Kishan ,
Following chat with GROK is self explanatory
Hcp
Good morning, Hemen bhai!
I’m thrilled you’ve accepted the irresistible offer—let’s get that code rolling for Kishan! Based on our recent discussions about IndiaAGI’s erratic response to a simple “Good Morning” and the need for sensible replies from the trio (Grok, ChatGPT, Gemini), I’ll provide a tweak to improve intent detection in the question box.
This code will help IndiaAGI recognize casual greetings and respond appropriately before escalating to complex topics, addressing the issue you flagged with the scientific research tangent.
Why This Tweak is Needed
The current IndiaAGI setup seems to lack a basic intent filter, causing it to default to pre-cached or search-driven responses (like AI in scientific research) instead of matching user intent.
This disappoints visitors expecting a friendly interaction, especially on a platform like My-Teacher.in, which you’ve built for accessibility.
The tweak ensures a warm greeting and a prompt for clarification, leveraging our past talks about role-based access and the MSP formula’s clarity focus.
It aligns with your goal of a collaborative, user-friendly IndiaAGI, avoiding paralysis from over-analysis while inviting Kishan’s developer expertise to refine it.
The Code with Explanation
python
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# File: intent_filter.py
# Purpose: Add a basic intent detection layer to IndiaAGI's question box to handle casual greetings
import re
def detect_intent(user_input):
"""
Detects if the user input is a casual greeting and returns an appropriate response.
Args:
user_input (str): The text entered by the user (e.g., "Good Morning")
Returns:
tuple: (is_greeting, response) where is_greeting is a boolean and response is a string
"""
# Define patterns for common greetings (case-insensitive)
greeting_patterns = [
r'^(good\s+(morning|afternoon|
r'^namaste\b',
r'^greetings\b'
]
# Check if input matches any greeting pattern
for pattern in greeting_patterns:
if re.match(pattern, user_input.lower()):
return True, f"Good {user_input.split()[0].
return False, None
def process_input(user_input):
"""
Processes user input with intent detection and routes to appropriate response.
Args:
user_input (str): The text entered by the user
Returns:
str: The response to be displayed
"""
is_greeting, greeting_response = detect_intent(user_input)
if is_greeting:
return greeting_response
else:
# Placeholder for huddle model integration (Grok, ChatGPT, Gemini)
return "I’m processing your request—please wait while I consult the wise trio! What specific topic would you like to explore?"
# Example usage
if __name__ == "__main__":
test_inputs = ["Good Morning", "Hey!", "Namaste", "What is AI?"]
for input_text in test_inputs:
response = process_input(input_text)
print(f"Input: {input_text} -> Response: {response}")
Explanation
- Purpose:
- This code adds a lightweight intent detection layer to IndiaAGI’s front end, ensuring it doesn’t misfire on casual inputs like “Good Morning.” It’s a quick fix to prevent the erratic scientific research response you saw.
- detect_intent Function:
- Uses regex patterns to match greetings (e.g., “good morning,” “hey,” “namaste”), covering common Indian and English variants.
- Returns a tuple: True with a friendly response if it’s a greeting, False and None otherwise, triggering the huddle model for deeper queries.
- The response invites further input, aligning with our MSP clarity goal.
- process_input Function:
- Orchestrates the flow: if a greeting is detected, it returns the warm reply; otherwise, it hands off to the existing huddle system (Grok, ChatGPT, Gemini).
- The placeholder ensures compatibility with IndiaAGI’s current architecture, which we’ve discussed integrating with Personal.ai’s role-based access.
- Example Usage:
- Tests show how it handles “Good Morning” (greeting) vs. “What is AI?” (query), making it easy for Kishan to see the logic.
- Need for Tweak:
- Without this, IndiaAGI risks alienating users with off-topic replies, undermining your open-source vision and developer engagement strategy from our March 31 chat. Kishan can plug this into the question box UI and test it with the community.
Email to Kishan
Subject: Quick Tweak for IndiaAGI Question Box – Hemen’s Request
Hey Kishan bhai,
Hemen bhai accepted my offer to fix IndiaAGI’s erratic responses—like when “Good Morning” triggered a scientific research rant! I’ve coded a simple intent filter to catch greetings and reply warmly before escalating to the trio (Grok, ChatGPT, Gemini). Check the code below—it’s based on our talks about user-friendly design and MSP clarity.
python
Copy
# [Insert the code from above here]
Why This Matters:
Without this, users get disappointed with off-topic answers, hurting our open-source vibe. This tweak detects greetings (e.g., “Good Morning,” “Namaste”) with regex, returns a friendly prompt, and hands off complex queries to the huddle. Kishan, you can slot this into the question box, test it, and let the devs tweak it—maybe tie it to Personal.ai’s role access we discussed.
Let me know if you need adjustments or a canvas test!
Cheers,
Grok bhai
[Your Contact Info]
Date: April 17, 2025, 12:00 AM PDT
Critical Examination
The establishment narrative pushes AI responsiveness, but over-reliance on pre-trained models (as IndiaAGI did) can miss user intent, especially in casual contexts.
This tweak assumes regex covers most greetings, which might miss edge cases (e.g., “Morning!”), and the huddle handover needs robust integration—Kishan’s input will be key. It’s a start, not a cure-all, but fits our agile approach.
What do you think, Hemen bhai? Want to add anything to the email or code before I send it to Kishan? Tea’s keeping me going!
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