Initial FastAPI app for transcripts API

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+

app.py

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+import logging
+from fastapi import FastAPI
+from datasets import load_dataset
+import pandas as pd
+import numpy as np
+
+# -------------------------------------------------
+# Logging configuration
+# -------------------------------------------------
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s | %(levelname)s | %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# -------------------------------------------------
+# Lazy dataset cache
+# -------------------------------------------------
+DF = None
+
+
+def load_data_once():
+    """
+    Load the dataset only once using streaming mode.
+    Hugging Face Spaces cannot download large datasets at startup,
+    so streaming=True avoids timeouts and memory issues.
+    """
+    global DF
+
+    if DF is None:
+        logger.info("Loading dataset (streaming mode): kurry/sp500_earnings_transcripts ...")
+
+        ds = load_dataset(
+            "kurry/sp500_earnings_transcripts",
+            split="train",
+            streaming=True
+        )
+
+        # Convert streaming dataset → pandas DataFrame
+        # Limit rows to avoid memory overload
+        rows = []
+        for i, item in enumerate(ds):
+            rows.append(item)
+            if i > 5000:   # Safety limit for Spaces
+                break
+
+        DF = pd.DataFrame(rows)
+        logger.info(f"Loaded {len(DF)} rows into DataFrame")
+
+    return DF
+
+
+# -------------------------------------------------
+# Utility: convert NumPy → Python
+# -------------------------------------------------
+def to_python(obj):
+    if isinstance(obj, (np.integer, np.int64)):
+        return int(obj)
+    if isinstance(obj, (np.floating, np.float64)):
+        return float(obj)
+    if isinstance(obj, (np.bool_,)):
+        return bool(obj)
+    if isinstance(obj, pd.Timestamp):
+        return obj.isoformat()
+    return obj
+
+
+def clean_dict(d):
+    return {k: to_python(v) for k, v in d.items()}
+
+
+# -------------------------------------------------
+# Routes
+# -------------------------------------------------
+
+@app.on_event("startup")
+def startup_event():
+    logger.info("🚀 Earnings Transcript API starting up")
+
+
+@app.get("/")
+def root():
+    logger.info("Root endpoint called")
+    return {"message": "Earnings Transcript API is running"}
+
+
+@app.get("/tickers")
+def list_tickers():
+    logger.info("Listing all tickers")
+    df = load_data_once()
+
+    if "symbol" not in df.columns:
+        return {"error": "Dataset does not contain 'symbol' column"}
+
+    tickers = sorted(df["symbol"].dropna().unique().tolist())
+    logger.info(f"Returned {len(tickers)} tickers")
+    return {"tickers": tickers}
+
+
+@app.get("/transcript/{symbol}")
+def get_transcript(symbol: str):
+    logger.info(f"Transcript request received for symbol: {symbol}")
+
+    df = load_data_once()
+    symbol = symbol.upper()
+
+    if "symbol" not in df.columns:
+        return {"error": "Dataset missing 'symbol' column"}
+
+    rows = df[df["symbol"] == symbol]
+
+    if rows.empty:
+        logger.warning(f"No transcripts found for symbol: {symbol}")
+        return {"error": f"No transcripts found for symbol {symbol}"}
+
+    row = rows.iloc[0]
+    base_info = clean_dict(row.to_dict())
+
+    # Extract structured content (correct column name)
+    segments = row.get("structured_content", None)
+
+    if isinstance(segments, list):
+        logger.info(f"Cleaning {len(segments)} segments for {symbol}")
+        cleaned_segments = [
+            clean_dict(seg) for seg in segments if isinstance(seg, dict)
+        ]
+        base_info["segments"] = cleaned_segments
+    else:
+        logger.info(f"No structured_content found for {symbol}")
+        base_info["segments"] = []
+
+    logger.info(f"Returning transcript for {symbol}")
+    return base_info
+

index.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Ticker Checker</title>
+    <style>
+        body { font-family: Arial; margin: 40px; }
+        input { padding: 8px; width: 200px; }
+        button { padding: 8px 12px; }
+        #result { margin-top: 20px; font-size: 18px; }
+    </style>
+</head>
+<body>
+
+<h2>Check if a Ticker Exists</h2>
+
+<input id="tickerInput" type="text" placeholder="Enter ticker (e.g., AAPL)">
+<button onclick="checkTicker()">Check</button>
+
+<div id="result"></div>
+
+<script>
+async function checkTicker() {
+    const ticker = document.getElementById("tickerInput").value.trim().toUpperCase();
+    const resultDiv = document.getElementById("result");
+
+    if (!ticker) {
+        resultDiv.innerHTML = "Please enter a ticker.";
+        return;
+    }
+
+    const response = await fetch(`/check/${ticker}`);
+    const data = await response.json();
+
+    if (data.exists) {
+        resultDiv.innerHTML = `<span style="color: green;">✔ ${ticker} exists in the dataset</span>`;
+    } else {
+        resultDiv.innerHTML = `<span style="color: red;">✘ ${ticker} does NOT exist in the dataset</span>`;
+    }
+}
+</script>
+
+</body>
+</html>
+

requirements.txt

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+fastapi==0.109.0
+uvicorn==0.27.0
+datasets==2.18.0
+pandas==2.2.0
+pyarrow==15.0.0
+