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63bcd5a 41bd215 63bcd5a 8d49ff0 eb029d7 8d49ff0 63bcd5a d4e242f 63bcd5a d4e242f 8d49ff0 63bcd5a 4ffaaf7 41bd215 63bcd5a 41bd215 63bcd5a 41bd215 63bcd5a | 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 | import pandas as pd
from fastapi import HTTPException
from src.similarity_model import find_similar_projects
from src.similarity_model import extract_features
def analyze_project(
title: str,
description: str,
abstract: str = "",
features=None,
top_k: int = 5
):
if features is None:
features = []
full_text = f"{title}. {abstract}. {description}"
auto_features = extract_features(full_text)
merged = []
seen = set()
for item in features + auto_features:
val = str(item).strip().lower()
if val and val not in seen:
seen.add(val)
merged.append(val)
results = find_similar_projects(
title=title,
description=f"{abstract} {description}",
features=merged,
top_k=top_k
)
if not isinstance(results, pd.DataFrame) or len(results) == 0:
return {
"message": "No similar projects found",
"extracted_features": merged,
"overall_originality_score": 100.0
}
# -----------------------------------
# رجع Top K كله
# -----------------------------------
top_projects = []
for _, row in results.iterrows():
orig_score = round(float(row.get("originality_score", 0)), 2)
sim_percent = round(float(row.get("hybrid_score", 0)) * 100, 2)
top_projects.append({
"project_title": row.get("project_title", ""),
"project_features": row.get("candidate_features", []),
"matched_features": row.get("matched_features", []),
"unique_features": row.get("unique_candidate_features", []),
"similarity_score": sim_percent,
"final_originality_score": orig_score
})
# Overall = worst-case originality (against the most similar project)
overall_originality_score = top_projects[0]["final_originality_score"]
return {
"extracted_features": merged,
"overall_originality_score": overall_originality_score,
"top_similar_projects": top_projects
}
def chat_with_llm(user_id: str, message: str):
try:
from src.recommendation_engine.chatbot_engine import chatbot
from src.recommendation_engine.llm_client import LLMProviderError
except Exception as exc:
raise HTTPException(
status_code=503,
detail=f"LLM service could not start: {exc}"
)
try:
response = chatbot(
user_id=user_id,
user_input=message
)
except LLMProviderError as exc:
raise HTTPException(
status_code=exc.status_code,
detail=exc.message
)
return {
"user_id": user_id,
"response": response
}
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