Create app.py

app.py

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+#@title Gradio UI - 1
+import gradio as gr
+import os
+from langchain.chat_models import init_chat_model
+from langchain_community.utilities import SQLDatabase
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+from langchain.agents import create_agent
+from google.colab import userdata
+
+# --- GLOBALS ---
+model = None  # ✅ prevent "NameError" before setup
+db = None
+toolkit = None
+agent = None
+
+# Define available models for each provider
+PROVIDER_MODELS = {
+    "google_genai": ["gemini-2.0-pro", "gemini-2.5-flash-lite", "gemini-2.5-flash", "gemini-2.0-flash-thinking"],
+    "openai": ["gpt-4o", "gpt-4o-mini", "gpt-3.5-turbo"],
+    "anthropic": ["claude-3-opus", "claude-3-sonnet", "claude-3-haiku"],
+    "azure_openai": ["gpt-4-turbo", "gpt-4o-mini", "gpt-35-turbo"],
+    "bedrock": ["anthropic.claude-3-sonnet-v1", "mistral.mixtral-8x7b"],
+    "xai": ["grok-2", "grok-2-mini"],
+    "deepseek": ["deepseek-chat", "deepseek-coder"],
+    "perplexity": ["sonar-small-chat", "sonar-medium-chat", "sonar-large-chat"]
+}
+
+
+def create_chatbot_interface():
+    with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Noto Sans")]),
+                   css='footer {visibility: hidden}') as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("## LLM Setup")
+                llm_provider = gr.Dropdown(
+                    list(PROVIDER_MODELS.keys()), label="Select Provider", value="google_genai"
+                )
+                llm_model = gr.Dropdown(
+                    choices=PROVIDER_MODELS["google_genai"],
+                    label="Select Model",
+                    value="gemini-2.5-flash-lite"
+                )
+                api_key = gr.Textbox(label="Enter API Key", type="password")
+                setup_llm_btn = gr.Button("Setup LLM")
+
+                gr.Markdown("## Database Connection")
+                db_connection_string = gr.Textbox(label="Enter Connection String", type="password")
+                connect_db_btn = gr.Button("Connect to Database")
+                db_status = gr.Markdown("")  # ✅ show DB connection feedback
+
+            with gr.Column(scale=2):
+                gr.Markdown("## Chatbot Interface")
+                chatbot = gr.Chatbot()
+                msg = gr.Textbox(label="Enter your question")
+                clear = gr.Button("Clear")
+
+        # --- FUNCTIONS ---
+
+        def update_model_dropdown(provider):
+            models = PROVIDER_MODELS.get(provider, [])
+            default = models[0] if models else None
+            return gr.update(choices=models, value=default)
+
+        def setup_llm(provider, model_name, key):
+            os.environ["GOOGLE_API_KEY"] = key
+            global model
+            model = init_chat_model(model_name, model_provider=provider)
+            return f"✅ LLM model `{model_name}` from `{provider}` setup successfully."
+
+        def connect_to_db(db_url):
+            global db, toolkit, tools, agent, system_prompt, model
+            if model is None:
+                return "❌ Please set up the LLM before connecting to the database."
+
+            db = SQLDatabase.from_uri(db_url)
+            toolkit = SQLDatabaseToolkit(db=db, llm=model)
+            tools = toolkit.get_tools()
+
+            system_prompt = """
+              You are an agent designed to interact with a SQL database.
+              Given an input question, create a syntactically correct {dialect} query to run,
+              then look at the results of the query and return the answer. Unless the user
+              specifies a specific number of examples they wish to obtain, always limit your
+              query to at most {top_k} results.
+
+              You can order the results by a relevant column to return the most interesting
+              examples in the database. Never query for all the columns from a specific table,
+              only ask for the relevant columns given the question.
+
+              You MUST double check your query before executing it. If you get an error while
+              executing a query, rewrite the query and try again.
+
+              DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the
+              database.
+
+              To start you should ALWAYS look at the tables in the database to see what you
+              can query. Do NOT skip this step.
+
+              Then you should query the schema of the most relevant tables.
+              """.format(
+                  dialect=db.dialect,
+                  top_k=5,
+              )
+
+            agent = create_agent(model, tools, system_prompt=system_prompt)
+            tables = db.get_usable_table_names()
+            return f"✅ Connected to database successfully.\n\n**Available tables:** {tables}"
+
+        def respond(message, chat_history):
+            result = ""
+            for step in agent.stream(
+                {"messages": [{"role": "user", "content": message}]},
+                stream_mode="values",
+            ):
+                result += step["messages"][-1].content
+            chat_history.append((message, result))
+            return "", chat_history
+
+        # --- EVENTS ---
+        llm_provider.change(update_model_dropdown, inputs=llm_provider, outputs=llm_model)
+        setup_llm_btn.click(setup_llm, inputs=[llm_provider, llm_model, api_key], outputs=None)
+        connect_db_btn.click(connect_to_db, inputs=[db_connection_string], outputs=db_status)
+        msg.submit(respond, [msg, chatbot], [msg, chatbot])
+        clear.click(lambda: None, None, chatbot, queue=False)
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = create_chatbot_interface()
+    demo.launch(debug=True)