 |
|
48 |
tracer_config()
|
49 |
|
50 |
-
tab1, tab2 = st.tabs(["π RAG", "π FactVsHallucinate" ])
|
51 |
|
52 |
|
53 |
|
|
|
|
|
|
|
54 |
|
55 |
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
|
56 |
|
@@ -156,11 +165,21 @@ def _load_docs(path: str):
|
|
156 |
|
157 |
|
158 |
def rag_response(response):
|
159 |
-
st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
|
160 |
-
|
161 |
-
st.
|
162 |
-
|
163 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
164 |
|
165 |
#st.button("Check Hallucination")
|
166 |
|
@@ -182,7 +201,8 @@ def hallu_eval(question: str, answer: str, context: str):
|
|
182 |
}
|
183 |
)
|
184 |
print("got hallu score")
|
185 |
-
st.
|
|
|
186 |
#return {"hallucination_score": hallucination_score}
|
187 |
#time.sleep(10)
|
188 |
|
@@ -215,12 +235,32 @@ with tab1:
|
|
215 |
|
216 |
#print("lenght in tab1, ", len(vectorstore.serialize_to_bytes()))
|
217 |
options = ["true", "false"]
|
218 |
-
|
219 |
-
|
220 |
-
|
221 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
222 |
|
223 |
-
if st.form_submit_button("RAG with evaluation"):
|
224 |
print("retrie ,", retriever)
|
225 |
chain = RetrievalQA.from_chain_type(
|
226 |
llm=llm,
|
@@ -260,9 +300,9 @@ with tab2:
|
|
260 |
|
261 |
|
262 |
#print("lenght in tab2, ", len(vectorstore.serialize_to_bytes()))
|
263 |
-
question = st.text_input(label="
|
264 |
-
answer = st.text_input(label="answer", value="", label_visibility="visible", disabled=False)
|
265 |
-
context = st.text_input(label="context", value="", label_visibility="visible", disabled=False)
|
266 |
|
267 |
|
268 |
if st.form_submit_button("Evaluate"):
|
@@ -347,4 +387,14 @@ def rag():
|
|
347 |
#st.write("Doing more optional stuff")
|
348 |
|
349 |
|
350 |
-
return(response)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
32 |
# from sklearn import datasets
|
33 |
# from sklearn.ensemble import RandomForestClassifier
|
34 |
|
35 |
+
from PIL import Image
|
36 |
+
|
37 |
+
|
38 |
global trace_df
|
39 |
|
40 |
+
# Page config
|
41 |
+
st.set_page_config(page_title="RAG PoC", layout="wide")
|
42 |
+
st.sidebar.image(Image.open("./test-logo.png"), use_column_width=True)
|
43 |
+
|
44 |
@st.cache_resource
|
45 |
def tracer_config():
|
46 |
#phoenix setup
|
|
|
49 |
tracer = OpenInferenceTracer()
|
50 |
# If no tracer is specified, a tracer is constructed for you
|
51 |
LangChainInstrumentor(tracer).instrument()
|
52 |
+
time.sleep(3)
|
53 |
print(session.url)
|
54 |
|
55 |
tracer_config()
|
56 |
|
|
|
57 |
|
58 |
|
59 |
|
60 |
+
tab1, tab2 = st.tabs(["π **RAG**", "π FactVsHallucinate" ])
|
61 |
+
|
62 |
+
|
63 |
|
64 |
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
|
65 |
|
|
|
165 |
|
166 |
|
167 |
def rag_response(response):
|
168 |
+
#st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
|
169 |
+
|
170 |
+
#st.markdown(".stTextInput > label {font-size:105%; font-weight:bold; color:blue;} ",unsafe_allow_html=True) #for all text-input label sections
|
171 |
+
|
172 |
+
question_title = '<h1 style="color:#33ff33;font-size:24px;">Question</h1>'
|
173 |
+
|
174 |
+
|
175 |
+
|
176 |
+
st.markdown('<h1 style="color:#100170;font-size:48px;text-align:center;">RAG Response</h1>', unsafe_allow_html=True)
|
177 |
+
st.markdown('<h1 style="color:#100170;font-size:24px;">Question</h1>', unsafe_allow_html=True)
|
178 |
+
st.text_area(label="", value=response["query"], height=30)
|
179 |
+
st.markdown('<h1 style="color:#100170;font-size:24px;">RAG Output</h1>', unsafe_allow_html=True)
|
180 |
+
st.text_area(label="", value=response["result"])
|
181 |
+
st.markdown('<h1 style="color:#100170;font-size:24px;">Augmented knowledge</h1>', unsafe_allow_html=True)
|
182 |
+
st.text_area(label="", value=response["source_documents"])
|
183 |
|
184 |
#st.button("Check Hallucination")
|
185 |
|
|
|
201 |
}
|
202 |
)
|
203 |
print("got hallu score")
|
204 |
+
st.markdown('<h1 style="color:#100170;font-size:24px;">Hallucinated?</h1>', unsafe_allow_html=True)
|
205 |
+
st.text_area(label=" ", value=hallucination_score, height=30)
|
206 |
#return {"hallucination_score": hallucination_score}
|
207 |
#time.sleep(10)
|
208 |
|
|
|
235 |
|
236 |
#print("lenght in tab1, ", len(vectorstore.serialize_to_bytes()))
|
237 |
options = ["true", "false"]
|
238 |
+
|
239 |
+
st.markdown('<h1 style="color:#100170;font-size:24px;">User Query</h1>', unsafe_allow_html=True)
|
240 |
+
|
241 |
+
question = st.text_input(label="", value="", placeholder="Type in question",label_visibility="visible", disabled=False)
|
242 |
+
#st.markdown('<h2 style="color:#3a0aa6;font-size:24px;">Evaluation</h2>', unsafe_allow_html=True)
|
243 |
+
evaluate = st.selectbox(label="***Perform Evaluation?***",options=options, index=1, placeholder="Choose an option", disabled=False, label_visibility="visible")
|
244 |
+
|
245 |
+
m = st.markdown("""
|
246 |
+
<style>
|
247 |
+
div.stButton > button:first-child {
|
248 |
+
background-color: #100170;
|
249 |
+
color:#ffffff;
|
250 |
+
}
|
251 |
+
div.stButton > button:hover {
|
252 |
+
background-color: #00ff00;
|
253 |
+
color:#ff0000;
|
254 |
+
}
|
255 |
+
</style>""", unsafe_allow_html=True)
|
256 |
+
|
257 |
+
#st.markdown("----", unsafe_allow_html=True)
|
258 |
+
columns = st.columns([2,1,2])
|
259 |
+
|
260 |
+
if columns[1].form_submit_button(" Start RAG "):
|
261 |
+
|
262 |
+
st.markdown("""<hr style="height:10px;border:none;color:#333;background-color: #100170;" /> """, unsafe_allow_html=True)
|
263 |
|
|
|
264 |
print("retrie ,", retriever)
|
265 |
chain = RetrievalQA.from_chain_type(
|
266 |
llm=llm,
|
|
|
300 |
|
301 |
|
302 |
#print("lenght in tab2, ", len(vectorstore.serialize_to_bytes()))
|
303 |
+
question = st.text_input(label="**Question**", value="", label_visibility="visible", disabled=False)
|
304 |
+
answer = st.text_input(label="**answer**", value="", label_visibility="visible", disabled=False)
|
305 |
+
context = st.text_input(label="**context**", value="", label_visibility="visible", disabled=False)
|
306 |
|
307 |
|
308 |
if st.form_submit_button("Evaluate"):
|
|
|
387 |
#st.write("Doing more optional stuff")
|
388 |
|
389 |
|
390 |
+
return(response)
|
391 |
+
|
392 |
+
|
393 |
+
a = st.markdown("""
|
394 |
+
<style>
|
395 |
+
div.stTextArea > textarea {
|
396 |
+
background-color: #0099ff;
|
397 |
+
height: 1400px;
|
398 |
+
width: 800px;
|
399 |
+
}
|
400 |
+
</style>""", unsafe_allow_html=True)
|
test-logo.png
ADDED