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README.md

@@ -1,12 +1,12 @@
----
-title: Llm Ptc4
-emoji: 🔥
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 5.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Llm Mini Series 4
+emoji: 📉
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 3.44.3
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,153 @@
+import gradio as gr
+import os
+import re
+
+from dotenv import load_dotenv
+from contextlib import redirect_stdout
+from io import StringIO
+
+
+from langchain import SQLDatabase, SQLDatabaseChain
+from langchain.llms import AzureOpenAI
+from langchain.agents import create_sql_agent
+from langchain.agents.agent_toolkits import SQLDatabaseToolkit
+from langchain.agents.agent_types import AgentType
+
+
+load_dotenv(os.getcwd() + "/.env")
+
+llm = AzureOpenAI(
+    model_name=os.environ["OPENAI_MODEL_NAME"],
+    deployment_name=os.environ["OPENAI_DEPLOYMENT_NAME"],
+    temperature=0,
+)
+
+sqlite_db_path = "data/Chinook.db"
+db = SQLDatabase.from_uri(f"sqlite:///{sqlite_db_path}")
+db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
+
+agent_executor = create_sql_agent(
+    llm=llm,
+    toolkit=SQLDatabaseToolkit(db=db, llm=llm),
+    verbose=True,
+    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+)
+
+
+def clear_input():
+    return "", "Hit 'Submit' to see output here"
+
+
+def generate_output_of_db_chain(user_message):
+    print(user_message)
+    if not user_message:
+        print("Empty input")
+        yield "Please enter a messager before hitting Send!"
+
+    with redirect_stdout(StringIO()) as f:
+        db_chain.run(user_message)
+
+    s = f.getvalue()
+    #[6:]: skip first two \n and special tag from LangChain
+    s = s[6:].replace('\n', '<br/>')
+
+    yield re.sub(r"(\x1b)?\[(\d+[m;])+", "", s)
+
+
+def generate_output_of_db_agent(user_message):
+    if not user_message:
+        print("Empty input")
+        yield "Please enter a messager before hitting Send!"
+        return ""
+
+    with redirect_stdout(StringIO()) as f:
+        agent_executor.run(user_message)
+
+    s = f.getvalue()
+    #[6:]: skip first two \n and special tag from LangChain
+    s = s[6:].replace("\n", "<br/>")
+
+    yield re.sub(r"(\x1b)?\[(\d+[m;])+", "", s)
+
+
+custom_css = """
+#banner-image {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+}
+#chat-message {
+    font-size: 14px;
+    min-height: 300px;
+}
+"""
+
+
+with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
+    gr.HTML("""<h1 align="center">LLM Mini-Series #4 💬</h1>""")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(
+                f"""
+                💻 TODO Add some nice description text
+                """
+            )
+
+    # normal SQL Chain
+    gr.HTML("""<h2 align="left">Using LangChain's SQLDatabaseChain</h2>""")
+    with gr.Row():
+        with gr.Column():
+            user_message = gr.Textbox(
+                placeholder="Enter your message here",
+                show_label=False,
+                elem_id="q-input",
+            )
+            with gr.Row():
+                clear_btn = gr.Button("Clear", elem_id="clear-btn", visible=True)
+                submit_btn = gr.Button("Submit", elem_id="submit-btn", visible=True)
+
+        with gr.Box():
+            output_field = gr.HTML(
+                value="Hit 'Submit' to see output here",
+                label="Output of model",
+                interactive=False,
+            )
+
+    # Agent-based approach
+    gr.HTML("""<h2 align="left">Using an agent-based approach with LangChain""")
+    with gr.Row():
+        with gr.Column():
+            user_message_agent = gr.Textbox(
+                placeholder="Enter your message here",
+                show_label=False,
+                elem_id="q-agent-input",
+            )
+            with gr.Row():
+                clear_agent_btn = gr.Button(
+                    "Clear", elem_id="clear-agent-btn", visible=True
+                )
+                submit_agent_btn = gr.Button(
+                    "Submit", elem_id="submit-agent-btn", visible=True
+                )
+
+        with gr.Box():
+            output_agent_field = gr.HTML(
+                value="Hit 'Submit' to see output here",
+                label="Output of model",
+                interactive=False,
+            )
+
+    clear_btn.click(clear_input, outputs=[user_message, output_field])
+    submit_btn.click(
+        generate_output_of_db_chain, inputs=[user_message], outputs=[output_field]
+    )
+
+    submit_agent_btn.click(
+        generate_output_of_db_agent,
+        inputs=[user_message_agent],
+        outputs=[output_agent_field],
+    )
+    clear_agent_btn.click(clear_input, outputs=[user_message_agent, output_agent_field])
+
+demo.queue(concurrency_count=16).launch(debug=True)  # , server_port=8080)

requirements.txt

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+gradio==3.35.2
+langchain==0.0.205
+openai==0.27.6
+python-dotenv==1.0.0