Added FastAPI app

Dockerfile

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+FROM python:3.10-slim
+
+# Working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+# Expose port 7860 (as per HF spec)
+EXPOSE 7860
+
+# Command to run FastAPI app with uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import pipeline
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# Load models once on startup
+try:
+    ner_model = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
+    sentiment_model = pipeline("sentiment-analysis", model="ProsusAI/finbert")
+except Exception as e:
+    logger.error(f"Model loading failed: {e}")
+    ner_model = None
+    sentiment_model = None
+
+class TextRequest(BaseModel):
+    text: str
+
+@app.get("/")
+def home():
+    return {"message": "Crypto News API is alive!"}
+
+@app.post("/sentiment")
+def analyze_sentiment(req: TextRequest):
+    if not sentiment_model:
+        raise HTTPException(status_code=503, detail="Sentiment model not available")
+    text = req.text
+    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)
+    }
+
+@app.post("/ner")
+def analyze_ner(req: TextRequest):
+    if not ner_model:
+        raise HTTPException(status_code=503, detail="NER model not available")
+    text = req.text
+    if not text:
+        raise HTTPException(status_code=400, detail="Text cannot be empty")
+    entities = ner_model(text[:512])
+    # Filter relevant entities (ORG, PERSON, MISC, PRODUCT, GPE)
+    relevant = [e['word'] for e in entities if e['entity_group'] in ['ORG', 'PERSON', 'MISC', 'PRODUCT', 'GPE']]
+    # Remove duplicates and limit to 5
+    unique_entities = list(dict.fromkeys(relevant))[:5]
+    return {"entities": unique_entities}

requirements.txt

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+fastapi
+uvicorn[standard]
+transformers
+torch