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Index.py

@@ -66,7 +66,6 @@ handler =  StdOutCallbackHandler()
 vectorstore = None
 retriever = None
 
-print("Hi There!")
 
 def initialize_vectorstore():
 

streamapp.py

@@ -32,18 +32,32 @@ import pandas as pd
 # from sklearn import datasets
 # from sklearn.ensemble import RandomForestClassifier
 
-#phoenix setup
-session = px.launch_app()
-# If no exporter is specified, the tracer will export to the locally running Phoenix server
-tracer = OpenInferenceTracer()
-# If no tracer is specified, a tracer is constructed for you
-LangChainInstrumentor(tracer).instrument()
-print(session.url)
+from PIL import Image
 
 
+global trace_df
 
-tab1, tab2, tab3 = st.tabs(["📈 RAG", "🗃 FactVsHallucinate" , "trace"])
+# Page config
+st.set_page_config(page_title="RAG PoC", layout="wide")
+st.sidebar.image(Image.open("./test-logo.png"), use_column_width=True)
 
+@st.cache_resource
+def tracer_config():
+    #phoenix setup
+    session = px.launch_app()
+    # If no exporter is specified, the tracer will export to the locally running Phoenix server
+    tracer = OpenInferenceTracer()
+    # If no tracer is specified, a tracer is constructed for you
+    LangChainInstrumentor(tracer).instrument()
+    time.sleep(3)
+    print(session.url)
+
+tracer_config()
+
+
+
+
+tab1, tab2 = st.tabs(["📈 **RAG**", "🗃 FactVsHallucinate" ])
 
 
 
@@ -151,11 +165,21 @@ def _load_docs(path: str):
 
 
 def rag_response(response):
-    st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
-    st.subheader('RAG response')  
-    st.text_area(label="user query", value=response["query"], height=30)
-    st.text_area(label="RAG output", value=response["result"])
-    st.text_area(label="Augmented knowledge", value=response["source_documents"])
+    #st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)
+
+    #st.markdown(".stTextInput > label {font-size:105%; font-weight:bold; color:blue;} ",unsafe_allow_html=True) #for all text-input label sections
+    
+    question_title = '<h1 style="color:#33ff33;font-size:24px;">Question</h1>'
+    
+
+
+    st.markdown('<h1 style="color:#100170;font-size:48px;text-align:center;">RAG Response</h1>', unsafe_allow_html=True)
+    st.markdown('<h1 style="color:#100170;font-size:24px;">Question</h1>', unsafe_allow_html=True)
+    st.text_area(label="", value=response["query"], height=30)
+    st.markdown('<h1 style="color:#100170;font-size:24px;">RAG Output</h1>', unsafe_allow_html=True)
+    st.text_area(label="", value=response["result"])
+    st.markdown('<h1 style="color:#100170;font-size:24px;">Augmented knowledge</h1>', unsafe_allow_html=True)
+    st.text_area(label="", value=response["source_documents"])
     
     #st.button("Check Hallucination")
 
@@ -177,7 +201,8 @@ def hallu_eval(question: str, answer: str, context: str):
         }        
     )
     print("got hallu score")
-    st.text_area(label="Hallucinated?", value=hallucination_score, height=30)
+    st.markdown('<h1 style="color:#100170;font-size:24px;">Hallucinated?</h1>', unsafe_allow_html=True)
+    st.text_area(label=" ", value=hallucination_score, height=30)
     #return {"hallucination_score": hallucination_score}    
     #time.sleep(10)
 
@@ -210,12 +235,32 @@ with tab1:
     
     #print("lenght in tab1,  ", len(vectorstore.serialize_to_bytes()))
     options = ["true", "false"]
-    question = st.text_input(label="***User Question***", value="", label_visibility="visible", disabled=False)
-    evaluate = st.selectbox(label="***Evaluation***",options=options, index=0,  placeholder="Choose an option", disabled=False, label_visibility="visible")
-
-
+    
+    st.markdown('<h1 style="color:#100170;font-size:24px;">User Query</h1>', unsafe_allow_html=True)
+
+    question = st.text_input(label="", value="", placeholder="Type in question",label_visibility="visible", disabled=False)
+    #st.markdown('<h2 style="color:#3a0aa6;font-size:24px;">Evaluation</h2>', unsafe_allow_html=True)
+    evaluate = st.selectbox(label="***Perform  Evaluation?***",options=options, index=1,  placeholder="Choose an option", disabled=False, label_visibility="visible")
+
+    m = st.markdown("""
+                    <style>
+                    div.stButton > button:first-child {
+                        background-color: #100170;
+                        color:#ffffff;
+                    }
+                    div.stButton > button:hover {
+                        background-color: #00ff00;
+                        color:#ff0000;
+                        }
+                    </style>""", unsafe_allow_html=True)
+
+    #st.markdown("----", unsafe_allow_html=True)
+    columns = st.columns([2,1,2])
+    
+    if columns[1].form_submit_button(" Start RAG "):
+  
+      st.markdown("""<hr style="height:10px;border:none;color:#333;background-color: #100170;" /> """, unsafe_allow_html=True)
 
-    if st.form_submit_button("RAG with evaluation"):
       print("retrie  ,", retriever)
       chain = RetrievalQA.from_chain_type(
       llm=llm,
@@ -255,30 +300,32 @@ with tab2:
       
     
     #print("lenght in tab2,  ", len(vectorstore.serialize_to_bytes()))    
-    question = st.text_input(label="question", value="", label_visibility="visible", disabled=False)
-    answer = st.text_input(label="answer", value="", label_visibility="visible", disabled=False)
-    context = st.text_input(label="context", value="", label_visibility="visible", disabled=False)
+    question = st.text_input(label="**Question**", value="", label_visibility="visible", disabled=False)
+    answer = st.text_input(label="**answer**", value="", label_visibility="visible", disabled=False)
+    context = st.text_input(label="**context**", value="", label_visibility="visible", disabled=False)
 
 
     if st.form_submit_button("Evaluate"):
         hallu_eval(question, answer, context)
       
-  
 
-with tab3:
+print("activ session:  ", px.active_session().get_spans_dataframe())  
+trace_df = px.active_session().get_spans_dataframe()
+
+st.session_state['trace_df'] = trace_df
+
+# with tab3:
   
   
   
-  with st.form(" trace"):
-
-    if px.active_session():  
-        df0 = px.active_session().get_spans_dataframe()
-        if (df0 != None):
-            if not df0.empty:
-                df= df0.fillna('')
-                st.dataframe(df)
-            else:
-                print("No Dataframe!")
+#   with st.form(" trace"):
+
+#     if px.active_session():  
+#         df0 = px.active_session().get_spans_dataframe()
+#         if not df0.empty:
+#             df= df0.fillna('')
+#             st.dataframe(df)
+
     
 
 
@@ -340,4 +387,14 @@ def rag():
         #st.write("Doing more optional stuff")
 
 
-    return(response) 
+    return(response) 
+  
+
+a = st.markdown("""
+            <style>
+            div.stTextArea > textarea {
+                background-color: #0099ff;     
+                height: 1400px;
+                width: 800px;
+            }               
+            </style>""", unsafe_allow_html=True)