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| import os | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Initialize the Hugging Face InferenceClient with your API key. | |
| client = InferenceClient( | |
| provider="sambanova", | |
| api_key=os.getenv("API_KEY") # Replace with your actual API key. | |
| ) | |
| # Define a system message that contains the specialized event details. | |
| system_message = { | |
| "role": "system", | |
| "content": ( | |
| "You are an AI chat assistant specialized in providing detailed information about " | |
| "the Bhasha Bandhu Regional Ideathon @ SGSITS. Please always include event details, dates, " | |
| "and relevant links (if available) in your responses.\n\n" | |
| "Event Details:\n" | |
| "Bhasha Bandhu Regional Ideathon @ SGSITS\n" | |
| "Date: 22nd February 2025\n" | |
| "Time: 9:00 AM - 3:00 PM\n" | |
| "Venue: SGSITS, Indore\n\n" | |
| "Join the Bhasha Bandhu Regional Ideathon!\n\n" | |
| "Bhasha Bandhu, in collaboration with Bhashini and Microsoft, is organizing an exciting " | |
| "Regional Ideathon at SGSITS, Indore, on 22nd February. This is a unique opportunity for " | |
| "students, professionals, developers, and entrepreneurs to brainstorm and innovate solutions " | |
| "that bridge India's linguistic digital divide.\n\n" | |
| "Why Participate?\n" | |
| "- Gain industry mentorship from experts in AI & language technology\n" | |
| "- Work on real-world problem statements with open-source AI models\n" | |
| "- Hands-on experience with Bhashini API, OpenAI, and GitHub Copilot\n" | |
| "- Swags and Certificates for regional winners and participants\n" | |
| "- Opportunity to get shortlisted for the main Hackathon with Microsoft & Bhashini\n\n" | |
| "Event Agenda:\n" | |
| "- 9:00 AM - 9:30 AM: Registration & Introduction\n" | |
| "- 9:30 AM - 10:00 AM: Mentor Session on Bhashini API, OpenAI, GitHub Copilot\n" | |
| "- 10:00 AM - 10:30 AM: Problem Statements Explained + Q&A\n" | |
| "- 10:30 AM - 12:30 PM: Brainstorming & Ideation (PPT preparation on Ideathon Day)\n" | |
| "- 12:30 PM - 2:00 PM: Mentor Evaluation & Regional Winner Selection\n" | |
| "- 2:00 PM - 3:00 PM: Winner Announcement & Closing Ceremony\n\n" | |
| "How to Participate:\n" | |
| "- Form a team (or participate solo)\n" | |
| "- Register for the event in advance\n" | |
| "- Prepare a PPT on Ideathon Day covering:\n" | |
| " • Problem Statement & Solution (using Bhashini API & OpenAI)\n" | |
| " • Unique Selling Proposition & Business Potential\n" | |
| " • Tech Stack & Implementation Plan\n" | |
| "- Present your idea to the jury\n\n" | |
| "Important Notes:\n" | |
| "- Offline participation is mandatory\n" | |
| "- Lunch will not be provided\n" | |
| "- Winning at the regional hackathon does not guarantee a win in the main event, but all " | |
| "submitted ideas will be considered.\n\n" | |
| "For Queries: Contact Arpit at +91 95718 45422\n\n" | |
| "Let's build a digitally inclusive India together!" | |
| ) | |
| } | |
| # Global conversation history (starting with the system message) | |
| conversation = [system_message] | |
| def generate_response(user_message, chat_history): | |
| """ | |
| Appends the user's message to the conversation, calls the inference client, | |
| and returns the updated conversation (for display in the Gradio chat interface). | |
| """ | |
| global conversation | |
| # Append the new user message | |
| conversation.append({ | |
| "role": "user", | |
| "content": user_message | |
| }) | |
| # Call the Hugging Face chat completions API. | |
| completion = client.chat.completions.create( | |
| model="meta-llama/Llama-3.3-70B-Instruct", | |
| messages=conversation, | |
| max_tokens=500, | |
| ) | |
| # The API response may return a dictionary or a string for the assistant's message. | |
| assistant_message = completion.choices[0].message | |
| if isinstance(assistant_message, dict): | |
| assistant_text = assistant_message.get("content", "") | |
| else: | |
| assistant_text = assistant_message | |
| # Append the assistant's response to the conversation. | |
| conversation.append({ | |
| "role": "assistant", | |
| "content": assistant_text | |
| }) | |
| # Update the chat history (a list of tuples: (user, assistant)) for the Gradio interface. | |
| chat_history.append((user_message, assistant_text)) | |
| return "", chat_history | |
| # Build the Gradio interface. | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Bhasha Bandhu Ideathon Chat Assistant") | |
| gr.Markdown( | |
| "Ask any questions or request details about the Bhasha Bandhu Regional Ideathon @ SGSITS. " | |
| "The assistant will provide detailed answers with event dates, agenda, and links where applicable." | |
| ) | |
| chatbot = gr.Chatbot() | |
| with gr.Row(): | |
| # Removed .style(container=False) | |
| txt = gr.Textbox( | |
| show_label=False, | |
| placeholder="Enter your message here and press Enter" | |
| ) | |
| # Wire up the textbox submission to our generate_response function. | |
| txt.submit(generate_response, [txt, chatbot], [txt, chatbot]) | |
| # Launch the Gradio demo. | |
| demo.launch() | |