File size: 5,880 Bytes
63bcd5a cff9d3b 63bcd5a b09149c 63bcd5a 4552666 63bcd5a cff9d3b 63bcd5a cff9d3b 63bcd5a 4552666 63bcd5a 4552666 63bcd5a 4552666 63bcd5a 4552666 b09149c c2a8167 b09149c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 | from src.recommendation_engine.llm_client import generate_text
from src.recommendation_engine.prompt_builder import (
build_full_project_prompt
)
import re
def extract_section(text, section_name):
text = text.strip()
marker = section_name + ":"
if marker not in text:
return ""
start = text.find(marker) + len(marker)
sections = [
"CATEGORY:",
"ABSTRACT:",
"DESCRIPTION:",
"TECHNOLOGIES:",
"KEYWORDS:",
"PROBLEM_STATEMENT:",
"PROPOSED_SOLUTION:",
"OBJECTIVES:",
"AI_SUMMARY:",
"FUTURE_WORK:",
"METHODOLOGY:"
]
end = len(text)
for s in sections:
if s == marker:
continue
pos = text.find(s, start)
if pos != -1 and pos < end:
end = pos
return text[start:end].strip()
def parse_bullets(text):
lines = text.splitlines()
final = []
for line in lines:
line = line.strip()
if not line:
continue
line = re.sub(
r"^[-•*0-9.\)\s]+",
"",
line
)
if line:
final.append(line)
return final
def generate_full_project(
title,
features,
description="",
abstract="",
custom_description=False,
custom_abstract=False
):
context = {
"project_title": title,
"features": features,
"description": description,
"abstract": abstract
}
prompt = build_full_project_prompt(context)
raw = generate_text(
prompt,
task="full_project"
)
result = {
"project_title": title,
"category":
extract_section(raw, "CATEGORY"),
"abstract":
abstract if custom_abstract else extract_section(raw, "ABSTRACT"),
"description":
description if custom_description else extract_section(raw, "DESCRIPTION"),
"technologies":
parse_bullets(
extract_section(raw, "TECHNOLOGIES")
),
"keywords":
parse_bullets(
extract_section(raw, "KEYWORDS")
),
"objectives":
parse_bullets(
extract_section(raw, "OBJECTIVES")
),
"future_work":
parse_bullets(
extract_section(raw, "FUTURE_WORK")
),
"problem_statement":
extract_section(
raw,
"PROBLEM_STATEMENT"
),
"proposed_solution":
extract_section(
raw,
"PROPOSED_SOLUTION"
),
"methodology":
extract_section(
raw,
"METHODOLOGY"
),
"ai_summary":
extract_section(
raw,
"AI_SUMMARY"
)
}
if not result.get("category"):
result["category"] = "General AI System"
if not result.get("keywords"):
result["keywords"] = [
"Artificial Intelligence",
"Automation",
"Smart System"
]
if not result.get("problem_statement"):
result["problem_statement"] = (
"Current traditional systems suffer from "
"limited automation, inefficiency, and "
"lack of intelligent decision-making."
)
if not result.get("proposed_solution"):
result["proposed_solution"] = (
"The proposed system uses AI-driven "
"automation and intelligent analytics "
"to improve operational efficiency."
)
if not result.get("objectives"):
result["objectives"] = [
"Improve automation efficiency",
"Enhance system accuracy",
"Reduce operational costs"
]
if not result.get("methodology"):
result["methodology"] = (
"The system will be developed using "
"data collection, preprocessing, "
"AI model training, testing, and deployment."
)
if not result.get("future_work"):
result["future_work"] = [
"Cloud integration",
"Mobile application support",
"Advanced AI optimization"
]
if not result.get("ai_summary"):
result["ai_summary"] = (
f"{title} is an intelligent AI-powered "
f"graduation project designed to provide "
f"automation, monitoring, and predictive analysis."
)
return result
def rewrite_custom_sections(features, abstract="", description=""):
features_text = "\n".join(f"- {f}" for f in features)
prompt = f"""
The user provided custom text for their project's abstract and/or description.
Your task is to update their custom text to incorporate the following NEW FEATURES.
CRITICAL RULES:
1. You MUST preserve the exact tone, style, and core phrasing of the user's original text!
2. Just weave the new features in naturally.
3. EXPAND the content significantly to ensure it is highly detailed.
4. The rewritten ABSTRACT MUST contain a minimum of 130 words.
5. The rewritten DESCRIPTION MUST contain a minimum of 160 words.
NEW FEATURES:
{features_text}
CUSTOM ABSTRACT:
{abstract if abstract else "N/A"}
CUSTOM DESCRIPTION:
{description if description else "N/A"}
OUTPUT FORMAT:
ABSTRACT:
(your rewritten abstract here, or leave empty if N/A)
DESCRIPTION:
(your rewritten description here, or leave empty if N/A)
""".strip()
raw = generate_text(prompt, task="chat")
return {
"abstract": extract_section(raw, "ABSTRACT") if abstract else "",
"description": extract_section(raw, "DESCRIPTION") if description else ""
}
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