Update app.py

app.py

@@ -1,40 +1,40 @@
-import gradio as gr
-
-def companyChat(company_name, chat_history):
-  from langchain.chains import ConversationalRetrievalChain
-  from langchain_community.retrievers import KayAiRetriever
-  from langchain_anthropic import ChatAnthropic
-  model = ChatAnthropic(
-        model_name="claude-3-sonnet-20240229"
-    )
-  retriever = KayAiRetriever.create(
-        dataset_id="company", data_types=["10-K"], num_contexts=10
-    ) 
-  qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
-  company = company_name
-  outputs = []
-  chat_history = []
-  questions = [
-    f"Summarize the {company}'s financial performance over the past years, including revenue growth, profitability (net income), and margins.",
-    f"Identify and analyze {company}'s earnings per share (EPS diluted and basic), Calculate Return on equity (ROE) and Debt-to-Equity ratio For past few years",
-    f"Add bullet points for main risks identified by {company} in its 10-K filing" ]
-  for question in questions:
-      result = qa({"question": question, "chat_history": chat_history})
-      chat_history.append((question, result["answer"]))
-      outputs.append(result["answer"])  # Append both answers to a list
-  return outputs  # Return the list of both answers
-
-# Define the interface with inputs and outputs
-interface = gr.Interface(
-  fn=companyChat,
-  inputs=gr.Textbox(label="Company Name or Ticker"),
-  outputs=[gr.Textbox(label="Company's financial performance over the past years"), gr.Textbox(label="Company's earnings per share (EPS), return on equity (ROE), and debt-to-equity ratio For past few years"), gr.Textbox(label="Main risks identified in 10-K filing")],
-  title="Insights on 10K Filings",
-  description="Get Insights right from the 10K Filings submitted by the company",
-  theme="soft",
-  examples=["APPL"],
-  cache_examples=True,
-  clear_btn="Clear",
-)
-
-interface.launch()
+import gradio as gr
+
+def companyChat(company_name, chat_history):
+  from langchain.chains import ConversationalRetrievalChain
+  from langchain_community.retrievers import KayAiRetriever
+  from langchain_anthropic import ChatAnthropic
+  model = ChatAnthropic(
+        model_name="claude-3-sonnet-20240229"
+    )
+  retriever = KayAiRetriever.create(
+        dataset_id="company", data_types=["10-K"], num_contexts=10
+    ) 
+  qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
+  company = company_name
+  outputs = []
+  chat_history = []
+  questions = [
+    f"Summarize the {company}'s financial performance over the past years, including revenue growth, profitability (net income), and margins. In English",
+    f"Identify and analyze {company}'s earnings per share (EPS diluted and basic), Calculate Return on equity (ROE) and Debt-to-Equity ratio For past few years. In English",
+    f"Add bullet points for main risks identified by {company} in its 10-K filing. In English" ]
+  for question in questions:
+      result = qa({"question": question, "chat_history": chat_history}
+      chat_history.append((question, result["answer"]))
+      outputs.append(result["answer"])  # Append both answers to a list
+  return outputs  # Return the list of both answers
+
+# Define the interface with inputs and outputs
+interface = gr.Interface(
+  fn=companyChat,
+  inputs=gr.Textbox(label="Company Name or Ticker"),
+  outputs=[gr.Textbox(label="Company's financial performance over the past years"), gr.Textbox(label="Company's earnings per share (EPS), return on equity (ROE), and debt-to-equity ratio For past few years"), gr.Textbox(label="Main risks identified in 10-K filing")],
+  title="Insights on 10K Filings",
+  description="Get Insights right from the 10K Filings submitted by the company",
+  theme="soft",
+  examples=["APPL"],
+  cache_examples=True,
+  clear_btn="Clear",
+)
+
+interface.launch()