Create Dash-Langchain-app2.py

Author: Coding-with-Adam

Blog/Dash-Langchain-app2.py

@@ -0,0 +1,83 @@
+from dash import Dash, dcc, html, callback, Input, Output, State, no_update
+from operator import itemgetter
+
+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
+from langchain.vectorstores import FAISS
+
+api_key = "my-api-key"  # https://platform.openai.com/account/api-keys
+app = Dash()
+
+app.layout = html.Div([
+    html.H1("Summarize Earnings Reports"),
+    html.Label("Select earnings report:"),
+    dcc.Dropdown(value='tesla-earning-report.txt',
+                 id='reports',
+                 clearable=False,
+                 style={'width':'120px'},
+                 options=[
+                     {'label':'Tesla', 'value':'tesla-earning-report.txt'},
+                     {'label':'Microsoft', 'value':'microsoft-earning-report.txt'}]
+                 ),
+    dcc.Input(id='question',
+              type='text',
+              placeholder='type your question...',
+              debounce=True,
+              style={'width':'500px', 'height':'30px', 'margin-top':20},
+              maxLength=100),
+    dcc.Loading(id="loading", children=html.Div(id='answer', children=None, style={'margin-bottom':20})),
+    html.Hr(),
+    html.Div(id='report-content', children=[])
+])
+
+
+@callback(
+    Output('answer', 'children'),
+    Output('report-content', 'children'),
+    Input('question','value'),
+    State('reports', 'value'),
+    prevent_initial_call=True
+)
+def update_layout(question_asked, file):
+    if question_asked:
+        with open(file, encoding="utf8") as f:
+            lines = f.readlines()
+
+        vectorstore = FAISS.from_texts(lines, embedding=OpenAIEmbeddings(openai_api_key=api_key))
+        retriever = vectorstore.as_retriever()
+
+        template = """Answer the question based only on the following context:
+        {context}
+    
+        Question: {question}
+        """
+
+        prompt = ChatPromptTemplate.from_template(template)
+
+        model = ChatOpenAI(openai_api_key=api_key)
+
+        chain = (
+                {"context": retriever, "question": RunnablePassthrough()}
+                | prompt
+                | model
+                | StrOutputParser()
+        )
+        display_answer = chain.invoke(question_asked)
+
+        view_selected_report = [
+            html.H3("Complete earnings report chosen:"),
+            dcc.Markdown(children=lines)
+
+        ]
+
+        return display_answer, view_selected_report
+
+    else:
+        return no_update
+
+
+if __name__ == "__main__":
+    app.run_server(debug=True)