Create app.py

app.py

@@ -0,0 +1,104 @@
+import os
+import json
+from fastapi import FastAPI, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+from datasets import load_dataset
+import uvicorn
+
+app = FastAPI(title="Human Essence Emotional Flow Visualizer")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+DATASET_REPO = "wop/Human-Essence-Dataset"
+
+def fetch_dataset(force=False):
+    download_mode = "force_redownload" if force else None
+    ds = load_dataset(DATASET_REPO, token=HF_TOKEN, split="train", download_mode=download_mode)
+    return ds.to_list()
+
+
+print("Loading dataset from Hugging Face...")
+try:
+    dataset = fetch_dataset()
+    print(f"Loaded {len(dataset)} entries successfully!")
+except Exception as e:
+    print(f"Error loading dataset: {e}")
+    dataset = []
+
+
+@app.post("/api/refresh")
+def refresh_data():
+    global dataset
+    try:
+        dataset = fetch_dataset(force=True)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Refresh failed: {e}")
+    print(f"Refreshed dataset: {len(dataset)} entries")
+    return {"ok": True, "total_entries": len(dataset)}
+
+
+@app.get("/api/dataset")
+def get_dataset(limit: int = 100, offset: int = 0):
+    """Get dataset entries with pagination"""
+    total = len(dataset)
+    end_idx = min(offset + limit, total)
+    entries = dataset[offset:end_idx]
+    
+    return {
+        "entries": entries,
+        "total": total,
+        "offset": offset,
+        "limit": limit
+    }
+
+
+@app.get("/api/entry/{index}")
+def get_entry(index: int):
+    """Get a specific entry by index"""
+    if index < 0 or index >= len(dataset):
+        raise HTTPException(status_code=404, detail="Entry not found")
+    
+    return dataset[index]
+
+
+@app.get("/api/stats")
+def get_stats():
+    """Get dataset statistics"""
+    total_entries = len(dataset)
+    entries_with_text = sum(1 for e in dataset if e.get("text"))
+    entries_with_emotions = sum(1 for e in dataset if e.get("emotional_flow"))
+    
+    # Count unique emotions
+    emotion_counts = {}
+    for entry in dataset:
+        for flow in entry.get("emotional_flow", []):
+            label = flow.get("label", "unknown")
+            emotion_counts[label] = emotion_counts.get(label, 0) + 1
+    
+    return {
+        "total_entries": total_entries,
+        "entries_with_text": entries_with_text,
+        "entries_with_emotions": entries_with_emotions,
+        "emotion_distribution": emotion_counts
+    }
+
+
+class NoCacheStaticFiles(StaticFiles):
+    async def get_response(self, path, scope):
+        response = await super().get_response(path, scope)
+        response.headers["Cache-Control"] = "no-cache, must-revalidate"
+        return response
+
+
+app.mount("/", NoCacheStaticFiles(directory="static", html=True), name="static")
+
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)