Update app.py

app.py

@@ -3,46 +3,31 @@ import requests
 import json
 import datetime
 import os
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv(dotenv_path=os.path.join(os.path.dirname(__file__), ".env"))
-
-# Nebius API configuration
-NEBIUS_API_URL = os.getenv("NEBIUS_API_URL")
-NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")
 
+# Nebius API configuration (hardcoded)
+NEBIUS_API_URL = "https://api.studio.nebius.ai/v1/chat/completions"
+NEBIUS_API_KEY = "eyJhbGciOiJIUzI1NiIsImtpZCI6IlV6SXJWd1h0dnprLVRvdzlLZWstc0M1akptWXBvX1VaVkxUZlpnMDRlOFUiLCJ0eXAiOiJKV1QifQ.eyJzdWIiOiJnb29nbGUtb2F1dGgyfDExMDkwNDYwNzI2NjMxOTY2NDYyMSIsInNjb3BlIjoib3BlbmlkIG9mZmxpbmVfYWNjZXNzIiwiaXNzIjoiYXBpX2tleV9pc3N1ZXIiLCJhdWQiOlsiaHR0cHM6Ly9uZWJpdXMtaW5mZXJlbmNlLmV1LmF1dGgwLmNvbS9hcGkvdjIvIl0sImV4cCI6MTkwNjc4ODk3OSwidXVpZCI6IjBiMDc5OGI4LTdkZjctNDcxMi05ZTY0LTZiNmU5OTk0OWRmNyIsIm5hbWUiOiJNQ1AgU0VSVkVSIiwiZXhwaXJlc19hdCI6IjIwMzAtMDYtMDRUMDc6MzY6MTkrMDAwMCJ9.-RG1eCxfuO9bqmTa00pHCAb6L47IWEFHVxq3xqHrjU8"
 
 # --- MCP Protocol Support ---
-# This is a placeholder for MCP integration. In a real scenario, you would use the MCP protocol to wrap/unwrap requests and responses.
 def mcp_supported_call(payload, endpoint, headers):
-    # Here, you could add MCP-specific headers or payload structure if needed
-    # For now, this just passes through to the Nebius API
     response = requests.post(endpoint, json=payload, headers=headers)
     return response
 
-
-# Function to call Nebius API directly (now MCP supported)
 def call_nebius_api(query, context_data=""):
     try:
-        # Prepare payload for Nebius API
         nebius_payload = {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "messages": [{"role": "user", "content": query}],
             "max_tokens": 1000,
             "temperature": 0.7,
         }
-
-        # Call Nebius API
         headers = {
             "Authorization": f"Bearer {NEBIUS_API_KEY}",
             "Content-Type": "application/json",
         }
         response = mcp_supported_call(nebius_payload, NEBIUS_API_URL, headers)
-
         if response.status_code != 200:
             return f"Error: Nebius API request failed - {response.text}"
-
         nebius_response = response.json()
         result = (
             nebius_response.get("choices", [{}])[0]
@@ -50,12 +35,9 @@ def call_nebius_api(query, context_data=""):
             .get("content", "No response")
         )
         return result
-
     except Exception as e:
         return f"Error: {str(e)}"
 
-
-# Function to humanize AI text using another API call with a different model (now MCP supported)
 def humanize_text(ai_response):
     try:
         humanize_prompt = f"""Please rewrite the following AI response to make it sound more natural, conversational, and human-like. 
@@ -65,39 +47,28 @@ def humanize_text(ai_response):
         {ai_response}
 
         Humanized version:"""
-
-        # Use DeepSeek model for humanization - excellent at conversational and creative responses
         nebius_payload = {
-            "model": "deepseek-ai/DeepSeek-R1",  # DeepSeek model for humanization
+            "model": "deepseek-ai/DeepSeek-R1",
             "messages": [{"role": "user", "content": humanize_prompt}],
             "max_tokens": 1200,
-            "temperature": 0.9,  # Higher temperature for more creative/human-like responses
+            "temperature": 0.9,
         }
-
         headers = {
             "Authorization": f"Bearer {NEBIUS_API_KEY}",
             "Content-Type": "application/json",
         }
         response = mcp_supported_call(nebius_payload, NEBIUS_API_URL, headers)
-
         if response.status_code != 200:
-            return ai_response  # Return original response if humanization fails
-
+            return ai_response
         nebius_response = response.json()
         humanized_result = (
             nebius_response.get("choices", [{}])[0]
             .get("message", {})
             .get("content", ai_response)
         )
-        # Only return the humanized response, not the prompt or any instructions
-        # Remove everything before the first line break if the model echoes the prompt or instructions
         if "Humanized version:" in humanized_result:
-            humanized_result = humanized_result.split("Humanized version:", 1)[
-                -1
-            ].strip()
-        # Remove any leading prompt/instruction lines (e.g., if model repeats the prompt or says what it's doing)
+            humanized_result = humanized_result.split("Humanized version:", 1)[-1].strip()
         lines = humanized_result.splitlines()
-        # Remove lines that look like instructions or meta-comments
         filtered_lines = [
             line
             for line in lines
@@ -118,43 +89,30 @@ def humanize_text(ai_response):
                 )
             )
         ]
-        # Join the remaining lines, strip leading/trailing whitespace
         cleaned = "\n".join(filtered_lines).strip()
-        # If nothing left after cleaning, fall back to the original humanized_result
         return cleaned if cleaned else humanized_result
-
     except Exception as e:
-        return ai_response  # Return original response if humanization fails
-
+        return ai_response
 
-# --- Additional Functionality ---
 def save_conversation(query, ai_response, humanized_response, context_data):
-    """Save the conversation to a local file with timestamp."""
     timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     with open("conversation_history.txt", "a", encoding="utf-8") as f:
         f.write(
             f"[{timestamp}]\nQuery: {query}\nContext: {context_data}\nAI Response: {ai_response}\nHumanized: {humanized_response}\n{'-' * 40}\n"
         )
 
-
 def clear_history():
-    """Clear the conversation history file."""
     open("conversation_history.txt", "w").close()
     return "History cleared."
 
-
 def load_history():
-    """Load the conversation history file."""
     try:
         with open("conversation_history.txt", "r", encoding="utf-8") as f:
             return f.read()
     except FileNotFoundError:
         return "No history found."
 
-
-# --- More Advanced Features ---
 def export_history_to_file(filename="conversation_export.txt"):
-    """Export the conversation history to a user-specified file."""
     try:
         with (
             open("conversation_history.txt", "r", encoding="utf-8") as src,
@@ -165,9 +123,7 @@ def export_history_to_file(filename="conversation_export.txt"):
     except Exception as e:
         return f"Export failed: {e}"
 
-
 def search_history(keyword):
-    """Search for a keyword in the conversation history."""
     try:
         with open("conversation_history.txt", "r", encoding="utf-8") as f:
             lines = f.readlines()
@@ -176,14 +132,12 @@ def search_history(keyword):
     except FileNotFoundError:
         return "No history found."
 
-
 def delete_last_conversation():
-    """Delete the last conversation from the history file."""
     try:
         with open("conversation_history.txt", "r", encoding="utf-8") as f:
             content = f.read().strip().split("-" * 40)
         if len(content) > 1:
-            content = content[:-1]  # Remove last conversation
+            content = content[:-1]
             with open("conversation_history.txt", "w", encoding="utf-8") as f:
                 f.write(("-" * 40).join(content).strip())
             return "Last conversation deleted."
@@ -193,28 +147,18 @@ def delete_last_conversation():
     except FileNotFoundError:
         return "No history found."
 
-
-# Gradio interface function
 def gradio_interface(query, context_data, humanize=False, save=False):
     if not query.strip():
         return "Please enter a query.", "", load_history()
-
-    # Get initial AI response
     ai_response = call_nebius_api(query, context_data)
-
-    # If humanization is enabled and we got a valid response, humanize it
     if humanize and not ai_response.startswith("Error:"):
         humanized_response = humanize_text(ai_response)
     else:
         humanized_response = ""
-
     if save:
         save_conversation(query, ai_response, humanized_response, context_data)
-
     return ai_response, humanized_response, load_history()
 
-
-# Create Gradio UI
 def create_gradio_app():
     with gr.Blocks() as demo:
         gr.Markdown("# MCP-Powered Chatbot with Nebius API & Text Humanization")
@@ -265,7 +209,6 @@ def create_gradio_app():
                 search_result = gr.Textbox(
                     label="Search Results", value="", lines=5, interactive=False
                 )
-        # Add event handlers for new features
         submit_button.click(
             fn=gradio_interface,
             inputs=[query_input, context_input, humanize_checkbox, save_checkbox],
@@ -303,15 +246,14 @@ def create_gradio_app():
 
     return demo
 
-
 if __name__ == "__main__":
     print("Starting Gradio Interface...")
     try:
         demo = create_gradio_app()
         print("Gradio app created successfully")
         demo.launch(
-            server_name="127.0.0.1",  # Changed to localhost only
-            server_port=7870,  # Changed back to 7860 to avoid conflicts
+            server_name="127.0.0.1",
+            server_port=7870,
             share=False,
             debug=True,
             show_error=True,
@@ -319,5 +261,4 @@ if __name__ == "__main__":
     except Exception as e:
         print(f"Error launching Gradio app: {e}")
         import traceback
-
-        traceback.print_exc()
+        traceback.print_exc()