Merge branch 'web-version' of https://github.com/Vargock/AI-Texts-Analysis into web-version

README.md

@@ -10,4 +10,4 @@ pinned: false
 # Text Sentiment Analyzer
 This was originally designed as a simple Telegram bot that uses the Hugging Face API to detect the sentiment of text messages, but for easier testing and hosting I decided to switch it to Flask-based web version.
 
-[![Open in Hugging Face](https://img.shields.io/badge/Open%20in-HuggingFace-blue)](https://huggingface.co/spaces/Vargock/Text-Sentiment-Analyzzer)
+[![Open in Hugging Face](https://img.shields.io/badge/Open%20in-HuggingFace-blue)](https://huggingface.co/spaces/Vargock/Text-Sentiment-Analyzzer)

app.py

@@ -1,9 +1,12 @@
-from flask import Flask, render_template, request
+import uuid
+from flask import Flask, render_template, request, session
 from modules.db import init_db, save_message, get_recent
 from modules.hf_helpers import hf_infer
 from modules.helpers import detect_lang, MODELS, LABELS, update_history, bar
 
 app = Flask(__name__)
+app.secret_key = "seed-string"
+
 init_db()
 
 COLOR_MAP = {
@@ -17,23 +20,32 @@ COLOR_MAP = {
     "Toxicity": "purple"
 }
 
+
 @app.route("/", methods=["GET", "POST"])
 def index():
+    
+    if "user_id" not in session:
+        session["user_id"] = str(uuid.uuid4())
+    user_id = session["user_id"]
+
     result = None
+
     if request.method == "POST":
         text = request.form.get("text", "").strip()
         if text:
-            user_id = 1 
             lang = detect_lang(text)
             labels = LABELS[lang]
 
-            # Sentiment 
+            # Sentiment
             sentiment_model = MODELS["ru_sentiment"] if lang == "ru" else MODELS["en_sentiment"]
             sentiment_data = hf_infer(sentiment_model, text)
             sentiment_result = None
             if sentiment_data:
                 top_raw_label = max(sentiment_data, key=lambda x: x["score"])
-                sentiment = labels.get(top_raw_label["label"].lower() if lang == "ru" else top_raw_label["label"], top_raw_label["label"]).upper()
+                sentiment = labels.get(
+                    top_raw_label["label"].lower() if lang == "ru" else top_raw_label["label"],
+                    top_raw_label["label"]
+                ).upper()
                 score = top_raw_label["score"]
                 sentiment_result = {
                     "name": sentiment,
@@ -53,12 +65,12 @@ def index():
                     top_emo = max(emo_data, key=lambda x: x["score"])
                     emotion_result = {
                         "name": top_emo["label"].capitalize(),
-                        "score": int(top_emo["score"]*100),
+                        "score": int(top_emo["score"] * 100),
                         "bar": bar(top_emo["score"]),
                         "color": COLOR_MAP["Emotion"]
                     }
 
-            #  Toxicity (EN only) 
+            # Toxicity (EN only)
             tox_result = None
             if lang == "en":
                 tox_data = hf_infer(MODELS["toxicity"], text)
@@ -66,7 +78,7 @@ def index():
                     top_tox = max(tox_data, key=lambda x: x["score"])
                     tox_result = {
                         "name": top_tox["label"],
-                        "score": int(top_tox["score"]*100),
+                        "score": int(top_tox["score"] * 100),
                         "bar": bar(top_tox["score"]),
                         "color": COLOR_MAP["Toxicity"]
                     }
@@ -78,7 +90,7 @@ def index():
                 "pos_count": pos_count
             }
 
-    history = get_recent(1, 10)
+    history = get_recent(user_id, 10)
     return render_template("index.html", result=result, history=history)
 
 

modules/db.py

@@ -11,6 +11,7 @@ DB_PATH = os.path.join(DB_FOLDER, "data.db")
 
 _conn: Optional[sqlite3.Connection] = None
 
+
 def get_db_connection() -> Optional[sqlite3.Connection]:
     global _conn
     if _conn:
@@ -24,6 +25,7 @@ def get_db_connection() -> Optional[sqlite3.Connection]:
         logger.error("Database connection error: %s", e)
         return None
 
+
 def init_db() -> None:
     conn = get_db_connection()
     if not conn:
@@ -34,22 +36,23 @@ def init_db() -> None:
         cur.execute("""
         CREATE TABLE IF NOT EXISTS history (
             id INTEGER PRIMARY KEY AUTOINCREMENT,
-            user_id INTEGER NOT NULL,
+            user_id TEXT NOT NULL,
             text TEXT NOT NULL,
             sentiment TEXT NOT NULL,
             confidence REAL NOT NULL,
             timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
-        )""")
+        )
+        """)
         cur.execute("CREATE INDEX IF NOT EXISTS idx_user_id ON history(user_id)")
         cur.execute("CREATE INDEX IF NOT EXISTS idx_timestamp ON history(timestamp)")
-        cur.execute("CREATE INDEX IF NOT EXISTS idx_user_timestamp ON history(user_id, timestamp)")
         conn.commit()
         logger.info("Database initialized")
     except sqlite3.Error as e:
         logger.exception("Database initialization error: %s", e)
         raise
 
-def save_message(user_id: int, text: str, sentiment: str, confidence: float) -> bool:
+
+def save_message(user_id: str, text: str, sentiment: str, confidence: float) -> bool:
     conn = get_db_connection()
     if not conn:
         return False
@@ -69,7 +72,8 @@ def save_message(user_id: int, text: str, sentiment: str, confidence: float) ->
             pass
         return False
 
-def get_recent(user_id: int, limit: int = 10) -> List[Tuple]:
+
+def get_recent(user_id: str, limit: int = 10) -> List[Tuple]:
     conn = get_db_connection()
     if not conn:
         return []
@@ -80,7 +84,8 @@ def get_recent(user_id: int, limit: int = 10) -> List[Tuple]:
         FROM history
         WHERE user_id = ?
         ORDER BY timestamp DESC
-        LIMIT ?""", (user_id, limit))
+        LIMIT ?
+        """, (user_id, limit))
         rows = cur.fetchall()
         return [tuple(r) for r in rows]
     except sqlite3.Error as e:

modules/hf_helpers.py

@@ -1,6 +1,9 @@
 import requests
 import logging
 import os
+from dotenv import load_dotenv
+
+load_dotenv()
 
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
 log = logging.getLogger(__name__)
@@ -16,7 +19,7 @@ def hf_infer(model: str, text: str):
         resp.raise_for_status()
         out = resp.json()
 
-        # Normalize: dict → list, flatten nested list
+        # Normalize API output from dict → list[]
         if isinstance(out, dict):
             out = [out]
         elif isinstance(out, list) and len(out) == 1 and isinstance(out[0], list):
@@ -24,7 +27,9 @@ def hf_infer(model: str, text: str):
 
         # Filter invalid entries
         out = [x for x in out if isinstance(x, dict) and "label" in x and "score" in x]
+
         return out
+    
     except Exception as e:
         log.warning(f"HF inference failed ({model}): {e}")
         return []