Fix insert issue and search API added

main.py

@@ -1,5 +1,6 @@
 import re
 from fastapi import FastAPI
+from fastapi import Header
 from pydantic import BaseModel
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForTokenClassification
 import dateparser
@@ -24,7 +25,6 @@ import psycopg2
 from psycopg2.extras import Json
 import os
 from dotenv import load_dotenv
-import random
 
 # Load environment variables
 load_dotenv()
@@ -581,7 +581,7 @@ def predict_expense_category(text, detected_stores):
         "khana kharch", "khana kharida", "khana diya", "khana laya", "khana banaya"
     ],
     "transport": [
-        "uber", "ola", "taxi", "cab", "bus", "train", "metro", "flight", "auto", "rickshaw", "car", "gaadi", "yatra", "safar", "travel", "ticket", "plane", "udaan", "station", "airport"
+        "uber", "ola", "taxi", "cab", "bus", "train", "metro", "flight", "auto", "rickshaw", "car", "gaadi", "yatra", "safar", "travel", "ticket", "plane", "udaan", "station", "airport", "rapido",
     ],
     "shopping": [
         "amazon", "flipkart", "myntra", "shopping", "clothes", "kapde", "apparel", "shoes", "jeans", "tshirt", "store", "fashion", "dukaan", "mall", "bazaar", "market", "kharida", "order diya", "le liya"
@@ -686,6 +686,73 @@ async def not_found_handler(request: Request, exc):
 async def internal_error_handler(request: Request, exc):
     return ORJSONResponse(status_code=500, content={"error": "Internal server error: " + str(exc)})
 
+@app.get("/search", response_class=ORJSONResponse)
+async def search_entries(
+    userid: str = Header(..., description="User ID"),
+    tags: str = "",
+    query: str = "",
+    startDate: str = "",
+    endDate: str = "",
+    type: str = ""
+):
+    # Validate user_id from header
+    if not userid or not userid.strip():
+        return ORJSONResponse(status_code=400, content={"error": "Missing or empty userid header."})
+
+    # Build SQL filters
+    filters = ["user_id = %s"]
+    params = [userid]
+
+    if type:
+        filters.append("type = %s")
+        params.append(type)
+
+    if tags:
+        tag_list = [t.strip() for t in tags.split(",") if t.strip()]
+        filters.append("tags && %s")
+        params.append(tag_list)
+
+    if query:
+        filters.append("(raw_text ILIKE %s OR summary ILIKE %s)")
+        params.extend([f"%{query}%", f"%{query}%"])
+
+    if startDate:
+        try:
+            start_dt = datetime.strptime(startDate, "%d-%m-%Y")
+            filters.append("created_at >= %s")
+            params.append(start_dt)
+        except:
+            return ORJSONResponse(status_code=400, content={"error": "Invalid startDate format. Use DD-MM-YYYY."})
+
+    if endDate:
+        try:
+            end_dt = datetime.strptime(endDate, "%d-%m-%Y")
+            filters.append("created_at <= %s")
+            params.append(end_dt)
+        except:
+            return ORJSONResponse(status_code=400, content={"error": "Invalid endDate format. Use DD-MM-YYYY."})
+
+    where_clause = " AND ".join(filters)
+    query_sql = f"SELECT * FROM user_entries WHERE {where_clause} ORDER BY created_at DESC LIMIT 50"
+
+    try:
+        conn = psycopg2.connect(DATABASE_URL)
+        cur = conn.cursor()
+        cur.execute(query_sql, tuple(params))
+        rows = cur.fetchall()
+        columns = [desc[0] for desc in cur.description]
+        entries = [dict(zip(columns, row)) for row in rows]
+        # Remove raw_json from each entry in results
+        for entry in entries:
+            entry.pop("raw_json", None)
+            
+        cur.close()
+        conn.close()
+    except Exception as e:
+        return ORJSONResponse(status_code=500, content={"error": str(e)})
+
+    return ORJSONResponse(content={"results": entries})
+
 @app.get("/visualyse/{user_id}", response_class=ORJSONResponse)
 async def visualyse_dashboard(user_id: str):
     try:
@@ -852,6 +919,9 @@ async def analyze(input: TextInput):
     tags = generate_tags(best_label, text)
     language_detected = detect_language(text)
     sentiment_score = get_sentiment_score(text)    
+    if sentiment_score is None or sentiment_score == "":
+        sentiment_score = 0.0
+
     entities = await asyncio.to_thread(extract_entities, text)
     people = entities["people"] # Extracted people entities
     intent = infer_intent(best_label, text)