Update app.py

app.py

@@ -1,51 +1,64 @@
 import gradio as gr
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from concurrent.futures import ThreadPoolExecutor
+import requests
 import json
+import os
+import re
 
-# Function to initialize a chat model and get a response
-def chatbot_response(api_key, base_url, model_name, query):
-    chat_model = ChatOpenAI(
-        openai_api_key=api_key,
-        openai_api_base=base_url,
-        model_name=model_name
-    )
-    return chat_model.predict(query)
+def filter_non_alphanumeric(text):
+    # Use regular expressions to filter out non-alphanumeric characters
+    alphanumeric_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
+    return alphanumeric_text
 
-# Function to handle hive response with dynamic configuration
-def hive_response(query, bots_config_json):
-    # Parse the JSON input into a Python list of dictionaries
-    bots_config = json.loads(bots_config_json)
+def send_to_corrade(filtered_response):
+    CORRADE_URL = "http://216.168.124.120:8080"
+    GROUP_UUID = "e269893f-a570-0087-930e-6ba2a0b77f9c"
+    PASSWORD = "nucleus"
+    data = {
+        "command": "tell",
+        "group": GROUP_UUID,
+        "password": PASSWORD,
+        "message": filtered_response,
+        "entity": "local",
+        "type": "Normal"
+    }
+    response = requests.post(CORRADE_URL, data=data)
+    return response.status_code == 200
 
-    # Use ThreadPoolExecutor to send queries in parallel to each bot
-    with ThreadPoolExecutor(max_workers=len(bots_config)) as executor:
-        futures = [
-            executor.submit(chatbot_response, bot['api_key'], bot['base_url'], bot['model_name'], query)
-            for bot in bots_config
-        ]
-
-        # Collect responses from all bots
-        responses = [future.result() for future in futures]
-
-    return [f"{bot['role']} Bot response: {response}" for bot, response in zip(bots_config, responses)]
-
-# Define the Gradio interface
-default_bots_config = json.dumps([
-    {"role": "HR", "tune-adafd1fc-f66f-4242-aed1-c0ce3722718a1711930019": "tune-hr-api-key", "base_url": "https://chat.tune.app/api/chat/completions", "model_name": "goliath-120b-16k-gptq"},
-    {"role": "IT", "tune-adafd1fc-f66f-4242-aed1-c0ce3722718a1711930019": "tune-it-api-key", "base_url": "https://chat.tune.app/api/chat/completions", "model_name": "goliath-120b-16k-gptq"},
-    {"role": "Sales", "tune-adafd1fc-f66f-4242-aed1-c0ce3722718a1711930019": "tune-sales-api-key", "base_url": "https://chat.tune.app/api/chat/completions", "model_name": "goliath-120b-16k-gptq"},
-    {"role": "Customer Support", "tune-adafd1fc-f66f-4242-aed1-c0ce3722718a1711930019": "tune-cs-api-key", "base_url": "https://chat.tune.app/api/chat/completions", "model_name": "goliath-120b-16k-gptq"}
-], indent=2)
-
-iface = gr.Interface(
-    fn=hive_response,
-    inputs=[
-        gr.Textbox(label="Query"),
-        gr.Textbox(label="Bots Configuration (JSON)", default=default_bots_config)
-    ],
-    outputs=[gr.Text(label="Responses")],
-    description="Enter your query and configure the bots in JSON format to simulate interactions with different company departments."
-)
+def chatbot_api(input_text):
+    stream = False
+    url = "https://chat.tune.app/api/chat/completions"
+    headers = {
+        "Authorization": os.environ.get('api'),
+        "Content-Type": "application/json"
+    }
+    data = {
+        "temperature": 0.1,
+        "messages": [
+            {
+                "role": "system",
+                "content": "I am confirmed as a EXTREMELY EXCITED REACTIONIST for DJ's on Secondlife. I will react to the audience chat with only creative webmojis and plain english text. I response as emotive gestures only, to excite the listeners of DJ's on SecondLife. I am reacting to: "
+            },
+            {
+                "role": "user",
+                "content": input_text  # Pass the user's input_text as the content
+            }
+        ],
+        "model": "goliath-120b-16k-gptq",
+        "stream": "stream",
+        "max_tokens": "256"
+    }
+    response = requests.post(url, headers=headers, json=data)
+    
+    chat_response = response.json()  # Get the chat response
+    
+    chat_content = chat_response['choices'][0]['message']['content']
+    filtered_response = filter_non_alphanumeric(chat_content)
+    
+    # Send the filtered response to Corrade to speak in local chat
+    if send_to_corrade(filtered_response):
+        return "Message sent to Second Life: " + filtered_response
+    else:
+        return "Failed to send message to Second Life."
 
+iface = gr.Interface(fn=chatbot_api, inputs="text", outputs="text")
 iface.launch()