Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import requests
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
-from routes import query_handler, purchase, order_management, account_management, customer_support, search_products
+from routes import input_handler, purchase, order_management, account_management, customer_support, search_products
 
 app = FastAPI()
 
@@ -15,13 +15,16 @@ app.include_router(customer_support.router,
                    prefix="/customer-support", tags=["customer-support"])
 app.include_router(search_products.router,
                    prefix="/search-products", tags=["search-products"])
-app.include_router(query_handler.router,
-                   prefix="/query-handler", tags=["query-handler"])
+app.include_router(input_handler.router,
+                   prefix="/input-handler", tags=["input-handler"])
 
 # Initialize the TestClient with the FastAPI app
 client = TestClient(app)
 
-def fastlane_agent(message, history):
+def add_message(history, message, audio_input):
+
+    if message is None and audio_input is None:
+        return "Please provide either text or audio."
 
     history_openai_format = [
         {"role": "system", "content": "You are an assistant for an eCommerce store."}]
@@ -29,30 +32,62 @@ def fastlane_agent(message, history):
         history_openai_format.append({"role": "user", "content": human})
         history_openai_format.append(
             {"role": "assistant", "content": assistant})
-    history_openai_format.append({"role": "user", "content": message})
 
-    response = client.post("/query-handler/", json={"text": message, "history": history_openai_format})
+    if message["text"] is not None:
+        history_openai_format.append(
+            {"role": "user", "content": message["text"]})
+
+    if audio_input:
+        transcription = transcribe(audio_input)
+        print(f"Transcription: {transcription}")
+        message["text"] += f' [Audio transcription] = {transcription}'
+        history_openai_format.append(
+            {"role": "user", "content": transcription})
+
+    for x in message["files"]:
+        #history.append(((x,), None))
+        message["text"] += ' [File attached]'
+        history_openai_format.append(
+            {"role": "user", "content": "Image attached"})
+
+    response = requests.post(
+        "http://localhost:8000/input-handler/", json={"text": message["text"], "files": message["files"], "history": history_openai_format})
+
     if response.status_code == 200:
-        return  response.json().get("generative response")
-    else:
-        return "Error: Could not fetch response."
-
-
-iface = gr.ChatInterface(
-    fn=fastlane_agent,
-    chatbot=gr.Chatbot(height=400),
-    textbox=gr.Textbox(
-        placeholder="How can I help you?", container=False, scale=7
-    ),
-    title="Try it!",
-    description="AI sales assistance for e-commmerce",
-    theme="soft",
-    examples=["Hello", "What is the status of my order?",
-              "Recommend me products"],
-    cache_examples=True,
-    retry_btn=None,
-    undo_btn="Delete Previous",
-    clear_btn="Clear"
-).launch()
-
-app = gr.mount_gradio_app(app, iface, path="./")
+        bot_response = response.json().get("generative response")
+        history.append((message["text"], bot_response))
+
+    return history, gr.MultimodalTextbox(value=None, interactive=False), None
+
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Fastlane Chat GPT</h1>")
+    gr.Markdown("AI sales assistance for e-commerce")
+
+    chatbot = gr.Chatbot(
+        height=400,
+        elem_id="chatbot"
+    )
+
+    # Add clear and undo buttons
+    with gr.Row():
+        undo_btn = gr.Button("Delete Previous")
+        clear_btn = gr.Button("Clear")
+
+    undo_btn.click(undo_last_message, chatbot, chatbot)
+    clear_btn.click(clear_chat, [], chatbot)
+
+    chat_input = gr.MultimodalTextbox(
+        interactive=True, placeholder="Enter message, upload file, or record audio...", show_label=False)
+    audio_input = gr.Audio(sources=["microphone"])
+
+    chat_msg = chat_input.submit(
+        add_message, [chatbot, chat_input, audio_input], [chatbot, chat_input, audio_input])
+    chat_msg.then(lambda: gr.MultimodalTextbox(
+        interactive=True), None, [chat_input])
+    
+    chatbot.like(print_like_dislike, None, None)
+
+demo.queue()
+demo.launch()
+
+app = gr.mount_gradio_app(app, demo, path="./")