Spaces:
Runtime error
Runtime error
File size: 7,914 Bytes
9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 5d0a351 9844436 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | import re
from typing import Dict, List, Any, Optional
from google import genai
from google.genai import types
from app.core.config import settings
import logging
logger = logging.getLogger(__name__)
class GeminiService:
def __init__(self):
self.client = None
self._initialize_client()
def _initialize_client(self):
try:
if settings.gemini_api_key:
self.client = genai.Client(api_key=settings.gemini_api_key)
logger.info("Gemini client initialized successfully")
else:
logger.warning("Gemini API key not provided")
except Exception as e:
logger.error(f"Failed to initialize Gemini client: {str(e)}")
def generateSearchQueryFromCase(self, caseFacts: str, geminiModel=None, verbose: bool = False) -> str:
if not self.client:
raise ValueError("Gemini client not initialized")
prompt = f"""
You are a legal assistant for a retrieval system based on Indian criminal law.
Given the case facts below, generate a **concise and focused search query** with **only the most relevant legal keywords**. These should include:
- Specific **IPC sections**
- Core **legal concepts** (e.g., "right of private defence", "criminal breach of trust")
- **Crime type** (e.g., "assault", "corruption")
- Any relevant **procedural issue** (e.g., "absence of intent", "lack of evidence")
Do **not** include:
- Full sentences
- Personal names
- Generic or vague words (e.g., "man", "incident", "case", "situation")
Keep the query under **20 words**. Separate terms by commas if needed. Optimize for legal document search.
Case Facts:
\"\"\"{caseFacts}\"\"\"
Return only the search query, no explanation or prefix:
"""
try:
response = self.client.models.generate_content(
model=settings.gemini_model,
contents=prompt
)
if response.text:
query = response.text.replace("Search Query:", "").strip().strip('"').replace("\n", "")
else:
query = caseFacts[:50] # Fallback
if verbose:
logger.info(f"Generated RAG Query: {query}")
return query
except Exception as e:
logger.error(f"Error generating search query: {str(e)}")
raise ValueError(f"Search query generation failed: {str(e)}")
def buildGeminiPrompt(self, inputText: str, modelVerdict: str, confidence: float,
support: Dict[str, List], query: Optional[str] = None) -> str:
verdictOutcome = "a loss for the person" if modelVerdict.lower() == "guilty" else "in favor of the person"
prompt = f"""You are a judge evaluating a legal dispute under Indian law.
### Case Facts:
{inputText}
### Initial Model Verdict:
{modelVerdict.upper()} (Confidence: {confidence * 100:.2f}%)
This verdict is interpreted as {verdictOutcome}.
"""
if query:
prompt += f"\n### Legal Query Used:\n{query}\n"
prompt += "\n---\n\n### Legal References Retrieved:\n\n#### Constitution Articles (Top 5):\n"
for i, art in enumerate(support.get("constitution", [])):
prompt += f"- {i+1}. {str(art)}\n"
prompt += "\n#### IPC Sections (Top 5):\n"
for i, sec in enumerate(support.get("ipcSections", [])):
prompt += f"- {i+1}. {str(sec)}\n"
prompt += "\n#### IPC Case Law (Top 5):\n"
for i, case in enumerate(support.get("ipcCase", [])):
prompt += f"- {i+1}. {str(case)}\n"
prompt += "\n#### Statutes (Top 5):\n"
for i, stat in enumerate(support.get("statutes", [])):
prompt += f"- {i+1}. {str(stat)}\n"
prompt += "\n#### QA Texts (Top 5):\n"
for i, qa in enumerate(support.get("qaTexts", [])):
prompt += f"- {i+1}. {str(qa)}\n"
prompt += "\n#### General Case Law (Top 5):\n"
for i, gcase in enumerate(support.get("caseLaw", [])):
prompt += f"- {i+1}. {str(gcase)}\n"
prompt += f"""
---
### Instructions to the Judge (You):
1. Review the legal materials provided:
- Identify which Constitution articles, IPC sections, statutes, and case laws are relevant to the facts.
- Also note and explain which retrieved references are **not applicable** or irrelevant.
2. If relevant past cases appear in the retrieved materials, summarize them and analyze whether they support or contradict the model's verdict.
3. Using the above, assess the model's prediction:
- If confidence is below 60%, you may revise or retain it.
- If confidence is 60% or higher, retain unless clear legal grounds exist to challenge it.
4. Provide a thorough and formal legal explanation that:
- Justifies the final decision using legal logic
- Cites relevant IPCs, constitutional provisions, statutes, and precedents
- Explains any reasoning for overriding the model's prediction, if applicable
5. Conclude with the following lines, formatted as shown:
Final Verdict: Guilty or Not Guilty
Verdict Changed: Yes or No
Respond in the tone of a formal Indian judge. Your explanation should reflect reasoning, neutrality, and respect for legal procedure.
"""
return prompt
def extractFinalVerdict(self, geminiOutput: str) -> tuple[Optional[str], str]:
verdictMatch = re.search(r"final verdict\s*[:\-]\s*(guilty|not guilty)", geminiOutput, re.IGNORECASE)
changedMatch = re.search(r"verdict changed\s*[:\-]\s*(yes|no)", geminiOutput, re.IGNORECASE)
finalVerdict = verdictMatch.group(1).lower() if verdictMatch else None
verdictChanged = "changed" if changedMatch and changedMatch.group(1).lower() == "yes" else "not changed"
return finalVerdict, verdictChanged
def evaluateCaseWithGemini(self, inputText: str, modelVerdict: str, confidence: float,
retrieveFn, geminiQueryModel=None):
try:
if geminiQueryModel:
support, searchQuery = retrieveFn.retrieveDualSupportChunks(inputText, self)
else:
support, _ = retrieveFn.retrieveSupportChunksParallel(inputText)
searchQuery = inputText
prompt = self.buildGeminiPrompt(inputText, modelVerdict, confidence, support, searchQuery)
response = self.client.models.generate_content(
model=settings.gemini_model,
contents=prompt
)
geminiOutput = response.text if response.text else "No response from Gemini"
finalVerdict, verdictChanged = self.extractFinalVerdict(geminiOutput)
logs = {
"inputText": inputText,
"modelVerdict": modelVerdict,
"confidence": confidence,
"support": support,
"promptToGemini": prompt,
"geminiOutput": geminiOutput,
"finalVerdictByGemini": finalVerdict,
"verdictChanged": verdictChanged,
"ragSearchQuery": searchQuery
}
return logs
except Exception as e:
return {
"error": str(e),
"inputText": inputText,
"modelVerdict": modelVerdict,
"confidence": confidence,
"ragSearchQuery": None,
"support": None,
"promptToGemini": None,
"geminiOutput": None,
"finalVerdictByGemini": None,
"verdictChanged": None
}
def is_configured(self) -> bool:
return self.client is not None
def is_healthy(self) -> bool:
return self.is_configured()
|