Create app.py

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+from flowise import Flowise, PredictionData
+import json
+
+# Flowise app base url
+base_url = "https://startrz-devi.hf.space/api/v1/prediction"
+
+# Chatflow/Agentflow ID
+flow_id = "e54adffc-ae77-42e5-9fc0-c4584e081093"
+
+# Show title and description.
+st.title("Devi Research")
+st.write(
+    "This is a simple chatbot that uses Flowise Python SDK"
+)
+
+# Create a Flowise client.
+client = Flowise(base_url=base_url)
+
+# Create a session state variable to store the chat messages. This ensures that the
+# messages persist across reruns.
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display the existing chat messages via `st.chat_message`.
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+def generate_response(prompt: str):
+    print('generating response')
+    completion = client.create_prediction(
+        PredictionData(
+            chatflowId=flow_id,
+            question=prompt,
+            streaming=True
+        )
+    )
+
+    for chunk in completion:
+        print(chunk)
+        parsed_chunk = json.loads(chunk)
+        if (parsed_chunk['event'] == 'token' and parsed_chunk['data'] != ''):
+            yield str(parsed_chunk['data'])
+
+# Create a chat input field to allow the user to enter a message. This will display
+# automatically at the bottom of the page.
+if prompt := st.chat_input("What is up?"):
+
+    # Store and display the current prompt.
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Stream the response to the chat using `st.write_stream`, then store it in 
+    # session state.
+    with st.chat_message("assistant"):
+        response = generate_response(prompt)
+        full_response = st.write_stream(response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})