Upload 3 files
Browse files- .streamlit/config.toml +9 -5
- streamapp.py +67 -17
- test-logo.png +0 -0
.streamlit/config.toml
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@@ -9,10 +9,14 @@
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#secondaryBackgroundColor = '#55B2FF' # Lighter Blue
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primaryColor="#ff4b4b"
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backgroundColor="#00325B"
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secondaryBackgroundColor="#262730"
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textColor="#fafafa"
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font="monospace"
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#secondaryBackgroundColor = '#55B2FF' # Lighter Blue
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#primaryColor="#ff4b4b"
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#backgroundColor="#00325B"
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#secondaryBackgroundColor="#262730"
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#textColor="#fafafa"
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#font="monospace"
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base="light"
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primaryColor="#efa729"
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textColor="#3a0aa6"
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streamapp.py
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@@ -32,8 +32,15 @@ import pandas as pd
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# from sklearn import datasets
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# from sklearn.ensemble import RandomForestClassifier
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global trace_df
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@st.cache_resource
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def tracer_config():
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#phoenix setup
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@@ -42,15 +49,17 @@ def tracer_config():
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tracer = OpenInferenceTracer()
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# If no tracer is specified, a tracer is constructed for you
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LangChainInstrumentor(tracer).instrument()
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time.sleep(
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print(session.url)
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tracer_config()
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tab1, tab2 = st.tabs(["π RAG", "π FactVsHallucinate" ])
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os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
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def rag_response(response):
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st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
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st.
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#st.button("Check Hallucination")
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@@ -182,7 +201,8 @@ def hallu_eval(question: str, answer: str, context: str):
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}
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print("got hallu score")
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st.
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#return {"hallucination_score": hallucination_score}
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#time.sleep(10)
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@@ -215,12 +235,32 @@ with tab1:
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#print("lenght in tab1, ", len(vectorstore.serialize_to_bytes()))
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options = ["true", "false"]
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if st.form_submit_button("RAG with evaluation"):
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print("retrie ,", retriever)
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chain = RetrievalQA.from_chain_type(
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llm=llm,
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@@ -260,9 +300,9 @@ with tab2:
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#print("lenght in tab2, ", len(vectorstore.serialize_to_bytes()))
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question = st.text_input(label="
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answer = st.text_input(label="answer", value="", label_visibility="visible", disabled=False)
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context = st.text_input(label="context", value="", label_visibility="visible", disabled=False)
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if st.form_submit_button("Evaluate"):
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@@ -347,4 +387,14 @@ def rag():
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#st.write("Doing more optional stuff")
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return(response)
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# from sklearn import datasets
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# from sklearn.ensemble import RandomForestClassifier
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from PIL import Image
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global trace_df
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# Page config
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st.set_page_config(page_title="RAG PoC", layout="wide")
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st.sidebar.image(Image.open("./test-logo.png"), use_column_width=True)
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@st.cache_resource
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def tracer_config():
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#phoenix setup
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tracer = OpenInferenceTracer()
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# If no tracer is specified, a tracer is constructed for you
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LangChainInstrumentor(tracer).instrument()
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time.sleep(3)
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print(session.url)
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tracer_config()
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tab1, tab2 = st.tabs(["π **RAG**", "π FactVsHallucinate" ])
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os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
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def rag_response(response):
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#st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
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#st.markdown(".stTextInput > label {font-size:105%; font-weight:bold; color:blue;} ",unsafe_allow_html=True) #for all text-input label sections
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question_title = '<h1 style="color:#33ff33;font-size:24px;">Question</h1>'
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st.markdown('<h1 style="color:#100170;font-size:48px;text-align:center;">RAG Response</h1>', unsafe_allow_html=True)
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st.markdown('<h1 style="color:#100170;font-size:24px;">Question</h1>', unsafe_allow_html=True)
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st.text_area(label="", value=response["query"], height=30)
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st.markdown('<h1 style="color:#100170;font-size:24px;">RAG Output</h1>', unsafe_allow_html=True)
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st.text_area(label="", value=response["result"])
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st.markdown('<h1 style="color:#100170;font-size:24px;">Augmented knowledge</h1>', unsafe_allow_html=True)
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st.text_area(label="", value=response["source_documents"])
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#st.button("Check Hallucination")
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}
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)
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print("got hallu score")
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st.markdown('<h1 style="color:#100170;font-size:24px;">Hallucinated?</h1>', unsafe_allow_html=True)
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st.text_area(label=" ", value=hallucination_score, height=30)
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#return {"hallucination_score": hallucination_score}
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#time.sleep(10)
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#print("lenght in tab1, ", len(vectorstore.serialize_to_bytes()))
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options = ["true", "false"]
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st.markdown('<h1 style="color:#100170;font-size:24px;">User Query</h1>', unsafe_allow_html=True)
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question = st.text_input(label="", value="", placeholder="Type in question",label_visibility="visible", disabled=False)
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#st.markdown('<h2 style="color:#3a0aa6;font-size:24px;">Evaluation</h2>', unsafe_allow_html=True)
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evaluate = st.selectbox(label="***Perform Evaluation?***",options=options, index=1, placeholder="Choose an option", disabled=False, label_visibility="visible")
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m = st.markdown("""
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<style>
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div.stButton > button:first-child {
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background-color: #100170;
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color:#ffffff;
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}
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div.stButton > button:hover {
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background-color: #00ff00;
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color:#ff0000;
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}
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</style>""", unsafe_allow_html=True)
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#st.markdown("----", unsafe_allow_html=True)
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columns = st.columns([2,1,2])
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if columns[1].form_submit_button(" Start RAG "):
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st.markdown("""<hr style="height:10px;border:none;color:#333;background-color: #100170;" /> """, unsafe_allow_html=True)
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print("retrie ,", retriever)
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chain = RetrievalQA.from_chain_type(
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llm=llm,
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#print("lenght in tab2, ", len(vectorstore.serialize_to_bytes()))
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question = st.text_input(label="**Question**", value="", label_visibility="visible", disabled=False)
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answer = st.text_input(label="**answer**", value="", label_visibility="visible", disabled=False)
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context = st.text_input(label="**context**", value="", label_visibility="visible", disabled=False)
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if st.form_submit_button("Evaluate"):
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#st.write("Doing more optional stuff")
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return(response)
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a = st.markdown("""
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<style>
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div.stTextArea > textarea {
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background-color: #0099ff;
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height: 1400px;
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width: 800px;
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}
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</style>""", unsafe_allow_html=True)
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test-logo.png
ADDED
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