added db to app_gui

app_gui.py

@@ -1,19 +1,12 @@
 # Import Gradio for UI, along with other necessary libraries
 import gradio as gr
-from fastapi import FastAPI
-from fastapi import FastAPI
-from rag_app.agents.react_agent import agent_executor, llm
-from rag_app.chains import user_response_sentiment_prompt
-from typing import Dict
-import re
-from rag_app.utils.utils import extract_responses
 from rag_app.loading_data.load_S3_vector_stores import get_chroma_vs
 from rag_app.agents.react_agent import agent_executor
-# need to import the qa!
+from config import db
 
-app = FastAPI()
 get_chroma_vs()
 
+
 if __name__ == "__main__":
 
     # Function to add a new input to the chat history
@@ -21,29 +14,12 @@ if __name__ == "__main__":
         # Append the new text to the history with a placeholder for the response
         history = history + [(text, None)]
         return history, ""
-    # Function to add a new input to the chat history
-    def add_text(history, text):
-        # Append the new text to the history with a placeholder for the response
-        history = history + [(text, None)]
-        return history, ""
-
-    # Function representing the bot's response mechanism
-    def bot(history):
-        # Obtain the response from the 'infer' function using the latest input
-        response = infer(history[-1][0], history)
-        #sources = [doc.metadata.get("source") for doc in response['source_documents']]
-        #src_list = '\n'.join(sources)
-        #print_this = response['result'] + "\n\n\n Sources: \n\n\n" + src_list
 
-
-        #history[-1][1] = print_this #response['answer']
-        # Update the history with the bot's response
-        history[-1][1] = response['output']
-        return history
     # Function representing the bot's response mechanism
     def bot(history):
         # Obtain the response from the 'infer' function using the latest input
         response = infer(history[-1][0], history)
+        print(response)
         history[-1][1] = response['output']
         return history
 
@@ -60,37 +36,7 @@ if __name__ == "__main__":
             )
             return result
         except Exception:
-            raise gr.Error("Model is Overloaded, Please retry later!")
-        
-    def vote(data: gr.LikeData):
-        if data.liked:
-            print("You upvoted this response: " + data.value)
-        else:
-            print("You downvoted this response: " + data.value)
-    # Function to infer the response using the RAG model
-    def infer(question, history):
-        # Use the question and history to query the RAG model
-        #result = qa({"query": question, "history": history, "question": question})
-        # try:
-        #     data = user_sentiment_chain.invoke({"user_reponse":question})
-        #     responses = extract_responses(data)
-        #     if responses['AI'] == "1":
-        #         pass
-        #         # Do important stuff here plox
-        #         # store into database
-        # except Exception as e:
-        #     raise e
-        try:
-            result = agent_executor.invoke(
-                {
-                    "input": question,
-                    "chat_history": history
-                }
-            )
-            return result
-        except Exception as e:
-            # raise gr.Error("Model is Overloaded, Please retry later!")
-            raise e
+            raise gr.Warning("Model is Overloaded, please try again in a few minutes!")
         
     def vote(data: gr.LikeData):
         if data.liked:
@@ -98,13 +44,14 @@ if __name__ == "__main__":
         else:
             print("You downvoted this response: ")
 
+    def get_examples(input_text: str):
+        tmp_history = [(input_text, None)]
+        response = infer(input_text, tmp_history)
+        return response['output']
+
     # CSS styling for the Gradio interface
     css = """
-    #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-    """
-    # CSS styling for the Gradio interface
-    css = """
-    #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+    #col-container {max-width: 1200px; margin-left: auto; margin-right: auto;}
     """
 
     # HTML content for the Gradio interface title
@@ -113,59 +60,51 @@ if __name__ == "__main__":
         <p>Hello, I BotTina 2.0, your intelligent AI assistant. I can help you explore Wuerttembergische Versicherungs products.<br />
     </div>
     """
-    # HTML content for the Gradio interface title
-    title = """
-    <div style="text-align:left;">
-        <p>Hello, I BotTina 2.0, your intelligent AI assistant. I can help you explore Wuerttembergische Versicherungs products.<br />
-    </div>
+    head_style = """
+    <style>
+    @media (min-width: 1536px)
+    {
+        .gradio-container {
+            min-width: var(--size-full) !important;
+        }
+    }
+    </style>
     """
 
     # Building the Gradio interface
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Blocks(theme=gr.themes.Soft(), title="InsurePal AI 🤵🏻‍♂️", head=head_style) as demo:
         with gr.Column(elem_id="col-container"):
-            gr.HTML(title)  # Add the HTML title to the interface
+            gr.HTML()  # Add the HTML title to the interface
             chatbot = gr.Chatbot([], elem_id="chatbot",
-                                        label="BotTina 2.0",
+                                        label="InsurePal AI",
                                         bubble_full_width=False,
                                         avatar_images=(None, "https://dacodi-production.s3.amazonaws.com/store/87bc00b6727589462954f2e3ff6f531c.png"),
                                         height=680,)  # Initialize the chatbot component
             chatbot.like(vote, None, None)
-            clear = gr.Button("Clear")  # Add a button to clear the chat
-    # Building the Gradio interface
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        with gr.Column(elem_id="col-container"):
-            gr.HTML(title)  # Add the HTML title to the interface
-            chatbot = gr.Chatbot([], elem_id="chatbot",
-                                        label="BotTina 2.0",
-                                        bubble_full_width=False,
-                                        avatar_images=(None, "https://dacodi-production.s3.amazonaws.com/store/87bc00b6727589462954f2e3ff6f531c.png"),
-                                        height=680,)  # Initialize the chatbot component
-            chatbot.like(vote, None, None)
-            clear = gr.Button("Clear")  # Add a button to clear the chat
 
             # Create a row for the question input
             with gr.Row():
-                question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-            # Create a row for the question input
+                question = gr.Textbox(label="Question", show_label=False, placeholder="Type your question and hit Enter ", scale=4)
+                send_btn = gr.Button(value="Send", variant="primary", scale=0)
+            with gr.Accordion(label="Beispiele", open=False):
+                #examples
+                examples = gr.Examples([
+                    "Welche Versicherungen brauche ich als Student?", 
+                    "Wie melde ich einen Schaden?",
+                    "Wie kann ich mich als Selbstständiger finanziell absichern?",
+                    "Welche Versicherungen sollte ich für meine Vorsorge abschliessen?"
+                    ], inputs=[question], label="") #, cache_examples="lazy", fn=get_examples, outputs=[chatbot]
+
             with gr.Row():
-                question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
+                clear = gr.Button("Clear")  # Add a button to clear the chat
 
         # Define the action when the question is submitted
         question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
-            bot, chatbot, chatbot
-        )
+            bot, chatbot, chatbot)
+        send_btn.click(add_text, [chatbot, question], [chatbot, question], queue=False).then(
+            bot, chatbot, chatbot)
         # Define the action for the clear button
         clear.click(lambda: None, None, chatbot, queue=False)
-        # Define the action when the question is submitted
-        question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
-            bot, chatbot, chatbot
-        )
-        # Define the action for the clear button
-        clear.click(lambda: None, None, chatbot, queue=False)
-
-    # Launch the Gradio demo interface
-    demo.queue().launch(share=False, debug=True)
 
-    app = gr.mount_gradio_app(app, demo, path="/")
     # Launch the Gradio demo interface
     demo.queue().launch(share=False, debug=True)