Upload app.py

app.py

@@ -0,0 +1,186 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug 18 08:01:41 2023
+
+@author: Shamim Ahamed, RE AIMS Lab
+"""
+
+import streamlit as st
+import pandas as pd
+from tqdm.cli import tqdm
+import numpy as np
+import requests
+import pandas as pd
+from tqdm import tqdm
+
+
+def get_user_data(api, parameters):
+    response = requests.post(f"{api}", json=parameters)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        print(f"ERROR: {response.status_code}")
+        return None
+
+
+
+st.set_page_config(page_title="SuSastho.AI Chatbot", page_icon="🚀", layout='wide')
+
+st.markdown("""
+<style>
+p {
+    font-size:0.8rem !important;
+}
+textarea {
+    font-size: 0.8rem !important;
+    padding: 0.8rem 1rem 0.75rem 0.8rem !important;
+}
+button {
+    padding: 0.65rem !important;
+}
+
+.css-1lr5yb2 {
+    background-color: rgb(105 197 180) !important;
+}
+
+
+.css-1c7y2kd {
+    background-color: Transparent !important;
+}
+.css-4oy321 {
+    background-color: rgba(240, 242, 246, 0.5) !important;
+}
+
+</style>
+""", unsafe_allow_html=True)
+
+st.markdown("""
+<style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+</style>
+""",unsafe_allow_html=True)
+
+
+model_names = {
+    'BLOOM 7B': 'bloom-7b',
+}
+
+
+
+with st.sidebar:
+    st.title("SuSastho.AI - ChatBot 🚀")
+    model_name = model_names[st.selectbox('Model', list(model_names.keys()), 0)]
+
+    max_ctx = st.slider('Select Top N Context', min_value=1, max_value=6, value=3, step=1)
+    # ctx_checker_tmp = st.slider('Context Checker Sensitivity', min_value=0.001, max_value=1.0, value=0.008, step=0.001)
+    ctx_checker_tmp = 0.008
+    lm_tmp = st.slider('Language Model Sensitivity', min_value=0.001, max_value=1.0, value=0.1, step=0.001)
+
+    cls_threshold = st.slider('Classification Threshold', min_value=0.01, max_value=1.0, value=0.5, step=0.01)
+    
+    verbose = st.checkbox('Show Detailed Response', value=False)
+    if verbose == True:
+        retv_cnt = st.slider('Display N retrived Doc', min_value=0, max_value=32, value=0, step=1)
+
+    
+      
+
+endpoint = st.secrets["LLMEndpoint"]
+
+
+def main():
+    if model_name == 'None':
+        st.markdown('##### Please select a model.')
+        return
+    
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": 'assistant', "content": 'হ্যালো! আমি একটি এআই অ্যাসিস্ট্যান্ট। কীভাবে সাহায্য করতে পারি? 😊'}]
+    
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+
+    # Accept user input
+    if prompt := st.chat_input("এখানে মেসেজ লিখুন"):
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        
+        ## Get context
+        params = {
+            "chat_history": [
+                {"content": prompt}
+            ],
+            "model": "bloom-7b",
+            "mode": "specific",
+            "config": {
+                "ctx_checker_tmp": ctx_checker_tmp,
+                "lm_tmp": lm_tmp,
+                "max_ctx": max_ctx,
+                "cls_threshold": cls_threshold,
+                "llm_enable": True,
+            }
+        }
+        resp = get_user_data(endpoint, params)
+        if resp == None:
+            st.markdown('#### INTERNAL ERROR')
+            return
+        
+        response = resp['data']['responses'][0]['content']
+        context = resp['data']['logs']['content']['retrival_model']['matched_doc']
+        context_prob = resp['data']['logs']['content']['retrival_model']['matched_prob']
+
+        if verbose:
+            clen = len(context)
+            retrived = resp['data']['logs']['content']['retrival_model']['retrived_doc'][:retv_cnt]
+            retrived_prob = resp['data']['logs']['content']['retrival_model']['retrived_prob'][:retv_cnt]
+            retrived = [str(round(b, 3)) + ':  ' + a for a, b in zip (retrived, retrived_prob)]
+            retrived = '\n\n===============================\n\n'.join(retrived)
+
+            context = [str(round(b, 3)) + ':  ' + a for a, b in zip (context, context_prob)]
+            context = '\n\n===============================\n\n'.join(context)
+            response = f'###### Config: Context Checker Value: {ctx_checker_tmp}, LM Value: {lm_tmp}\n\n##### Retrived Context:\n{retrived}\n\n##### Matched Context:{clen}\n{context}\n\n##### Response:\n{response}'
+
+        
+        # Display assistant response in chat message container
+        with st.chat_message("assistant", avatar=None):
+            st.markdown(response)
+            
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+    
+    
+
+def app_viewport():
+    passw = st.empty()
+    appc = st.container()
+    
+    if 'logged_in' not in st.session_state:
+        with passw.container():
+            secret = st.text_input('Please Enter Access Code')
+            if st.button("Submit", type='primary'):
+                if secret == st.secrets["login_secret"]:
+                    passw.empty()
+                    st.session_state['logged_in'] = True
+                else:
+                    st.error('Wrong Access Code.')
+                    
+    if 'logged_in' in st.session_state and st.session_state['logged_in'] == True:
+        with appc:
+            main()
+
+
+    
+if __name__ == '__main__':
+    app_viewport()
+
+
+
+