Create app.py

app.py

@@ -0,0 +1,97 @@
+import os
+from typing import Optional, Tuple
+
+import gradio as gr
+from langchain.chains import create_pandas_dataframe_agent
+from langchain.llms import Anthropic
+import pandas as pd
+
+def load_data():
+    """Load the data you want to use for the chain."""
+    return pd.read_parquet("data/part.17.parquet")
+
+def load_chain(trans: pd.DataFrame) -> create_pandas_dataframe_agent:
+    """Logic for loading the chain you want to use should go here."""
+    llm = Anthropic(temperature=0)
+    trans = load_data()
+    return create_pandas_dataframe_agent(llm, 
+                         trans, 
+                         verbose=True)
+
+def set_anthropic_api_key(api_key: str):
+    """Set the api key and return chain.
+    If no api_key, then None is returned.
+    """
+    if api_key:
+        os.environ["ANTHROPIC_API_KEY"] = api_key
+        chain = load_chain()
+        os.environ["ANTHROPIC_API_KEY"] = ""
+        return chain
+
+
+def chat(
+    inp: str, history: Optional[Tuple[str, str]], chain: Optional[create_pandas_dataframe_agent]
+):
+    """Execute the chat functionality."""
+    history = history or []
+    # If chain is None, that is because no API key was provided.
+    if chain is None:
+        history.append((inp, "Please paste your Anthropic key to use"))
+        return history, history
+    # Run chain and append input.
+    output = chain.run(input=inp)
+    history.append((inp, output))
+    return history, history
+
+
+block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
+
+with block:
+    with gr.Row():
+        gr.Markdown("<h3><center>LangChain Demo</center></h3>")
+
+        anthropic_api_key_textbox = gr.Textbox(
+            placeholder="Paste your Anthropic API key (sk-...)",
+            show_label=False,
+            lines=1,
+            type="password",
+        )
+
+    chatbot = gr.Chatbot()
+
+    with gr.Row():
+        message = gr.Textbox(
+            label="What's your question?",
+            placeholder="What was the average gas price on 2019-01-22?",
+            lines=1,
+        )
+        submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
+
+    gr.Examples(
+        examples=[
+            "Which to_address spent the most gas?",
+            "What was the average gas price on 2019-01-22?",
+            "How many unique addresses were sending transactions on 2019-01-22?",
+        ],
+        inputs=message,
+    )
+
+    gr.HTML("Demo application of a LangChain chain.")
+
+    gr.HTML(
+        "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
+    )
+
+    state = gr.State()
+    agent_state = gr.State()
+
+    submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
+    message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
+
+    anthropic_api_key_textbox.change(
+        set_anthropic_api_key,
+        inputs=[anthropic_api_key_textbox],
+        outputs=[agent_state],
+    )
+
+block.launch(debug=True)