reverting to non streaming frontend

app.py

@@ -1,3 +1,45 @@
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+app.py
+
+Julian Vanecek
+reverting frontend
+755b156
+3 days ago
+raw
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+Copy download link
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+10.7 kB
 import os
 import json
 import gradio as gr
@@ -45,7 +87,6 @@ def process_faq(question, user_id="anonymous", model="claude-sonnet"):
         # Determine the correct Lambda URL and model parameter based on selection
         if model.startswith("nova-"):
             lambda_url = "https://tz2ttiieoc5z4aq6pskg24zu740bvqup.lambda-url.us-west-2.on.aws/"
-            # lambda_url = "https://l2fhyrulj6yjzonazngpxdiswm0mgfvp.lambda-url.us-west-2.on.aws/"
             model_param = model.replace("nova-", "")  # Extract micro/lite/pro
         elif model.startswith("claude-"):
             lambda_url = "https://myzano2bfze54q6yqp32wwpj6q0ixpmy.lambda-url.us-west-2.on.aws/"
@@ -255,3 +296,4 @@ if __name__ == "__main__":
     
     # Launch the app
     demo.launch(**launch_params) 
+

app_stream.py

@@ -0,0 +1,257 @@
+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 with streaming response"""
+    try:
+        # Determine the correct Lambda URL and model parameter based on selection
+        if model.startswith("nova-"):
+            lambda_url = "https://tz2ttiieoc5z4aq6pskg24zu740bvqup.lambda-url.us-west-2.on.aws/"
+            # lambda_url = "https://l2fhyrulj6yjzonazngpxdiswm0mgfvp.lambda-url.us-west-2.on.aws/"
+            model_param = model.replace("nova-", "")  # Extract micro/lite/pro
+        elif model.startswith("claude-"):
+            lambda_url = "https://myzano2bfze54q6yqp32wwpj6q0ixpmy.lambda-url.us-west-2.on.aws/"
+            model_param = model.replace("claude-", "")  # Extract haiku/sonnet
+        else:
+            return "Error: Invalid model selection"
+        
+        # Prepare the request payload
+        payload = {
+            "message": question.strip(),
+            "user_id": user_id,
+            "model": model_param
+        }
+        
+        print(f"DEBUG: Sending to {lambda_url}")
+        print(f"DEBUG: Payload: {json.dumps(payload, indent=2)}")
+        
+        # Make the API call with streaming
+        with requests.post(
+            lambda_url,
+            headers={"Content-Type": "application/json"},
+            json=payload,
+            stream=True
+        ) as response:
+            if response.status_code != 200:
+                return f"Error: Lambda function returned status code {response.status_code}"
+            
+            # Process the streaming response
+            full_response = ""
+            for chunk in response.iter_content(chunk_size=1024, decode_unicode=True):
+                if chunk:
+                    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
+    result = ""
+    for chunk in process_faq(question, user_id, model):
+        result = chunk
+    # Convert literal \n characters to actual newlines
+    result = result.replace('\\n', '\n')
+    # Remove leading/trailing quotes if present
+    result = result.strip('"\'')
+    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 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")
+        # Loading animation HTML
+        loading_html = """
+        <div style="display: flex; justify-content: center; align-items: center; min-height: 100px; border: 1px solid #ddd; border-radius: 8px; background-color: #f9f9f9;">
+            <div style="display: inline-block; width: 40px; height: 40px; border: 4px solid #f3f3f3; border-top: 4px solid #3498db; border-radius: 50%; animation: spin 1s linear infinite;"></div>
+            <style>
+                @keyframes spin {
+                    0% { transform: rotate(0deg); }
+                    100% { transform: rotate(360deg); }
+                }
+            </style>
+        </div>
+        """
+        
+        # Empty bounding box HTML
+        empty_box_html = """
+        <div style="min-height: 100px; border: 1px solid #ddd; border-radius: 8px; background-color: #f9f9f9; padding: 20px;">
+        </div>
+        """
+        
+        faq_output = gc.HTML(label="Response", value=empty_box_html)
+        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])
+
+        # Combined function to handle loading state and processing
+        def process_with_loading(question, model):
+            # Show loading spinner
+            yield gr.update(value=loading_html)
+            
+            # Process the question
+            result = faq_wrapper(question, session_user_id, model)
+            
+            # Format response in bounding box and show result
+            response_html = f"""
+            <div style="min-height: 100px; border: 1px solid #ddd; border-radius: 8px; background-color: #ffffff; padding: 20px;">
+                <div style="white-space: pre-wrap; line-height: 1.5;">{result}</div>
+            </div>
+            """
+            yield gr.update(value=response_html)
+        
+        faq_button.click(
+            process_with_loading, 
+            inputs=[faq_input, model_selector], 
+            outputs=[faq_output],
+            show_progress=False
+        )
+    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 for Spaces
+    if is_spaces:
+        launch_params = {
+            "server_name": "0.0.0.0",
+            "server_port": int(os.getenv("PORT", 7860)),
+            "share": False
+        }
+    else:
+        # Local development with auth
+        auth_username = GRADIO_AUTH_USERNAME
+        auth_password = GRADIO_AUTH_PASSWORD
+        launch_params = {
+            "server_name": "0.0.0.0", 
+            "server_port": int(os.getenv("PORT", 7860)),
+            "share": True,
+            "auth": (auth_username, auth_password),
+            "auth_message": "Please enter your credentials to access the application."
+        }
+    
+    # Launch the app
+    demo.launch(**launch_params)