Pushed app.py and updated Hugging Face Space

app.py

@@ -1,41 +1,35 @@
 import os
+import logging
+import datetime
+from io import BytesIO
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from fastapi.responses import StreamingResponse
+
+import matplotlib
+matplotlib.use('Agg')  # headless backend
+import matplotlib.pyplot as plt
 
+from transformers import pipeline
+import httpx
+
+# Set environment variables ONCE before transformers import
 os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["MPLCONFIGDIR"] = "/tmp/mplconfig"
-os.environ["HF_HOME"] = "/tmp"
 
+# Create necessary dirs if not exist
 os.makedirs("/tmp/huggingface", exist_ok=True)
 os.makedirs("/tmp/mplconfig", exist_ok=True)
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-os.environ["HF_HOME"] = "/tmp"
-# Load the NER model
-tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-
-
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from transformers import pipeline
-from fastapi.responses import StreamingResponse
-import matplotlib
-matplotlib.use('Agg')  # ✅ Force headless-safe backend
-import matplotlib.pyplot as plt  # ✅ Safe to use now
-import requests
-import datetime
-from io import BytesIO
-import logging
-
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Create FastAPI app
+# Initialize FastAPI app
 app = FastAPI()
 
-# Load transformers models
+# Load transformers pipelines once at startup
 try:
     ner_model = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
     sentiment_model = pipeline("sentiment-analysis", model="ProsusAI/finbert")
@@ -45,11 +39,10 @@ except Exception as e:
     ner_model = None
     sentiment_model = None
 
-# Request body schema for sentiment and NER
+# Request body schemas
 class TextRequest(BaseModel):
     text: str
 
-# Request body schema for chart
 class CoinRequest(BaseModel):
     coin_id: str
 
@@ -91,7 +84,7 @@ def analyze_ner(req: TextRequest):
         raise HTTPException(status_code=500, detail="NER analysis failed")
 
 @app.post("/chart")
-def generate_chart(req: CoinRequest):
+async def generate_chart(req: CoinRequest):
     coin_id = req.coin_id.strip().lower()
     if not coin_id:
         raise HTTPException(status_code=400, detail="coin_id cannot be empty")
@@ -100,7 +93,8 @@ def generate_chart(req: CoinRequest):
     params = {"vs_currency": "usd", "days": "1"}
 
     try:
-        res = requests.get(url, params=params)
+        async with httpx.AsyncClient() as client:
+            res = await client.get(url, params=params)
         if res.status_code != 200:
             logger.error(f"CoinGecko API error: {res.text}")
             raise HTTPException(status_code=502, detail="Failed to fetch coin data from CoinGecko")