Fix matplotlib dir and add httpx to requirements

app.py

@@ -7,38 +7,31 @@ os.environ["HF_HOME"] = "/tmp"
 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 transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse, FileResponse
 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
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
 import httpx
 import io
-import requests
-import datetime
-from io import BytesIO
 import logging
+import random
 
-# Configure logging
+# Logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Create FastAPI app
+# FastAPI app
 app = FastAPI()
 
-# Load transformers models
+# Load models
 try:
+    tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+    model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
     ner_model = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
     sentiment_model = pipeline("sentiment-analysis", model="ProsusAI/finbert")
     logger.info("Models loaded successfully.")
@@ -47,14 +40,17 @@ except Exception as e:
     ner_model = None
     sentiment_model = None
 
-# Request body schema for sentiment and NER
+# Schemas
 class TextRequest(BaseModel):
     text: str
 
-# Request body schema for chart
 class CoinRequest(BaseModel):
     coin_id: str
 
+class VisualRequest(BaseModel):
+    coin_id: str
+    topic: str
+
 @app.get("/")
 def home():
     return {"message": "Crypto News API is alive!"}
@@ -63,15 +59,12 @@ def home():
 def analyze_sentiment(req: TextRequest):
     if not sentiment_model:
         raise HTTPException(status_code=503, detail="Sentiment model not available")
-    text = req.text.strip()
-    if not text:
-        raise HTTPException(status_code=400, detail="Text cannot be empty")
     try:
+        text = req.text.strip()
+        if not text:
+            raise HTTPException(status_code=400, detail="Text cannot be empty")
         result = sentiment_model(text[:512])[0]
-        return {
-            "label": result["label"],
-            "score": round(result["score"] * 100, 2)
-        }
+        return {"label": result["label"], "score": round(result["score"] * 100, 2)}
     except Exception as e:
         logger.error(f"Sentiment analysis error: {e}")
         raise HTTPException(status_code=500, detail="Sentiment analysis failed")
@@ -80,10 +73,10 @@ def analyze_sentiment(req: TextRequest):
 def analyze_ner(req: TextRequest):
     if not ner_model:
         raise HTTPException(status_code=503, detail="NER model not available")
-    text = req.text.strip()
-    if not text:
-        raise HTTPException(status_code=400, detail="Text cannot be empty")
     try:
+        text = req.text.strip()
+        if not text:
+            raise HTTPException(status_code=400, detail="Text cannot be empty")
         entities = ner_model(text[:512])
         relevant = [e['word'] for e in entities if e.get('entity_group') in ['ORG', 'PERSON', 'MISC', 'PRODUCT', 'GPE']]
         unique_entities = list(dict.fromkeys(relevant))[:5]
@@ -92,39 +85,89 @@ def analyze_ner(req: TextRequest):
         logger.error(f"NER analysis error: {e}")
         raise HTTPException(status_code=500, detail="NER analysis failed")
 
-    
-
 @app.post("/chart")
 def generate_chart(req: CoinRequest):
     coin_id = req.coin_id.strip().lower()
     logger.info(f"Generating chart for coin: {coin_id}")
-
     try:
         url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart"
         params = {"vs_currency": "usd", "days": "7"}
         response = httpx.get(url, params=params)
-
         if response.status_code != 200:
             logger.error(f"CoinGecko API error: {response.text}")
             raise HTTPException(status_code=502, detail="Failed to fetch coin data from CoinGecko")
-
         prices = response.json()["prices"]
-        timestamps, values = zip(*prices)
-
+        _, values = zip(*prices)
         plt.figure(figsize=(6, 3))
         plt.plot(values, color="blue")
         plt.title(f"{coin_id.capitalize()} - Last 7 Days")
         plt.xlabel("Time")
         plt.ylabel("Price (USD)")
         plt.grid(True)
-
         buffer = io.BytesIO()
         plt.savefig(buffer, format="png")
         plt.close()
         buffer.seek(0)
-
         return StreamingResponse(buffer, media_type="image/png")
-
     except Exception as e:
         logger.exception(f"Chart generation error: {e}")
-        raise HTTPException(status_code=500, detail="Chart generation failed")
+        raise HTTPException(status_code=500, detail="Chart generation failed")
+
+# ✅ News image generator
+def generate_news_image(topic: str) -> str:
+    file_path = f"/tmp/{topic.replace(' ', '_')}_news.png"
+    plt.figure(figsize=(6, 3))
+    plt.text(0.5, 0.5, f"📰 {topic}", fontsize=18, ha='center')
+    plt.axis("off")
+    plt.savefig(file_path)
+    plt.close()
+    return file_path
+
+# ✅ Chart image generator for visual endpoint (reuse)
+def generate_chart_image(coin_id: str) -> str:
+    try:
+        url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart"
+        params = {"vs_currency": "usd", "days": "7"}
+        response = httpx.get(url, params=params)
+        if response.status_code != 200:
+            raise Exception("CoinGecko data fetch failed")
+        prices = response.json()["prices"]
+        _, values = zip(*prices)
+        file_path = f"/tmp/{coin_id.replace(' ', '_')}_chart.png"
+        plt.figure(figsize=(6, 3))
+        plt.plot(values, color="green")
+        plt.title(f"{coin_id.capitalize()} Chart")
+        plt.grid(True)
+        plt.savefig(file_path)
+        plt.close()
+        return file_path
+    except Exception as e:
+        logger.error(f"Chart image generation error: {e}")
+        raise
+
+# ✅ Random visual endpoint
+@app.post("/visual")
+def generate_visual(req: VisualRequest):
+    choice = random.choice(["chart", "news"])
+    logger.info(f"Generating visual: {choice}")
+    try:
+        if choice == "chart":
+            path = generate_chart_image(req.coin_id)
+        else:
+            path = generate_news_image(req.topic)
+        return FileResponse(path, media_type="image/png")
+    except Exception as e:
+        logger.error(f"Visual generation failed: {e}")
+        raise HTTPException(status_code=500, detail="Visual generation failed")
+
+
+
+
+
+
+
+
+
+
+
+

requirements.txt

@@ -3,4 +3,5 @@ uvicorn[standard]
 transformers
 torch
 matplotlib
-requests
+requests
+httpx