reverting frontend

app.py

@@ -2,11 +2,45 @@ import os
 import json
 import gradio as gr
 import requests
+from dotenv import load_dotenv
 import gradio.components as gc
 import uuid
 
+# Load environment variables
+load_dotenv()
+
+# Get sensitive config from environment variables (set these in your .env file)
+#ELASTICSEARCH_URL = os.getenv("ELASTICSEARCH_URL")
+#ELASTICSEARCH_USER = os.getenv("ELASTICSEARCH_USER")
+#ELASTICSEARCH_PASSWORD = os.getenv("ELASTICSEARCH_PASSWORD")
+#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+#AWS_LAMBDA_URL = os.getenv("AWS_LAMBDA_URL")
+GRADIO_AUTH_USERNAME = os.getenv("GRADIO_AUTH_USERNAME")
+GRADIO_AUTH_PASSWORD = os.getenv("GRADIO_AUTH_PASSWORD")
+
+# Check required env vars for local development only
+if not os.getenv("SPACE_ID"):
+    missing_vars = []
+    for var in ["GRADIO_AUTH_USERNAME", "GRADIO_AUTH_PASSWORD"]:
+        if not os.getenv(var):
+            missing_vars.append(var)
+    if missing_vars:
+        print(f"Warning: Missing auth environment variables for local development: {', '.join(missing_vars)}")
+
+es = None
+
+# Initialize OpenAI
+#openai_client = OpenAI(api_key=OPENAI_API_KEY)
+def chat_completion(messages, model="gpt-3.5-turbo", temperature=0.1):
+    #return openai_client.chat.completions.create(
+    #    model=model,
+    #    messages=messages,
+    #    temperature=temperature
+    #)
+    return None
+
 def process_faq(question, user_id="anonymous", model="claude-sonnet"):
-    """Process FAQ by calling AWS Lambda function"""
+    """Process FAQ by calling AWS Lambda function with streaming response"""
     try:
         # Determine the correct Lambda URL and model parameter based on selection
         if model.startswith("nova-"):
@@ -38,26 +72,69 @@ def process_faq(question, user_id="anonymous", model="claude-sonnet"):
             if response.status_code != 200:
                 return f"Error: Lambda function returned status code {response.status_code}"
             
-            # Process the response (Lambda returns complete response as plain text)
+            # Process the streaming response
             full_response = ""
             for chunk in response.iter_content(chunk_size=1024, decode_unicode=True):
                 if chunk:
-                    full_response += chunk
-            
-            # Simulate streaming by yielding progressively
-            if full_response:
-                words = full_response.split()
-                current_text = ""
-                for i, word in enumerate(words):
-                    current_text += word + " "
-                    if i % 2 == 0 or i == len(words) - 1:  # Yield every 2 words or at the end
-                        yield current_text.strip()
+                    try:
+                        # Try to parse the chunk as JSON
+                        chunk_data = json.loads(chunk)
+                        if "response" in chunk_data:
+                            chunk_text = chunk_data["response"]
+                            full_response += chunk_text
+                            yield full_response
+                    except json.JSONDecodeError:
+                        # If not JSON, treat as plain text
+                        full_response += chunk
+                        yield full_response
             
             return full_response
             
     except Exception as e:
         return f"Error processing FAQ: {str(e)}"
 
+def natural_to_query(natural_query):
+    """Convert natural language to Elasticsearch query body"""
+    try:
+        prompt = f"""Convert the following natural language query into an Elasticsearch query body.\nThe query should be in JSON format and follow Elasticsearch query DSL syntax.\n\nNatural language query: {natural_query}\n\nReturn only the JSON query body, nothing else."""
+        response = chat_completion([
+            {"role": "system", "content": "You are an expert in Elasticsearch query DSL. Convert natural language to Elasticsearch queries."},
+            {"role": "user", "content": prompt}
+        ], model="gpt-3.5-turbo", temperature=0.1)
+        # Extract and format the query
+        if hasattr(response, 'choices'):
+            # For OpenAI v1.x
+            content = response.choices[0].message.content.strip()
+        else:
+            # For OpenAI v0.x
+            content = response["choices"][0]["message"]["content"].strip()
+        try:
+            query_json = json.loads(content)
+            return json.dumps(query_json, indent=2)
+        except json.JSONDecodeError:
+            return content
+    except Exception as e:
+        return f"Error generating query: {str(e)}"
+
+def execute_elasticsearch_query(query_body):
+    """Execute the Elasticsearch query"""
+    try:
+        # Parse the query body
+        query_json = json.loads(query_body)
+        
+        # Execute the query
+        response = es.search(
+            index="your_index_name",  # Replace with your actual index name
+            body=query_json
+        )
+        
+        # Format the response
+        return json.dumps(response, indent=2)
+    except json.JSONDecodeError:
+        return "Error: Invalid JSON query body"
+    except Exception as e:
+        return f"Error executing query: {str(e)}"
+
 # --- Gradio v4.x UI ---
 def faq_wrapper(question, user_id, model):
     # Gradio expects a non-generator for Interface
@@ -66,53 +143,74 @@ def faq_wrapper(question, user_id, model):
         result = chunk
     return result
 
+def elasticsearch_generate(natural_input):
+    return natural_to_query(natural_input)
+
+def elasticsearch_execute(query_body):
+    return execute_elasticsearch_query(query_body)
+
 with gr.Blocks() as demo:
-    gc.Markdown("# MCP Chatbot")
-    faq_input = gc.Textbox(label="Enter your question", lines=3)
-    model_selector = gc.Dropdown(
-        label="Select Model",
-        choices=["nova-micro", "nova-pro", "claude-haiku", "claude-sonnet"],
-        value="claude-sonnet",
-        interactive=True
-    )
-    # Generate random user ID for this session
-    session_user_id = str(uuid.uuid4())[:8]
-    faq_button = gc.Button("Process")
-    faq_output = gc.Textbox(label="Response", lines=10)
-    with gr.Row():  # type: ignore
-        thumbs_down = gc.Button("Report bad response", elem_id="thumbs-down", interactive=True)
-    feedback_msg = gc.Markdown(visible=False)
-
-    def report_bad_response():
-        return gr.update(value="Bad response reported. Thank you for your feedback.", visible=True), gr.update(interactive=False)
-
-    thumbs_down.click(report_bad_response, outputs=[feedback_msg, thumbs_down])
-
-    # Re-enable report button and clear feedback when a new FAQ is processed
-    def reset_feedback(*args):
-        return gr.update(interactive=True), gr.update(value="", visible=False)
-    faq_button.click(reset_feedback, outputs=[thumbs_down, feedback_msg], preprocess=False)
-    faq_button.click(lambda q, m: faq_wrapper(q, session_user_id, m), inputs=[faq_input, model_selector], outputs=faq_output)
+    gc.Markdown("# MCP Tools - Local Version")
+    with gr.Tab(label="FAQ"):  # type: ignore
+        faq_input = gc.Textbox(label="Enter your question", lines=3)
+        model_selector = gc.Dropdown(
+            label="Select Model",
+            choices=["nova-micro", "nova-pro", "claude-haiku", "claude-sonnet"],
+            value="claude-sonnet",
+            interactive=True
+        )
+        # Generate random user ID for this session
+        session_user_id = str(uuid.uuid4())[:8]
+        faq_button = gc.Button("Process")
+        faq_output = gc.Textbox(label="Response", lines=10)
+        with gr.Row():  # type: ignore
+            thumbs_down = gc.Button("Report bad response", elem_id="thumbs-down", interactive=True)
+        feedback_msg = gc.Markdown(visible=False)
+
+        def report_bad_response():
+            return gr.update(value="Bad response reported. Thank you for your feedback.", visible=True), gr.update(interactive=False)
+
+        thumbs_down.click(report_bad_response, outputs=[feedback_msg, thumbs_down])
+
+        # Re-enable report button and clear feedback when a new FAQ is processed
+        def reset_feedback(*args):
+            return gr.update(interactive=True), gr.update(value="", visible=False)
+        faq_button.click(reset_feedback, outputs=[thumbs_down, feedback_msg], preprocess=False)
+        faq_button.click(lambda q, m: faq_wrapper(q, session_user_id, m), inputs=[faq_input, model_selector], outputs=faq_output)
+    with gr.Tab(label="Elasticsearch"):  # type: ignore
+        gc.Markdown("### Step 1: Natural Language to Query")
+        natural_input = gc.Textbox(label="Describe what you want to search for", lines=3, placeholder="Example: Find all documents containing 'machine learning' in the title")
+        generate_button = gc.Button("Generate Query")
+        query_output = gc.Textbox(label="Generated Query Body", lines=10, placeholder="The generated Elasticsearch query will appear here")
+        generate_button.click(elasticsearch_generate, inputs=natural_input, outputs=query_output)
+        gc.Markdown("### Step 2: Execute Query")
+        gc.Markdown("You can modify the query above if needed, then click Execute")
+        execute_button = gc.Button("Execute Query")
+        result_output = gc.Textbox(label="Query Results", lines=10, placeholder="The query results will appear here")
+        execute_button.click(elasticsearch_execute, inputs=query_output, outputs=result_output)
 
 if __name__ == "__main__":
     # Check if running in Hugging Face Spaces
     is_spaces = os.getenv("SPACE_ID") is not None
     
-    # Configure launch parameters
+    # Configure launch parameters for Spaces
     if is_spaces:
-        # No auth in Hugging Face Spaces
         launch_params = {
             "server_name": "0.0.0.0",
             "server_port": int(os.getenv("PORT", 7860)),
             "share": False
         }
     else:
-        # Local development - no auth required
+        # Local development with auth
+        auth_username = GRADIO_AUTH_USERNAME
+        auth_password = GRADIO_AUTH_PASSWORD
         launch_params = {
-            "server_name": "0.0.0.0",
+            "server_name": "0.0.0.0", 
             "server_port": int(os.getenv("PORT", 7860)),
-            "share": True
+            "share": True,
+            "auth": (auth_username, auth_password),
+            "auth_message": "Please enter your credentials to access the application."
         }
     
     # Launch the app
-    demo.launch(**launch_params)
+    demo.launch(**launch_params)