| # import sys | |
| # import re | |
| # import json | |
| # import os | |
| # from datasets import Dataset | |
| # from transformers import ( | |
| # DistilBertTokenizerFast, | |
| # DistilBertForSequenceClassification, | |
| # Trainer, | |
| # TrainingArguments, | |
| # pipeline | |
| # ) | |
| # import gradio as gr | |
| # # --- Training Section --- | |
| # def train_model(): | |
| # # Create small custom dataset | |
| # data = { | |
| # "train": [ | |
| # # Greet (label 0) | |
| # {"text": "hi", "label": 0}, {"text": "hello", "label": 0}, {"text": "hey there", "label": 0}, | |
| # {"text": "good morning", "label": 0}, {"text": "greetings", "label": 0}, {"text": "hi bot", "label": 0}, | |
| # {"text": "hello jusbook", "label": 0}, {"text": "hey", "label": 0}, {"text": "start chat", "label": 0}, | |
| # {"text": "yo", "label": 0}, | |
| # # Services (label 1) | |
| # {"text": "what services do you offer", "label": 1}, {"text": "list services", "label": 1}, | |
| # {"text": "what can I book", "label": 1}, {"text": "services available", "label": 1}, | |
| # {"text": "tell me about your services", "label": 1}, {"text": "what do you provide", "label": 1}, | |
| # {"text": "service list", "label": 1}, {"text": "offerings", "label": 1}, | |
| # {"text": "jusbook services", "label": 1}, {"text": "what's offered", "label": 1}, | |
| # # Contact (label 2) | |
| # {"text": "contact info", "label": 2}, {"text": "how to reach you", "label": 2}, | |
| # {"text": "support contact", "label": 2}, {"text": "email address", "label": 2}, | |
| # {"text": "phone number", "label": 2}, {"text": "address", "label": 2}, | |
| # {"text": "get in touch", "label": 2}, {"text": "contact details", "label": 2}, | |
| # {"text": "support info", "label": 2}, {"text": "how to contact", "label": 2}, | |
| # # Available slots (label 3) | |
| # {"text": "available slots", "label": 3}, {"text": "booking times", "label": 3}, | |
| # {"text": "what slots are free", "label": 3}, {"text": "show available bookings", "label": 3}, | |
| # {"text": "free slots", "label": 3}, {"text": "appointment times", "label": 3}, | |
| # {"text": "available appointments", "label": 3}, {"text": "slots for booking", "label": 3}, | |
| # {"text": "when can I book", "label": 3}, {"text": "open slots", "label": 3}, | |
| # # Book slot (label 4) | |
| # {"text": "book a slot", "label": 4}, {"text": "reserve monday 10am", "label": 4}, | |
| # {"text": "book tuesday", "label": 4}, {"text": "schedule appointment", "label": 4}, | |
| # {"text": "reserve a time", "label": 4}, {"text": "book now", "label": 4}, | |
| # {"text": "make a booking", "label": 4}, {"text": "slot reservation", "label": 4}, | |
| # {"text": "book wednesday 9am", "label": 4}, {"text": "reserve slot", "label": 4}, | |
| # # Broadcast (label 5) | |
| # {"text": "broadcast slots", "label": 5}, {"text": "announce available slots", "label": 5}, | |
| # {"text": "slot broadcast", "label": 5}, {"text": "share slots", "label": 5}, | |
| # {"text": "announce bookings", "label": 5}, {"text": "broadcast info", "label": 5}, | |
| # {"text": "publicize slots", "label": 5}, {"text": "slot announcement", "label": 5}, | |
| # {"text": "tell everyone about slots", "label": 5}, {"text": "broadcast", "label": 5}, | |
| # # Upcoming (label 6) | |
| # {"text": "upcoming events", "label": 6}, {"text": "future bookings", "label": 6}, | |
| # {"text": "what's coming up", "label": 6}, {"text": "upcoming appointments", "label": 6}, | |
| # {"text": "events soon", "label": 6}, {"text": "next events", "label": 6}, | |
| # {"text": "upcoming info", "label": 6}, {"text": "future events", "label": 6}, | |
| # {"text": "what bookings are upcoming", "label": 6}, {"text": "events list", "label": 6}, | |
| # ] | |
| # } | |
| # dataset = Dataset.from_dict({ | |
| # "text": [item["text"] for item in data["train"]], | |
| # "label": [item["label"] for item in data["train"]] | |
| # }) | |
| # tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| # model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=7) | |
| # def tokenize(batch): | |
| # return tokenizer(batch["text"], padding=True, truncation=True, return_tensors="pt") | |
| # dataset = dataset.map(tokenize, batched=True) | |
| # dataset.set_format("torch", columns=["input_ids", "attention_mask", "label"]) | |
| # training_args = TrainingArguments( | |
| # output_dir="./results", | |
| # num_train_epochs=3, | |
| # per_device_train_batch_size=8, | |
| # logging_steps=10, | |
| # save_steps=50, | |
| # evaluation_strategy="no", | |
| # learning_rate=5e-5, | |
| # ) | |
| # trainer = Trainer(model=model, args=training_args, train_dataset=dataset) | |
| # trainer.train() | |
| # model.save_pretrained("./trained_model") | |
| # tokenizer.save_pretrained("./trained_model") | |
| # # --- Chatbot Class --- | |
| # class Chatbot: | |
| # def __init__(self): | |
| # model_path = os.path.abspath("trained_model") | |
| # self.model = DistilBertForSequenceClassification.from_pretrained(model_path) | |
| # self.tokenizer = DistilBertTokenizerFast.from_pretrained(model_path) | |
| # self.classifier = pipeline("text-classification", model=self.model, tokenizer=self.tokenizer) | |
| # self.intent_map = { | |
| # "LABEL_0": "greet", | |
| # "LABEL_1": "services", | |
| # "LABEL_2": "contact", | |
| # "LABEL_3": "available_slots", | |
| # "LABEL_4": "book_slot", | |
| # "LABEL_5": "broadcast", | |
| # "LABEL_6": "upcoming" | |
| # } | |
| # self.services = ["Legal Consultation", "Document Review", "Appointment Booking", "Virtual Meetings"] | |
| # self.contact_info = {"Email": "support@jusbook.com", "Phone": "+1-123-456-7890", "Address": "123 Justice Lane, Law City, USA"} | |
| # self.available_slots = ["Monday 10AM-12PM", "Tuesday 2PM-4PM", "Wednesday 9AM-11AM"] | |
| # self.upcoming_events = ["Webinar on Legal Rights: Sept 20, 2025", "Free Consultation Day: Oct 1, 2025"] | |
| # self.booked_slots = {} | |
| # def preprocess(self, text): | |
| # return text.lower().strip() | |
| # def get_response(self, user_input): | |
| # processed = self.preprocess(user_input) | |
| # prediction = self.classifier(processed)[0] | |
| # intent = self.intent_map.get(prediction["label"], "unknown") | |
| # if intent == "greet": | |
| # return "Hello! Welcome to jusbook.com. How can I help you today?" | |
| # elif intent == "services": | |
| # return "We offer:\n" + "\n".join(f"- {s}" for s in self.services) | |
| # elif intent == "contact": | |
| # return "Contact:\n" + "\n".join(f"- {k}: {v}" for k, v in self.contact_info.items()) | |
| # elif intent == "available_slots": | |
| # return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "book_slot": | |
| # match = re.search(r'(monday|tuesday|wednesday)\s+(\d+am|\d+pm)-(\d+am|\d+pm)', processed) | |
| # if match: | |
| # slot = f"{match.group(1).capitalize()} {match.group(2).upper()}-{match.group(3).upper()}" | |
| # if slot in self.available_slots: | |
| # self.available_slots.remove(slot) | |
| # self.booked_slots["user"] = slot | |
| # return f"Booked: {slot}. Confirmed!" | |
| # return "Slot not available." | |
| # return "Specify slot, e.g., 'book Monday 10AM-12PM'." | |
| # elif intent == "broadcast": | |
| # return "Broadcast: Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "upcoming": | |
| # return "Upcoming:\n" + "\n".join(f"- {e}" for e in self.upcoming_events) | |
| # else: | |
| # return "Sorry, didn't understand. Ask about services, slots, etc." | |
| # # --- Gradio App --- | |
| # def run_gradio(): | |
| # bot = Chatbot() | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("# 🤖 JusBook Chatbot") | |
| # chatbot_ui = gr.Chatbot() | |
| # msg = gr.Textbox(placeholder="Type your message here...") | |
| # clear = gr.Button("Clear Chat") | |
| # def respond(message, chat_history): | |
| # response = bot.get_response(message) | |
| # chat_history.append((message, response)) | |
| # return "", chat_history | |
| # msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui]) | |
| # clear.click(lambda: None, None, chatbot_ui, queue=False) | |
| # demo.launch() | |
| # # --- Main Execution --- | |
| # if __name__ == "__main__": | |
| # if len(sys.argv) > 1 and sys.argv[1] == "--train": | |
| # train_model() | |
| # else: | |
| # run_gradio() | |
| # import sys | |
| # import re | |
| # import json | |
| # import os | |
| # from datasets import Dataset | |
| # from transformers import ( | |
| # DistilBertTokenizerFast, | |
| # DistilBertForSequenceClassification, | |
| # pipeline | |
| # ) | |
| # import gradio as gr | |
| # import torch # Add this import | |
| # # --- Chatbot Class (No Training Here) --- | |
| # class Chatbot: | |
| # def __init__(self): | |
| # # Load from Hub: Replace 'your-username/fine-tuned-jusbook-model' with your pushed model | |
| # # For now, use a base model; fine-tune and push for custom intents | |
| # model_name = "distilbert-base-uncased" # Or your fine-tuned repo | |
| # self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # self.model = DistilBertForSequenceClassification.from_pretrained( | |
| # model_name, num_labels=7 | |
| # ).to(self.device) | |
| # self.tokenizer = DistilBertTokenizerFast.from_pretrained(model_name) | |
| # self.model.eval() # Inference mode | |
| # self.classifier = pipeline( | |
| # "text-classification", | |
| # model=self.model, | |
| # tokenizer=self.tokenizer, | |
| # device=0 if self.device.type == 'cuda' else -1 # -1 for CPU | |
| # ) | |
| # self.intent_map = { | |
| # "LABEL_0": "greet", | |
| # "LABEL_1": "services", | |
| # "LABEL_2": "contact", | |
| # "LABEL_3": "available_slots", | |
| # "LABEL_4": "book_slot", | |
| # "LABEL_5": "broadcast", | |
| # "LABEL_6": "upcoming" | |
| # } | |
| # self.services = ["Legal Consultation", "Document Review", "Appointment Booking", "Virtual Meetings"] | |
| # self.contact_info = {"Email": "support@jusbook.com", "Phone": "+1-123-456-7890", "Address": "123 Justice Lane, Law City, USA"} | |
| # self.available_slots = ["Monday 10AM-12PM", "Tuesday 2PM-4PM", "Wednesday 9AM-11AM"] | |
| # self.upcoming_events = ["Webinar on Legal Rights: Sept 20, 2025", "Free Consultation Day: Oct 1, 2025"] | |
| # self.booked_slots = {} | |
| # def preprocess(self, text): | |
| # return text.lower().strip() | |
| # def get_response(self, user_input): | |
| # try: | |
| # processed = self.preprocess(user_input) | |
| # prediction = self.classifier(processed)[0] | |
| # intent = self.intent_map.get(prediction["label"], "unknown") | |
| # if intent == "greet": | |
| # return "Hello! Welcome to jusbook.com. How can I help you today?" | |
| # elif intent == "services": | |
| # return "We offer:\n" + "\n".join(f"- {s}" for s in self.services) | |
| # elif intent == "contact": | |
| # return "Contact:\n" + "\n".join(f"- {k}: {v}" for k, v in self.contact_info.items()) | |
| # elif intent == "available_slots": | |
| # return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "book_slot": | |
| # match = re.search(r'(monday|tuesday|wednesday)\s+(\d+am|\d+pm)-(\d+am|\d+pm)', processed) | |
| # if match: | |
| # slot = f"{match.group(1).capitalize()} {match.group(2).upper()}-{match.group(3).upper()}" | |
| # if slot in self.available_slots: | |
| # self.available_slots.remove(slot) | |
| # self.booked_slots["user"] = slot | |
| # return f"Booked: {slot}. Confirmed!" | |
| # return "Slot not available." | |
| # return "Specify slot, e.g., 'book Monday 10AM-12PM'." | |
| # elif intent == "broadcast": | |
| # return "Broadcast: Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "upcoming": | |
| # return "Upcoming:\n" + "\n".join(f"- {e}" for e in self.upcoming_events) | |
| # else: | |
| # return "Sorry, didn't understand. Ask about services, slots, etc." | |
| # except Exception as e: | |
| # print(f"Error in get_response: {e}") # Logs to Space console | |
| # return "Sorry, something went wrong. Try rephrasing!" | |
| # # --- Gradio App --- | |
| # def run_gradio(): | |
| # bot = Chatbot() # Pre-load here | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("# 🤖 JusBook Chatbot") | |
| # chatbot_ui = gr.Chatbot() | |
| # msg = gr.Textbox(placeholder="Type your message here...") | |
| # clear = gr.Button("Clear Chat") | |
| # def respond(message, chat_history): | |
| # response = bot.get_response(message) | |
| # chat_history.append((message, response)) | |
| # return "", chat_history | |
| # msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui]) | |
| # clear.click(lambda: None, None, chatbot_ui, queue=False) | |
| # demo.queue() # Enable queue for Spaces | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # # --- Main Execution --- | |
| # if __name__ == "__main__": | |
| # run_gradio() | |
| # import sys | |
| # import re | |
| # import os | |
| # import torch | |
| # import gradio as gr | |
| # from datasets import Dataset | |
| # from transformers import ( | |
| # DistilBertTokenizerFast, | |
| # DistilBertForSequenceClassification, | |
| # Trainer, | |
| # TrainingArguments, | |
| # pipeline | |
| # ) | |
| # # --- Training Data --- | |
| # def get_dataset(): | |
| # data = { | |
| # "train": [ | |
| # # Greet (label 0) | |
| # {"text": "hi", "label": 0}, {"text": "hello", "label": 0}, {"text": "hey there", "label": 0}, | |
| # {"text": "good morning", "label": 0}, {"text": "greetings", "label": 0}, {"text": "hi bot", "label": 0}, | |
| # {"text": "hello jusbook", "label": 0}, {"text": "hey", "label": 0}, {"text": "start chat", "label": 0}, | |
| # {"text": "yo", "label": 0}, {"text": "helo", "label": 0}, {"text": "hii", "label": 0}, | |
| # # Services (label 1) | |
| # {"text": "what services do you offer", "label": 1}, {"text": "list services", "label": 1}, | |
| # {"text": "what can I book", "label": 1}, {"text": "services available", "label": 1}, | |
| # {"text": "tell me about your services", "label": 1}, {"text": "what do you provide", "label": 1}, | |
| # {"text": "service list", "label": 1}, {"text": "offerings", "label": 1}, | |
| # {"text": "jusbook services", "label": 1}, {"text": "what's offered", "label": 1}, | |
| # {"text": "srvices", "label": 1}, {"text": "what u offer", "label": 1}, | |
| # # Contact (label 2) | |
| # {"text": "contact info", "label": 2}, {"text": "how to reach you", "label": 2}, | |
| # {"text": "support contact", "label": 2}, {"text": "email address", "label": 2}, | |
| # {"text": "phone number", "label": 2}, {"text": "address", "label": 2}, | |
| # {"text": "get in touch", "label": 2}, {"text": "contact details", "label": 2}, | |
| # {"text": "support info", "label": 2}, {"text": "how to contact", "label": 2}, | |
| # {"text": "cntact", "label": 2}, {"text": "email?", "label": 2}, | |
| # # Available slots (label 3) | |
| # {"text": "available slots", "label": 3}, {"text": "booking times", "label": 3}, | |
| # {"text": "what slots are free", "label": 3}, {"text": "show available bookings", "label": 3}, | |
| # {"text": "free slots", "label": 3}, {"text": "appointment times", "label": 3}, | |
| # {"text": "available appointments", "label": 3}, {"text": "slots for booking", "label": 3}, | |
| # {"text": "when can I book", "label": 3}, {"text": "open slots", "label": 3}, | |
| # {"text": "free slot?", "label": 3}, {"text": "slots plz", "label": 3}, | |
| # # Book slot (label 4) | |
| # {"text": "book a slot", "label": 4}, {"text": "reserve monday 10am-12pm", "label": 4}, | |
| # {"text": "book tuesday", "label": 4}, {"text": "schedule appointment", "label": 4}, | |
| # {"text": "reserve a time", "label": 4}, {"text": "book now", "label": 4}, | |
| # {"text": "make a booking", "label": 4}, {"text": "slot reservation", "label": 4}, | |
| # {"text": "book wednesday 9am-11am", "label": 4}, {"text": "reserve slot", "label": 4}, | |
| # {"text": "book mon 10am", "label": 4}, {"text": "reserve tues 2pm", "label": 4}, | |
| # # Broadcast (label 5) | |
| # {"text": "broadcast slots", "label": 5}, {"text": "announce available slots", "label": 5}, | |
| # {"text": "slot broadcast", "label": 5}, {"text": "share slots", "label": 5}, | |
| # {"text": "announce bookings", "label": 5}, {"text": "broadcast info", "label": 5}, | |
| # {"text": "publicize slots", "label": 5}, {"text": "slot announcement", "label": 5}, | |
| # {"text": "tell everyone about slots", "label": 5}, {"text": "broadcast", "label": 5}, | |
| # {"text": "announce slot", "label": 5}, {"text": "share slot plz", "label": 5}, | |
| # # Upcoming (label 6) | |
| # {"text": "upcoming events", "label": 6}, {"text": "future bookings", "label": 6}, | |
| # {"text": "what's coming up", "label": 6}, {"text": "upcoming appointments", "label": 6}, | |
| # {"text": "events soon", "label": 6}, {"text": "next events", "label": 6}, | |
| # {"text": "upcoming info", "label": 6}, {"text": "future events", "label": 6}, | |
| # {"text": "what bookings are upcoming", "label": 6}, {"text": "events list", "label": 6}, | |
| # {"text": "whats next", "label": 6}, {"text": "upcoming plz", "label": 6} | |
| # ] | |
| # } | |
| # return Dataset.from_dict({ | |
| # "text": [item["text"] for item in data["train"]], | |
| # "label": [item["label"] for item in data["train"]] | |
| # }) | |
| # # --- Model Training --- | |
| # def train_model(): | |
| # dataset = get_dataset() | |
| # tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| # model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=7) | |
| # def tokenize(batch): | |
| # return tokenizer(batch["text"], padding=True, truncation=True, return_tensors="pt") | |
| # dataset = dataset.map(tokenize, batched=True) | |
| # dataset.set_format("torch", columns=["input_ids", "attention_mask", "label"]) | |
| # training_args = TrainingArguments( | |
| # output_dir="./results", | |
| # num_train_epochs=3, | |
| # per_device_train_batch_size=8, | |
| # logging_steps=10, | |
| # save_steps=50, | |
| # evaluation_strategy="no", | |
| # learning_rate=5e-5, | |
| # ) | |
| # trainer = Trainer(model=model, args=training_args, train_dataset=dataset) | |
| # trainer.train() | |
| # model.save_pretrained("./trained_model") | |
| # tokenizer.save_pretrained("./trained_model") | |
| # print("Model trained and saved to ./trained_model") | |
| # return "✅ Model training completed! Saved to ./trained_model" | |
| # # --- Model Loading --- | |
| # def load_model(): | |
| # model_path = "./trained_model" # Use fine-tuned model if available | |
| # if not os.path.exists(model_path): | |
| # model_path = "distilbert-base-uncased" # Fallback to base model | |
| # print(f"⚠️ Using base model (no trained model found at {model_path})") | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # model = DistilBertForSequenceClassification.from_pretrained(model_path, num_labels=7).to(device) | |
| # tokenizer = DistilBertTokenizerFast.from_pretrained(model_path) | |
| # model.eval() | |
| # classifier = pipeline( | |
| # "text-classification", | |
| # model=model, | |
| # tokenizer=tokenizer, | |
| # device=0 if device.type == 'cuda' else -1 | |
| # ) | |
| # return classifier, device | |
| # # --- Rule-Based Fallback --- | |
| # def rule_based_classify(text): | |
| # text_lower = text.lower() | |
| # if any(word in text_lower for word in ["hi", "hello", "hey", "greet", "helo", "hii"]): | |
| # return 0 # greet | |
| # elif any(word in text_lower for word in ["service", "offer", "book", "list", "srvices"]): | |
| # return 1 # services | |
| # elif any(word in text_lower for word in ["contact", "email", "phone", "address", "cntact"]): | |
| # return 2 # contact | |
| # elif any(word in text_lower for word in ["slot", "available", "time", "appointment"]): | |
| # return 3 # available_slots | |
| # elif "book" in text_lower or "reserve" in text_lower: | |
| # return 4 # book_slot | |
| # elif any(word in text_lower for word in ["broadcast", "announce", "share"]): | |
| # return 5 # broadcast | |
| # elif any(word in text_lower for word in ["upcoming", "event", "future", "next"]): | |
| # return 6 # upcoming | |
| # return -1 # unknown | |
| # # --- Intent Classification --- | |
| # def classify_intent(text): | |
| # try: | |
| # classifier, _ = load_model() | |
| # with torch.no_grad(): | |
| # prediction = classifier(text)[0] | |
| # label = int(prediction["label"].split("_")[1]) | |
| # return label | |
| # except Exception as e: | |
| # print(f"Model error: {e}, using rule-based fallback") | |
| # return rule_based_classify(text) # Fallback | |
| # # --- Response Logic --- | |
| # class ChatbotResponses: | |
| # def __init__(self): | |
| # self.intent_map = { | |
| # 0: "greet", 1: "services", 2: "contact", 3: "available_slots", | |
| # 4: "book_slot", 5: "broadcast", 6: "upcoming" | |
| # } | |
| # self.services = ["Legal Consultation", "Document Review", "Appointment Booking", "Virtual Meetings"] | |
| # self.contact_info = {"Email": "support@jusbook.com", "Phone": "+1-123-456-7890", "Address": "123 Justice Lane, Law City, USA"} | |
| # self.available_slots = ["Monday 10AM-12PM", "Tuesday 2PM-4PM", "Wednesday 9AM-11AM"] | |
| # self.upcoming_events = ["Webinar on Legal Rights: Sept 20, 2025", "Free Consultation Day: Oct 1, 2025"] | |
| # self.booked_slots = {} | |
| # def get_response(self, user_input, intent_label): | |
| # intent = self.intent_map.get(intent_label, "unknown") | |
| # processed = user_input.lower().strip() | |
| # if intent == "greet": | |
| # return "Hello! Welcome to jusbook.com. How can I help you today?" | |
| # elif intent == "services": | |
| # return "We offer:\n" + "\n".join(f"- {s}" for s in self.services) | |
| # elif intent == "contact": | |
| # return "Contact:\n" + "\n".join(f"- {k}: {v}" for k, v in self.contact_info.items()) | |
| # elif intent == "available_slots": | |
| # return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "broadcast": | |
| # return "🔊 **Broadcast Message**: Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # elif intent == "upcoming": | |
| # return "Upcoming Events:\n" + "\n".join(f"- {e}" for e in self.upcoming_events) | |
| # elif intent == "book_slot": | |
| # pattern = r'(monday|tuesday|wednesday)\s+(\d+am?|\d+pm?)-(\d+am?|\d+pm?)' | |
| # match = re.search(pattern, processed, re.IGNORECASE) | |
| # if match: | |
| # day = match.group(1).capitalize() | |
| # start = match.group(2).upper() | |
| # end = match.group(3).upper() | |
| # slot = f"{day} {start}-{end}" | |
| # if slot in self.available_slots: | |
| # self.available_slots.remove(slot) | |
| # self.booked_slots["user"] = slot | |
| # return f"✅ **Booking Confirmed!**\n\n**Slot**: {slot}\n**Status**: Reserved for you!" | |
| # return f"❌ **Slot Unavailable**\n\nThe requested slot '{slot}' is no longer available." | |
| # # Show available slots and prompt for specific booking | |
| # return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) + "\n\n**To book**: Type 'book [day] [time]', e.g., 'book Monday 10AM-12PM'" | |
| # else: | |
| # return "Sorry, I didn't understand that. You can ask me about:\n- Services\n- Contact info\n- Available slots\n- Booking a slot\n- Upcoming events\n\nTry something like 'hello', 'services', or 'book Monday 10AM-12PM'" | |
| # # Global chatbot instance | |
| # bot = ChatbotResponses() | |
| # # --- Testing Function --- | |
| # def test_chatbot(): | |
| # """Run comprehensive tests for the chatbot""" | |
| # test_cases = [ | |
| # ("hello", 0, "Hello! Welcome to jusbook.com"), | |
| # ("what services", 1, "We offer:\n- Legal Consultation"), | |
| # ("contact info", 2, "Contact:\n- Email: support@jusbook.com"), | |
| # ("available slots", 3, "Available slots:\n- Monday 10AM-12PM"), | |
| # ("book monday 10am-12pm", 4, "Booking Confirmed"), | |
| # ("broadcast", 5, "Broadcast Message"), | |
| # ("upcoming events", 6, "Upcoming Events"), | |
| # ("random nonsense", -1, "Sorry, I didn't understand") | |
| # ] | |
| # results = [] | |
| # for input_text, expected_intent, expected_response_contains in test_cases: | |
| # try: | |
| # intent = classify_intent(input_text) | |
| # response = bot.get_response(input_text, intent) | |
| # intent_pass = "✅" if intent == expected_intent else "❌" | |
| # response_pass = "✅" if expected_response_contains in response else "❌" | |
| # results.append({ | |
| # "Input": input_text, | |
| # "Expected Intent": expected_intent, | |
| # "Got Intent": intent, | |
| # "Intent Pass": intent_pass, | |
| # "Response": response[:100] + "..." if len(response) > 100 else response, | |
| # "Response Pass": response_pass | |
| # }) | |
| # except Exception as e: | |
| # results.append({ | |
| # "Input": input_text, | |
| # "Error": str(e), | |
| # "Status": "❌ Failed" | |
| # }) | |
| # # Print results | |
| # print("\n" + "="*80) | |
| # print("🧪 CHATBOT TEST RESULTS") | |
| # print("="*80) | |
| # all_passed = True | |
| # for result in results: | |
| # if "Error" in result: | |
| # print(f"❌ {result['Input']}: {result['Error']}") | |
| # all_passed = False | |
| # else: | |
| # intent_status = result["Intent Pass"] | |
| # response_status = result["Response Pass"] | |
| # status = "✅" if intent_status == "✅" and response_status == "✅" else "⚠️" | |
| # print(f"{status} '{result['Input']}' → Intent: {result['Got Intent']} ({intent_status}) | Response: {response_status}") | |
| # if intent_status != "✅" or response_status != "✅": | |
| # all_passed = False | |
| # if all_passed: | |
| # print("\n🎉 All tests passed! The chatbot is ready to use.") | |
| # else: | |
| # print("\n⚠️ Some tests failed. Check the results above.") | |
| return results | |
| # --- Gradio Chat Function --- | |
| # def chat_response(message, history): | |
| # """Generate chatbot response for Gradio interface""" | |
| # if not message.strip(): | |
| # return history, "" | |
| # try: | |
| # # Classify intent | |
| # intent = classify_intent(message) | |
| # # Generate response | |
| # response = bot.get_response(message, intent) | |
| # # Add to history | |
| # history.append([message, response]) | |
| # return history, "" | |
| # except Exception as e: | |
| # error_msg = f"Sorry, something went wrong: {str(e)}" | |
| # history.append([message, error_msg]) | |
| # return history, "" | |
| # # --- Gradio Interface --- | |
| # def create_gradio_interface(): | |
| # """Create the main Gradio interface""" | |
| # with gr.Blocks(title="JusBook Chatbot", theme=gr.themes.Soft()) as demo: | |
| # gr.Markdown( | |
| # """ | |
| # # 🤖 **JusBook Chatbot** | |
| # *Your legal appointment assistant* | |
| # Ask me about: | |
| # - Available services | |
| # - Contact information | |
| # - Booking slots | |
| # - Upcoming events | |
| # **Try:** "hello", "services", "available slots", or "book Monday 10AM-12PM" | |
| # """ | |
| # ) | |
| # # Chat interface | |
| # chatbot = gr.Chatbot( | |
| # height=500, | |
| # show_label=False, | |
| # avatar_images=("user-avatar.png", "bot-avatar.png") | |
| # ) | |
| # msg = gr.Textbox( | |
| # placeholder="Type your message here...", | |
| # container=True, | |
| # label="Your Message", | |
| # scale=7 | |
| # ) | |
| # with gr.Row(): | |
| # submit_btn = gr.Button("Send", variant="primary", scale=1) | |
| # clear_btn = gr.Button("Clear Chat", scale=1) | |
| # test_btn = gr.Button("Run Tests", scale=1) | |
| # Status messages | |
| # status = gr.Textbox(label="Status", interactive=False) | |
| # # Event handlers | |
| # def submit_message(message, history): | |
| # return chat_response(message, history) | |
| # def clear_chat(): | |
| # return [], "" | |
| # def run_tests(): | |
| # results = test_chatbot() | |
| # # Convert results to chat format for display | |
| # test_history = [] | |
| # for result in results[:5]: # Show first 5 tests | |
| # if "Error" not in result: | |
| # test_msg = f"**Test:** {result['Input']}\n**Intent:** {result['Got Intent']} ({result['Intent Pass']})\n**Response:** {result['Response'][:50]}..." | |
| # test_history.append([test_msg, ""]) | |
| # return test_history, "🧪 Test results displayed above. Check console for full output." | |
| # msg.submit(submit_message, [msg, chatbot], [chatbot, msg]) | |
| # submit_btn.click(submit_message, [msg, chatbot], [chatbot, msg]) | |
| # clear_btn.click(clear_chat, None, [chatbot, msg]) | |
| # test_btn.click(run_tests, None, [chatbot, status]) | |
| # # Add examples | |
| # gr.Examples( | |
| # examples=[ | |
| # ["hello"], | |
| # ["what services do you offer?"], | |
| # ["show me available slots"], | |
| # ["book Monday 10AM-12PM"], | |
| # ["contact information"], | |
| # ["upcoming events"], | |
| # ["broadcast available slots"] | |
| # ], | |
| # inputs=msg, | |
| # label="Quick Examples" | |
| # ) | |
| # return demo | |
| # # --- Training Interface --- | |
| # def create_training_interface(): | |
| # """Create Gradio interface for model training""" | |
| # with gr.Blocks(title="Model Training") as demo: | |
| # gr.Markdown("# 🔄 **Model Training Dashboard**") | |
| # gr.Markdown("Train your custom DistilBERT model for the JusBook chatbot") | |
| # with gr.Row(): | |
| # train_btn = gr.Button("🚀 Start Training", variant="primary", size="lg") | |
| # status = gr.Textbox(label="Training Status", interactive=False, lines=3) | |
| # # Training output | |
| # training_log = gr.Textbox(label="Training Log", lines=10, interactive=False) | |
| # def run_training(): | |
| # try: | |
| # log_content = [] | |
| # def log_callback(msg): | |
| # log_content.append(msg) | |
| # return "\n".join(log_content) | |
| # print("Starting model training...") | |
| # status_msg = train_model() | |
| # log_content.append(status_msg) | |
| # training_log.update(value="\n".join(log_content)) | |
| # status.update(value="✅ Training completed successfully!") | |
| # return log_content, "✅ Training completed successfully!" | |
| # except Exception as e: | |
| # error_msg = f"❌ Training failed: {str(e)}" | |
| # status.update(value=error_msg) | |
| # return [error_msg], error_msg | |
| # train_btn.click( | |
| # run_training, | |
| # outputs=[training_log, status] | |
| # ) | |
| # gr.Markdown("### 📊 Dataset Info") | |
| # gr.Markdown(f""" | |
| # **Training Examples:** {len(get_dataset())} | |
| # **Intents:** 7 (Greet, Services, Contact, Slots, Booking, Broadcast, Upcoming) | |
| # **Model:** DistilBERT-base (~66M parameters) | |
| # **Epochs:** 3 | |
| # """) | |
| # return demo | |
| # # --- Main Execution --- | |
| # def main(): | |
| # if len(sys.argv) > 1: | |
| # if sys.argv[1] == "--train": | |
| # print("🔄 Starting model training...") | |
| # train_model() | |
| # elif sys.argv[1] == "--test": | |
| # print("🧪 Running chatbot tests...") | |
| # test_chatbot() | |
| # else: | |
| # print("❓ Unknown command. Use --train or --test") | |
| # sys.exit(1) | |
| # else: | |
| # # Launch Gradio interface | |
| # demo = create_gradio_interface() | |
| # demo.launch( | |
| # share=True, # For public sharing | |
| # server_name="0.0.0.0", | |
| # server_port=7860, | |
| # show_error=True | |
| # ) | |
| # if __name__ == "__main__": | |
| # main() | |
| import sys | |
| import re | |
| import os | |
| import torch | |
| import gradio as gr | |
| from datasets import Dataset | |
| from transformers import ( | |
| DistilBertTokenizerFast, | |
| DistilBertForSequenceClassification, | |
| Trainer, | |
| TrainingArguments, | |
| pipeline | |
| ) | |
| # --- Training Data --- (unchanged, your full dataset here) | |
| def get_dataset(): | |
| data = { | |
| "train": [ | |
| # Greet (label 0) | |
| {"text": "hi", "label": 0}, {"text": "hello", "label": 0}, {"text": "hey there", "label": 0}, | |
| {"text": "good morning", "label": 0}, {"text": "greetings", "label": 0}, {"text": "hi bot", "label": 0}, | |
| {"text": "hello jusbook", "label": 0}, {"text": "hey", "label": 0}, {"text": "start chat", "label": 0}, | |
| {"text": "yo", "label": 0}, {"text": "helo", "label": 0}, {"text": "hii", "label": 0}, | |
| # Services (label 1) | |
| {"text": "what services do you offer", "label": 1}, {"text": "list services", "label": 1}, | |
| {"text": "what can I book", "label": 1}, {"text": "services available", "label": 1}, | |
| {"text": "tell me about your services", "label": 1}, {"text": "what do you provide", "label": 1}, | |
| {"text": "service list", "label": 1}, {"text": "offerings", "label": 1}, | |
| {"text": "jusbook services", "label": 1}, {"text": "what's offered", "label": 1}, | |
| {"text": "srvices", "label": 1}, {"text": "what u offer", "label": 1}, | |
| # Contact (label 2) | |
| {"text": "contact info", "label": 2}, {"text": "how to reach you", "label": 2}, | |
| {"text": "support contact", "label": 2}, {"text": "email address", "label": 2}, | |
| {"text": "phone number", "label": 2}, {"text": "address", "label": 2}, | |
| {"text": "get in touch", "label": 2}, {"text": "contact details", "label": 2}, | |
| {"text": "support info", "label": 2}, {"text": "how to contact", "label": 2}, | |
| {"text": "cntact", "label": 2}, {"text": "email?", "label": 2}, | |
| # Available slots (label 3) | |
| {"text": "available slots", "label": 3}, {"text": "booking times", "label": 3}, | |
| {"text": "what slots are free", "label": 3}, {"text": "show available bookings", "label": 3}, | |
| {"text": "free slots", "label": 3}, {"text": "appointment times", "label": 3}, | |
| {"text": "available appointments", "label": 3}, {"text": "slots for booking", "label": 3}, | |
| {"text": "when can I book", "label": 3}, {"text": "open slots", "label": 3}, | |
| {"text": "free slot?", "label": 3}, {"text": "slots plz", "label": 3}, | |
| # Book slot (label 4) | |
| {"text": "book a slot", "label": 4}, {"text": "reserve monday 10am-12pm", "label": 4}, | |
| {"text": "book tuesday", "label": 4}, {"text": "schedule appointment", "label": 4}, | |
| {"text": "reserve a time", "label": 4}, {"text": "book now", "label": 4}, | |
| {"text": "make a booking", "label": 4}, {"text": "slot reservation", "label": 4}, | |
| {"text": "book wednesday 9am-11am", "label": 4}, {"text": "reserve slot", "label": 4}, | |
| {"text": "book mon 10am", "label": 4}, {"text": "reserve tues 2pm", "label": 4}, | |
| # Broadcast (label 5) | |
| {"text": "broadcast slots", "label": 5}, {"text": "announce available slots", "label": 5}, | |
| {"text": "slot broadcast", "label": 5}, {"text": "share slots", "label": 5}, | |
| {"text": "announce bookings", "label": 5}, {"text": "broadcast info", "label": 5}, | |
| {"text": "publicize slots", "label": 5}, {"text": "slot announcement", "label": 5}, | |
| {"text": "tell everyone about slots", "label": 5}, {"text": "broadcast", "label": 5}, | |
| {"text": "announce slot", "label": 5}, {"text": "share slot plz", "label": 5}, | |
| # Upcoming (label 6) | |
| {"text": "upcoming events", "label": 6}, {"text": "future bookings", "label": 6}, | |
| {"text": "what's coming up", "label": 6}, {"text": "upcoming appointments", "label": 6}, | |
| {"text": "events soon", "label": 6}, {"text": "next events", "label": 6}, | |
| {"text": "upcoming info", "label": 6}, {"text": "future events", "label": 6}, | |
| {"text": "what bookings are upcoming", "label": 6}, {"text": "events list", "label": 6}, | |
| {"text": "whats next", "label": 6}, {"text": "upcoming plz", "label": 6} | |
| ] | |
| } | |
| return Dataset.from_dict({ | |
| "text": [item["text"] for item in data["train"]], | |
| "label": [item["label"] for item in data["train"]] | |
| }) | |
| # --- Model Training --- (unchanged) | |
| def train_model(): | |
| dataset = get_dataset() | |
| tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=7) | |
| def tokenize(batch): | |
| return tokenizer(batch["text"], padding=True, truncation=True, return_tensors="pt") | |
| dataset = dataset.map(tokenize, batched=True) | |
| dataset.set_format("torch", columns=["input_ids", "attention_mask", "label"]) | |
| training_args = TrainingArguments( | |
| output_dir="./results", | |
| num_train_epochs=3, | |
| per_device_train_batch_size=8, | |
| logging_steps=10, | |
| save_steps=50, | |
| evaluation_strategy="no", | |
| learning_rate=5e-5, | |
| ) | |
| trainer = Trainer(model=model, args=training_args, train_dataset=dataset) | |
| trainer.train() | |
| model.save_pretrained("./trained_model") | |
| tokenizer.save_pretrained("./trained_model") | |
| print("Model trained and saved to ./trained_model") | |
| return "✅ Model training completed! Saved to ./trained_model" | |
| # --- Model Loading --- (unchanged) | |
| def load_model(): | |
| model_path = "./trained_model" | |
| if not os.path.exists(model_path): | |
| model_path = "distilbert-base-uncased" | |
| print(f"⚠️ Using base model (train first for better accuracy!)") | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model = DistilBertForSequenceClassification.from_pretrained(model_path, num_labels=7).to(device) | |
| tokenizer = DistilBertTokenizerFast.from_pretrained(model_path) | |
| model.eval() | |
| classifier = pipeline( | |
| "text-classification", | |
| model=model, | |
| tokenizer=tokenizer, | |
| device=0 if device.type == 'cuda' else -1 | |
| ) | |
| return classifier, device | |
| # --- FIXED Rule-Based Fallback --- (Better keywords, first priority) | |
| def rule_based_classify(text): | |
| text_lower = text.lower() | |
| print(f"DEBUG: Rule classify input: '{text_lower}'") # For debugging | |
| if any(word in text_lower for word in ["hi", "hello", "hey", "greet", "helo", "hii", "good morning"]): | |
| print("DEBUG: Matched greet") | |
| return 0 | |
| elif any(word in text_lower for word in ["service", "offer", "list", "srvices", "what can i"]): | |
| print("DEBUG: Matched services") | |
| return 1 | |
| elif any(word in text_lower for word in ["contact", "email", "phone", "address", "cntact", "support"]): | |
| print("DEBUG: Matched contact") | |
| return 2 | |
| elif any(word in text_lower for word in ["available", "free slot", "open slot", "when can i book"]): | |
| print("DEBUG: Matched available_slots") | |
| return 3 | |
| elif any(word in text_lower for word in ["book", "reserve", "schedule", "booked slots", "my booking", "slots booked"]): | |
| print("DEBUG: Matched book_slot") | |
| return 4 | |
| elif any(word in text_lower for word in ["broadcast", "announce", "share"]): | |
| print("DEBUG: Matched broadcast") | |
| return 5 | |
| elif any(word in text_lower for word in ["upcoming", "event", "future", "next", "show my bookings"]): | |
| print("DEBUG: Matched upcoming") | |
| return 6 | |
| print("DEBUG: No match, unknown") | |
| return -1 | |
| def classify_intent(text): | |
| # Try rules first (reliable without training) | |
| rule_intent = rule_based_classify(text) | |
| if rule_intent != -1: | |
| print(f"DEBUG: Using rule-based intent: {rule_intent}") | |
| return rule_intent | |
| # Fallback to model | |
| try: | |
| classifier, _ = load_model() | |
| with torch.no_grad(): | |
| prediction = classifier(text)[0] | |
| label_str = prediction["label"] | |
| label = int(label_str.split("_")[1]) if "_" in label_str else int(label_str) | |
| print(f"DEBUG: Model predicted: {label_str} (score: {prediction['score']:.2f})") | |
| return label | |
| except Exception as e: | |
| print(f"DEBUG: Model error: {e}, using rules") | |
| return rule_based_classify(text) | |
| # --- FIXED Response Logic --- (Better booking handling, show booked slots) | |
| class ChatbotResponses: | |
| def __init__(self): | |
| self.intent_map = { | |
| 0: "greet", 1: "services", 2: "contact", 3: "available_slots", | |
| 4: "book_slot", 5: "broadcast", 6: "upcoming" | |
| } | |
| self.services = ["Legal Consultation", "Document Review", "Appointment Booking", "Virtual Meetings"] | |
| self.contact_info = {"Email": "support@jusbook.com", "Phone": "+1-123-456-7890", "Address": "123 Justice Lane, Law City, USA"} | |
| self.available_slots = ["Monday 10AM-12PM", "Tuesday 2PM-4PM", "Wednesday 9AM-11AM"] | |
| self.upcoming_events = ["Webinar on Legal Rights: Sept 20, 2025", "Free Consultation Day: Oct 1, 2025"] | |
| self.booked_slots = {} # e.g., {"user": "Monday 10AM-12PM"} | |
| def get_response(self, user_input, intent_label): | |
| intent = self.intent_map.get(intent_label, "unknown") | |
| processed = user_input.lower().strip() | |
| print(f"DEBUG: Intent: {intent}, Input: {processed}") # Debug | |
| if intent == "greet": | |
| return "Hello! Welcome to jusbook.com. How can I help you today?" | |
| elif intent == "services": | |
| return "We offer:\n" + "\n".join(f"- {s}" for s in self.services) | |
| elif intent == "contact": | |
| return "Contact:\n" + "\n".join(f"- {k}: {v}" for k, v in self.contact_info.items()) | |
| elif intent == "available_slots": | |
| return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| elif intent == "broadcast": | |
| return "🔊 **Broadcast Message**: Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| elif intent == "upcoming": | |
| booked_info = "\n".join(f"- {user}: {slot}" for user, slot in self.booked_slots.items()) if self.booked_slots else "No upcoming bookings yet." | |
| return f"Upcoming Events:\n" + "\n".join(f"- {e}" for e in self.upcoming_events) + f"\n\nYour Bookings:\n{booked_info}" | |
| elif intent == "book_slot": | |
| # Enhanced regex for booking | |
| book_pattern = r'book\s+(monday|tuesday|wednesday)\s+(\d+am?|\d+pm?)-(\d+am?|\d+pm?)' | |
| match = re.search(book_pattern, processed, re.IGNORECASE) | |
| if match: | |
| day = match.group(1).capitalize() | |
| start = match.group(2).upper() | |
| end = match.group(3).upper() | |
| slot = f"{day} {start}-{end}" | |
| if slot in self.available_slots: | |
| self.available_slots.remove(slot) | |
| self.booked_slots["user"] = slot # Simple user tracking | |
| return f"✅ **Booking Confirmed!**\n\n**Slot**: {slot}\n**Status**: Reserved for you!" | |
| return f"❌ **Slot Unavailable**\n\nThe requested slot '{slot}' is no longer available." | |
| # If no specific book, check if query is about booked slots | |
| if any(word in processed for word in ["booked", "my booking", "slots booked"]): | |
| if self.booked_slots: | |
| booked_info = "\n".join(f"- {user}: {slot}" for user, slot in self.booked_slots.items()) | |
| return f"Your Booked Slots:\n{booked_info}\n\nAvailable: " + "\n".join(f"- {s}" for s in self.available_slots) | |
| return "No slots booked yet. Available:\n" + "\n".join(f"- {s}" for s in self.available_slots) | |
| # Default prompt | |
| return "Available slots:\n" + "\n".join(f"- {s}" for s in self.available_slots) + "\n\n**To book**: Type 'book [day] [time]', e.g., 'book Monday 10AM-12PM'" | |
| else: | |
| return "Sorry, I didn't understand that. You can ask me about:\n- Services\n- Contact info\n- Available slots\n- Booking a slot (e.g., 'book Monday 10AM-12PM')\n- Upcoming events\n- Your booked slots" | |
| # Global instance | |
| bot = ChatbotResponses() | |
| # --- UPDATED Testing Function --- (Added your failing cases) | |
| def test_chatbot(): | |
| test_cases = [ | |
| ("hello", 0, "Hello! Welcome"), | |
| ("contact info", 2, "Contact:\n- Email"), | |
| ("available slots", 3, "Available slots"), | |
| ("book monday 10am-12pm", 4, "Booking Confirmed"), | |
| ("slots booked", 4, "Your Booked Slots"), # New: Should match booking intent | |
| ("booked slots", 4, "No slots booked yet"), # New | |
| ("broadcast", 5, "Broadcast Message"), | |
| ("upcoming events", 6, "Upcoming Events"), | |
| ("random text", -1, "Sorry, I didn't understand") | |
| ] | |
| results = [] | |
| for input_text, expected_intent, expected_response_contains in test_cases: | |
| intent = classify_intent(input_text) | |
| response = bot.get_response(input_text, intent) | |
| intent_pass = "✅" if intent == expected_intent else "❌" | |
| response_pass = "✅" if expected_response_contains in response else "❌" | |
| results.append({ | |
| "Input": input_text, | |
| "Expected Intent": expected_intent, | |
| "Got Intent": intent, | |
| "Intent Pass": intent_pass, | |
| "Response": response[:100] + "..." if len(response) > 100 else response, | |
| "Response Pass": response_pass | |
| }) | |
| print(f"Test '{input_text}': Intent {intent} ({intent_pass}), Response has '{expected_response_contains}' ({response_pass})") | |
| all_passed = all(r["Intent Pass"] == "✅" and r["Response Pass"] == "✅" for r in results) | |
| print(f"\n🎉 {'All' if all_passed else 'Some failed - check above'} tests passed!") | |
| return results | |
| # --- Gradio Chat Function --- (unchanged, but with debug prints) | |
| def chat_response(message, history): | |
| if not message.strip(): | |
| return history, "" | |
| print(f"\n--- New Message: {message} ---") # Debug separator | |
| try: | |
| intent = classify_intent(message) | |
| response = bot.get_response(message, intent) | |
| history.append([message, response]) | |
| print(f"Response: {response[:50]}...") # Debug | |
| return history, "" | |
| except Exception as e: | |
| error_msg = f"Sorry, something went wrong: {str(e)}" | |
| history.append([message, error_msg]) | |
| return history, "" | |
| # --- Gradio Interface --- (Added debug console link) | |
| def create_gradio_interface(): | |
| with gr.Blocks(title="JusBook Chatbot", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🤖 **JusBook Chatbot** (Debug Mode)") | |
| gr.Markdown("*Check browser console for DEBUG logs*") | |
| chatbot = gr.Chatbot(height=500) | |
| msg = gr.Textbox(placeholder="Type your message here...", label="Your Message") | |
| with gr.Row(): | |
| submit_btn = gr.Button("Send", variant="primary") | |
| clear_btn = gr.Button("Clear Chat") | |
| test_btn = gr.Button("Run Tests") | |
| status = gr.Textbox(label="Status", interactive=False) | |
| def submit_message(message, history): | |
| return chat_response(message, history) | |
| def clear_chat(): | |
| return [], "" | |
| def run_tests(): | |
| results = test_chatbot() | |
| test_history = [[r["Input"], f"Intent: {r['Got Intent']} | Pass: {r['Intent Pass'] + r['Response Pass']}"] for r in results] | |
| return test_history, f"🧪 Tests complete! All passed: {all(r['Intent Pass'] == '✅' and r['Response Pass'] == '✅' for r in results)}" | |
| msg.submit(submit_message, [msg, chatbot], [chatbot, msg]) | |
| submit_btn.click(submit_message, [msg, chatbot], [chatbot, msg]) | |
| clear_btn.click(clear_chat, None, [chatbot, msg]) | |
| test_btn.click(run_tests, None, [chatbot, status]) | |
| gr.Examples( | |
| examples=[ | |
| ["hello"], ["contact info"], ["available slots"], ["book Monday 10AM-12PM"], | |
| ["slots booked"], ["booked slots"], ["upcoming events"] | |
| ], | |
| inputs=msg, | |
| label="Test These (Including Your Failing Ones)" | |
| ) | |
| return demo | |
| # --- Main Execution --- | |
| def main(): | |
| if len(sys.argv) > 1: | |
| if sys.argv[1] == "--train": | |
| print("🔄 Training...") | |
| train_model() | |
| elif sys.argv[1] == "--test": | |
| print("🧪 Testing...") | |
| test_chatbot() | |
| else: | |
| print("Usage: python app.py [--train | --test]") | |
| sys.exit(1) | |
| else: | |
| demo = create_gradio_interface() | |
| demo.launch(share=True, server_port=7860, show_error=True) | |
| if __name__ == "__main__": | |
| main() |