Update app.py

app.py

@@ -6,7 +6,7 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import AwaEmbeddings
 import os
 import openai
-import gradio as gr
+import streamlit as st
 
 
 def get_transcript(url):
@@ -37,36 +37,72 @@ text_splitter = RecursiveCharacterTextSplitter(
     is_separator_regex = False,
 )
 
-def chat(url, question, api_key):
-  os.environ["OPENAI_API_KEY"] = api_key
-  openai.api_key = api_key
-
-  info = get_transcript(url)
-  texts = text_splitter.create_documents([info])
-  db = FAISS.from_documents(texts, embeddings)
-  docs = db.similarity_search(question)
-
-  prompt = [
-      {"role": "system", "content": """You are my Youtube Asisstant. I will pass you texts from a Youtube Video Transcrip and I need you to use them to answer my question from the Youtube Video.
-      Please do not invent any information, and I am asking about information in the Youtube Video."""},
-      {"role":"user", "content": f"Context:{docs}"},
-      {"role":"user", "content": f"Question:{question}"},
-  ]
-  response = openai.ChatCompletion.create(
-    model="gpt-3.5-turbo-0613",
-    messages=prompt,
-    temperature = 0
-    )
-
-  return response["choices"][0]["message"]["content"]
-
-
-# Create a gradio interface with two inputs and one output
-demo = gr.Interface(
-  fn=chat, # The function to call
-  inputs=[gr.Textbox(label="Youtube URL"), gr.Textbox(label="Question"), gr.Textbox(label="API_KEY")], # The input components
-  outputs=gr.Textbox(label="Answer") # The output component
-)
+def chat(question):
+  if 'database' in st.session_state:
+    docs = st.session_state.database.similarity_search(question)
+
+    prompt = [
+        {"role": "system", "content": """You are my Youtube Asisstant. I will pass you texts from a Youtube Video Transcrip and I need you to use them to answer my question from the Youtube Video.
+        Please do not invent any information, and I am asking about information in the Youtube Video."""},
+        {"role":"user", "content": f"Context:{docs}"},
+        {"role":"user", "content": f"Question:{question}"},
+    ]
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        messages=prompt,
+        temperature = 0
+        )
+
+    return response["choices"][0]["message"]["content"]
+  else:
+      return "Error, not generated database"
+
+
+#------------------------------------------------------------- APP STREAMLIT--------------------------------------------------------------------
+
+st.title("Ask Question Youtube Videos")
+
+with st.sidebar:
+    if "api" not in st.session_state:
+        api_key= st.text_input(label="api", placeholder="API Key from OpenAI", label_visibility="hidden")
+        if st.button(label="Save"):
+            os.environ["OPENAI_API_KEY"] = api_key
+            openai.api_key = api_key
+            st.session_state['api'] = api_key
+    else:
+        url = st.text_input(label="url", placeholder="Youtube Video URL", label_visibility="hidden")
+        if st.button(label="Save"):
+            st.session_state['url'] = url
+            info = get_transcript(url)
+            texts = text_splitter.create_documents([info])
+            st.session_state['database'] = FAISS.from_documents(texts, embeddings)
+
+if "api" not in st.session_state:
+    st.write("Please, introduce your OpenAI API key to ask questions to any YouTube Video")
+    
+elif 'url' not in st.session_state:
+    st.write("Please, introduce link URL from the YouTube Video")
+
+else:
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # 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"])
 
-# Launch the interface
-demo.launch(share=True)
+    # Accept user input
+    if prompt := st.chat_input("What is up?"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        # Get response from your custom chat function
+        response = chat(prompt)
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})