Update app.py

app.py

@@ -1,70 +1,121 @@
+# app.py
 import gradio as gr
-from huggingface_hub import InferenceClient
+import requests
+import pandas as pd
+import base64
+from io import BytesIO
 
+# 🔧 CONFIGURE: Replace with your EC2 public IP
+API_URL = "http://<your-ec2-public-ip>:5000/ask"
+# Example: "http://3.8.123.45:5000/ask"
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+def query_database(question):
+    if not question.strip():
+        return "", None, None, "Please enter a question."
+    
+    try:
+        response = requests.post(API_URL, json={"question": question}, timeout=30)
+        result = response.json()
+        
+        if "error" in result:
+            return "", None, None, f"❌ {result['error']}"
+        
+        sql = result.get("sql", "N/A")
+        rows = result.get("result", [])
+        chart_data = result.get("chart")  # base64 PNG
+        
+        df = pd.DataFrame(rows) if rows else pd.DataFrame()
+        
+        # Decode chart
+        chart_image = None
+        if chart_data:
+            image_bytes = base64.b64decode(chart_data)
+            chart_image = BytesIO(image_bytes)
+        
+        return sql, df, chart_image, ""
+    
+    except requests.exceptions.ConnectionError:
+        return "", None, None, "❌ Connection failed. Is your API running?"
+    except requests.exceptions.Timeout:
+        return "", None, None, "⏱️ Request timed out. Try a simpler question."
+    except Exception as e:
+        return "", None, None, f"🚨 Unexpected error: {str(e)}"
 
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# 🎨 Theme: Enterprise Dark Blue
+theme = gr.themes.Default(
+    primary_hue="blue",
+    secondary_hue="gray",
+    neutral_hue="slate",
+    font=["Inter", "sans-serif"]
+).set(
+    body_background_fill="*neutral_950",
+    background_fill_secondary="*neutral_900"
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
+# 🚀 UI Layout
+with gr.Blocks(theme=theme, title="Enterprise SQL Assistant") as demo:
+    gr.HTML("""
+    <div style="text-align: center; padding: 20px;">
+        <h1 style="color: #3B82F6;">📊 Enterprise SQL Assistant</h1>
+        <p style="color: #9CA3AF; font-size: 1.1em;">
+            Ask questions about your data. Get SQL, results, and insights.
+        </p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🔍 Ask a Question")
+            question_input = gr.Textbox(
+                placeholder="E.g., How many members are there?",
+                label="Natural Language Query",
+                lines=3
+            )
+            submit_btn = gr.Button("🚀 Generate SQL & Results", variant="primary")
+            
+            gr.Markdown("### 💡 Example Queries")
+            gr.Examples(
+                examples=[
+                    ["How many members are there?"],
+                    ["What is the total transaction amount?"],
+                    ["Show members with their account balances"],
+                    ["Which member has the highest balance?"],
+                    ["Show transaction trends over time"]
+                ],
+                inputs=question_input,
+                label=""
+            )
+        
+        with gr.Column(scale=2):
+            gr.Markdown("### 📄 Results")
+            status = gr.Textbox(label="Status", interactive=False)
+            
+            with gr.Tabs():
+                with gr.Tab("SQL Query"):
+                    sql_output = gr.Code(label="", language="sql")
+                
+                with gr.Tab("Data Results"):
+                    results_output = gr.Dataframe(
+                        label="Query Results",
+                        interactive=False,
+                        wrap=True
+                    )
+                
+                with gr.Tab("Visual Insights"):
+                    chart_output = gr.Image(
+                        label="Chart",
+                        type="pil",
+                        height=400,
+                        elem_classes="chart-container"
+                    )
+    
+    # Events
+    submit_btn.click(
+        fn=query_database,
+        inputs=question_input,
+        outputs=[sql_output, results_output, chart_output, status]
+    )
 
+# Launch
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()