Spaces:
Sleeping
Sleeping
File size: 7,017 Bytes
f39ba75 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
# agents/drug_info_agent.py
"""
Drug Information Agent - Handles drug-related queries using Generative AI.
"""
# agents/drug_info_agent.py
import re
from .agent_helpers import format_history_for_prompt
class DrugInfoAgent:
def __init__(self, gemini_model=None):
"""
Initializes the agent with the Gemini model.
"""
self.model = gemini_model
def _extract_drug_name(self, query: str) -> str:
"""
A simple helper to extract the drug name from the user's query.
This is a crucial step for this agent's reliability.
"""
# Remove common phrases and get the potential drug name
query_lower = query.lower()
patterns = [
"what are the side effects of", "tell me about", "information on",
"info on", "about the drug", "what is", "about"
]
cleaned_query = query_lower
for p in patterns:
cleaned_query = cleaned_query.replace(p, "")
# A simple assumption: the first significant word is the drug name.
# This can be improved with more advanced NLP in the future.
match = re.search(r'\b[a-zA-Z0-9]+\b', cleaned_query)
if match:
return match.group(0).title()
return ""
def process_query(self, query: str, file_context: str = "", chat_history: list = None):
"""
Processes a query to retrieve information about a specific drug.
"""
if not self.model:
return {'message': "π The pharmacy database is offline! The Gemini API key is missing.", 'agent_used': 'drug_info', 'status': 'error_no_api_key'}
# --- RESTORED LOGIC ---
drug_name = self._extract_drug_name(query)
if not drug_name:
# If the history also doesn't provide context, ask the user.
if "drug" not in str(chat_history).lower():
return {
'message': "Please tell me which drug you want to know about! For example, try 'info on Paracetamol'.",
'agent_used': 'drug_info',
'status': 'error_no_topic'
}
history_for_prompt = format_history_for_prompt(chat_history)
context_section = f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
prompt = f"""You are a cautious AI Pharmacist Tutor providing educational information like ancient india's Chanakya advisor to Chandragupta Maurya.
**CRITICAL SAFETY INSTRUCTION:** START EVERY RESPONSE with this disclaimer: "β οΈ **Disclaimer:** This information is for educational purposes ONLY and is not a substitute for professional medical advice."
Your reasoning process is:
1. Analyze the CONVERSATION HISTORY and CURRENT QUESTION to identify the specific drug being discussed. The primary drug to focus on is: **{drug_name}**.
2. If the user asks a follow-up (e.g., "what about its dosage forms?"), answer that specific question in the context of the drug.
3. Provide a structured summary.
CONVERSATION HISTORY:
{history_for_prompt}
{context_section}
CURRENT QUESTION:
User: {query}
Provide a structured summary for the drug **{drug_name}** including: Therapeutic Class, Mechanism of Action (MOA), Indications, Side Effects, and Warnings. DO NOT provide specific dosages.
"""
try:
response = self.model.generate_content(prompt)
return {'message': response.text, 'agent_used': 'drug_info', 'status': 'success'}
except Exception as e:
print(f"Drug Info Agent Error: {e}")
return {'message': f"Sorry, I couldn't access the drug database. An error occurred.", 'agent_used': 'drug_info', 'status': 'error_api_call'}
# def process_query(self, query: str, file_context: str = "", chat_history: list = None):
# """
# Processes a query to retrieve information about a specific drug.
# Args:
# query (str): The user's full query (e.g., "Tell me about Metformin").
# file_context (str): Optional context from uploaded files.
# Returns:
# dict: A dictionary containing the response message and agent metadata.
# """
# # Fallback response if the AI model is not configured
# if not self.model:
# return {
# 'message': "π **Drug Information Agent**\n\nThe pharmacy database is offline! The Gemini API key is missing, so I can't look up drug information. Please configure the API key to enable this feature.",
# 'agent_type': 'drug_info',
# 'status': 'error_no_api_key'
# }
# drug_name = self._extract_drug_name(query)
# if not drug_name:
# return {
# 'message': "Please tell me which drug you want to know about! For example, try 'info on Paracetamol'.",
# 'agent_type': 'drug_info',
# 'status': 'error_no_topic'
# }
# # Construct a specialized, safety-conscious prompt for the Gemini model
# prompt = f"""
# You are a highly knowledgeable and cautious AI Pharmacist Tutor. Your primary role is to provide accurate drug information for a B.Pharmacy student in India for EDUCATIONAL PURPOSES ONLY.
# **CRITICAL SAFETY INSTRUCTION:**
# START EVERY RESPONSE with the following disclaimer, exactly as written:
# "β οΈ **Disclaimer:** This information is for educational purposes ONLY and is not a substitute for professional medical advice. Always consult a qualified healthcare provider."
# **Task:**
# Provide a structured summary for the drug: **{drug_name}**
# **Information to Include:**
# 1. **Therapeutic Class:** What family of drugs does it belong to?
# 2. **Mechanism of Action (MOA):** How does it work in the body? Explain simply.
# 3. **Common Indications:** What is it typically used for?
# 4. **Common Side Effects:** List a few of the most common side effects.
# 5. **Important Contraindications/Warnings:** Who should not take this drug or be cautious?
# 6. **Common Dosage Forms:** What forms is it available in (e.g., Tablets, Syrup, Injection)? DO NOT provide specific dosages like mg or frequency.
# **Format:**
# Use clear headings (like "π¬ Mechanism of Action") and bullet points for readability. Use relevant emojis.
# """
# try:
# # Generate content using the AI model
# ai_response = self.model.generate_content(prompt, chat_history)
# return {
# 'message': ai_response.text,
# 'agent_used': 'drug_info',
# 'status': 'success'
# }
# except Exception as e:
# print(f"Drug Info Agent Error: {e}")
# return {
# 'message': f"I'm sorry, I couldn't access the drug database at the moment. An error occurred: {str(e)}",
# 'agent_type': 'drug_info',
# 'status': 'error_api_call'
# } |