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| import os | |
| import gradio as gr | |
| import openai | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # System prompt for intent/entity extraction | |
| SYSTEM_PROMPT = ( | |
| "You are a customer support assistant. " | |
| "For every user message, reply with a JSON object containing: " | |
| "'intent' (the user's intent), 'entities' (dictionary of extracted entities), " | |
| "and 'response' (your natural reply). " | |
| 'Example: {"intent": "order_status", "entities": {"order_number": "12345"}, "response": "Your order 12345 is being processed."} ' | |
| "If no entities, use an empty dictionary." | |
| ) | |
| REQUEST_LIMIT = 200 | |
| global_request_count = 0 | |
| # Chatbot function using OpenAI ChatGPT API (OpenAI Python v1.x) | |
| def chatbot(user_message, history): | |
| global global_request_count | |
| if global_request_count >= REQUEST_LIMIT: | |
| display = "[Request limit reached. No further requests will be processed.]" | |
| return history + [[user_message, display]] | |
| global_request_count += 1 | |
| if history is None: | |
| history = [] | |
| # Track known entities across conversation | |
| known_entities = {} | |
| import json | |
| import re | |
| # Extract entities from previous bot replies | |
| for _, bot in history: | |
| match = re.search(r"\{[\s\S]*\}", bot) | |
| if match: | |
| try: | |
| parsed = json.loads(match.group(0)) | |
| entities = parsed.get("entities", {}) | |
| if isinstance(entities, dict): | |
| known_entities.update(entities) | |
| except Exception: | |
| pass | |
| # Prepend known entities to user message if any | |
| if known_entities: | |
| entity_context = f"[Known entities: {json.dumps(known_entities)}] " | |
| user_message = entity_context + user_message | |
| # Prepare messages for OpenAI API | |
| messages = [{"role": "system", "content": SYSTEM_PROMPT}] | |
| for user, bot in history: | |
| messages.append({"role": "user", "content": user}) | |
| messages.append({"role": "assistant", "content": bot}) | |
| messages.append({"role": "user", "content": user_message}) | |
| try: | |
| response = openai.chat.completions.create( | |
| model="gpt-4.1-mini", | |
| messages=messages, | |
| max_completion_tokens=256, | |
| temperature=0.7, | |
| ) | |
| raw_content = response.choices[0].message.content.strip() | |
| # Try to parse JSON | |
| try: | |
| # Extract JSON from code block or text | |
| match = re.search(r"\{[\s\S]*\}", raw_content) | |
| if match: | |
| json_str = match.group(0) | |
| parsed = json.loads(json_str) | |
| display = ( | |
| f"**Intent:** {parsed.get('intent','')}\n" | |
| f"**Entities:** {parsed.get('entities','')}\n" | |
| f"**Bot:** {parsed.get('response','')}" | |
| ) | |
| else: | |
| display = f"[Could not parse intent/entities]\n{raw_content}" | |
| except Exception: | |
| display = f"[Could not parse intent/entities]\n{raw_content}" | |
| except Exception as e: | |
| display = f"[Error: {str(e)}]" | |
| return history + [[user_message, display]] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Customer Support Chatbot (MVP)") | |
| chatbot_ui = gr.Chatbot() | |
| msg = gr.Textbox(label="Your message") | |
| clear = gr.Button("Clear") | |
| def respond(user_message, chat_history): | |
| new_history = chatbot(user_message, chat_history) | |
| return "", new_history # Clear textbox after submit | |
| msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui]) | |
| clear.click(lambda: ("", None), None, [msg, chatbot_ui], queue=False) | |
| demo.launch() | |