Sync updates from source repository

README.md

@@ -1,14 +1,13 @@
 ---
 title: Ask Anabel
-emoji: 📈
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.32.2
+emoji: 🚀
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 4.38.1
 app_file: app.py
-pinned: false
 license: apache-2.0
-short_description: Ask SEL questions based on the works of Anabel Jensen
+pinned: false
+short_description: Ask Anabel about Social Emotional Learning (EQ)
 ---
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,117 +1,79 @@
 from omegaconf import OmegaConf
 from query import VectaraQuery
 import os
-
-import streamlit as st
-from streamlit_pills import pills
-
-from PIL import Image
-
-max_examples = 6
+import gradio as gr
 
 def isTrue(x) -> bool:
     if isinstance(x, bool):
         return x
     return x.strip().lower() == 'true'
 
-def launch_bot():
-    def generate_response(question):
-        response = vq.submit_query(question)
-        return response
-    
-    def generate_streaming_response(question):
-        response = vq.submit_query_streaming(question)
-        return response
-    
-    def show_example_questions():        
-        if len(st.session_state.example_messages) > 0 and st.session_state.first_turn:            
-            selected_example = pills("Queries to Try:", st.session_state.example_messages, index=None)
-            if selected_example:
-                st.session_state.ex_prompt = selected_example
-                st.session_state.first_turn = False
-                return True
-        return False
-
-    if 'cfg' not in st.session_state:
-        corpus_keys = str(os.environ['corpus_keys']).split(',')
-        cfg = OmegaConf.create({
-            'corpus_keys': corpus_keys,
-            'api_key': str(os.environ['api_key']),
-            'title': os.environ['title'],
-            'source_data_desc': os.environ['source_data_desc'],
-            'streaming': isTrue(os.environ.get('streaming', False)),
-            'prompt_name': os.environ.get('prompt_name', None),
-            'examples': os.environ.get('examples', None)
-        })
-        st.session_state.cfg = cfg
-        st.session_state.ex_prompt = None
-        st.session_state.first_turn = True        
-        example_messages = [example.strip() for example in cfg.examples.split(",")]
-        st.session_state.example_messages = [em for em in example_messages if len(em)>0][:max_examples]
-        
-        st.session_state.vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name)
-
-    cfg = st.session_state.cfg
-    vq = st.session_state.vq
-    st.set_page_config(page_title=cfg.title, layout="wide")
+corpus_keys = str(os.environ['corpus_keys']).split(',')
+cfg = OmegaConf.create({
+    'corpus_keys': corpus_keys,
+    'api_key': str(os.environ['api_key']),
+    'title': os.environ['title'],
+    'source_data_desc': os.environ['source_data_desc'],
+    'streaming': isTrue(os.environ.get('streaming', False)),
+    'prompt_name': os.environ.get('prompt_name', None),
+    'examples': os.environ.get('examples', None)
+})
 
-    # left side content
-    with st.sidebar:
-        image = Image.open('Vectara-logo.png')
-        st.image(image, width=175)
-        st.markdown(f"## About\n\n"
-                    f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n\n")
+vq = VectaraQuery(cfg.api_key, cfg.corpus_keys, cfg.prompt_name)
 
-        st.markdown("---")
-        st.markdown(
-            "## How this works?\n"
-            "This app was built with [Vectara](https://vectara.com).\n"
-            "Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\n"
-            "This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
-        )
-        st.markdown("---")
+def respond(message, history):
+    if cfg.streaming:
+        # Call stream response and stream output
+        stream = vq.submit_query_streaming(message)
         
+        
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield outputs
+    else:
+        # Call non-stream response and return message output
+        response = vq.submit_query(message)
+        yield response
 
-    st.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)
+heading_html = f'''
+                <table>
+                  <tr>
+                      <td style="width: 80%; text-align: left; vertical-align: middle;"> <h1>Vectara AI Assistant: {cfg.title}</h1> </td>
+                      <td style="width: 20%; text-align: right; vertical-align: middle;"> <img src="https://github.com/david-oplatka/chatbot-streamlit/blob/main/Vectara-logo.png?raw=true"> </td>
+                  </tr>
+                  <tr>
+                      <td colspan="2" style="font-size: 16px;">This demo uses Retrieval Augmented Generation from <a href="https://vectara.com/">Vectara</a> to ask questions about {cfg.source_data_desc}.</td>
+                  </tr>
+                </table>
+                '''
 
-    if "messages" not in st.session_state.keys():
-        st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
-                
-    # Display chat messages
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.write(message["content"])
+bot_css = """
+table {
+  border: none;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+td {
+  vertical-align: middle;
+  border: none;
+}
+img {
+  width: 75%;
+}
+h1 {
+  font-size: 2em; /* Adjust the size as needed */
+}
+"""
 
-    example_container = st.empty()
-    with example_container:
-        if show_example_questions():
-            example_container.empty()
-            st.rerun()
+if cfg.examples:
+    app_examples = [example.strip() for example in cfg.examples.split(",")]
+else:
+    app_examples = None
+
+demo = gr.ChatInterface(respond, description = heading_html, css = bot_css,
+                        chatbot = gr.Chatbot(value = [[None, "How may I help you?"]]), examples = app_examples, cache_examples = False)
 
-    # select prompt from example question or user provided input
-    if st.session_state.ex_prompt:
-        prompt = st.session_state.ex_prompt
-    else:
-        prompt = st.chat_input()
-    if prompt:
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.write(prompt)
-        st.session_state.ex_prompt = None
-        
-    # Generate a new response if last message is not from assistant
-    if st.session_state.messages[-1]["role"] != "assistant":
-        with st.chat_message("assistant"):
-            if cfg.streaming:
-                stream = generate_streaming_response(prompt) 
-                response = st.write_stream(stream) 
-            else:
-                with st.spinner("Thinking..."):
-                    response = generate_response(prompt)
-                    st.write(response)
-            message = {"role": "assistant", "content": response}
-            st.session_state.messages.append(message)
-            st.rerun()
-    
 if __name__ == "__main__":
-    launch_bot()
+    demo.launch()

query.py

@@ -125,4 +125,4 @@ class VectaraQuery():
                         chunks.append(chunk)
                         yield chunk
 
-        return ''.join(chunks)
+        return ''.join(chunks)

requirements.txt

@@ -1,5 +1,5 @@
+gradio==3.40.0
 requests_to_curl==1.1.0
 toml==0.10.2
 omegaconf==2.3.0
 syrupy==4.0.8
-streamlit_pills==0.3.0