Create app.py

app.py

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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="deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+        messages=conversation,
+        max_tokens=5000,
+    )
+
+    # 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():
+        txt = gr.Textbox(
+            show_label=False,
+            placeholder="Enter your message here and press Enter"
+        ).style(container=False)
+
+    # Wire up the textbox submission to our generate_response function.
+    txt.submit(generate_response, [txt, chatbot], [txt, chatbot])
+
+# Launch the Gradio demo.
+demo.launch()