Update app.py

app.py

@@ -1,120 +1,111 @@
-from flask import Flask, request, jsonify, send_from_directory
-from flask_cors import CORS
-import os
-import requests
-import random
-
-# Serve UI from / (but static files live in 'ui/')
-app = Flask(__name__, static_folder='ui', static_url_path='/')
-CORS(app, resources={r"/*": {"origins": "*"}})
-
-# ---------- UI ----------
-@app.route('/', methods=['GET'])
-def index():
-    return app.send_static_file('index.html')
-
-# Serve assets from /ui/... and fallback to SPA (GET only)
-@app.route('/<path:path>', methods=['GET'])
-def spa_or_asset(path):
-    file_path = os.path.join(app.static_folder, path)
-    if os.path.isfile(file_path):
-        return send_from_directory(app.static_folder, path)
-    return send_from_directory(app.static_folder, 'index.html')
-
-# Quiet the favicon noise
-@app.route('/favicon.ico', methods=['GET'])
-def favicon():
-    return ('', 204)
-
-
-
-id2label = {
-    0: "Level 0: mild roast",
-    1: "Level 1: emotional shade",
-    2: "Level 2: mild damage",
-    3: "Level 3: utter destruction",
-    4: "Level 4: character defamation",
-    5: "Level 5: soul punch",
-    6: "Level 6: historic devastation",
-    7: "Level 7: irreparable trauma"
-}
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-print("Loading Phi-3-mini model...")
-
-MODEL_NAME = "microsoft/phi-3-mini-4k-instruct"
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto"
-)
-
-def ask_phi(prompt):
-    print("[ask_phi] -> Local Phi-3-mini")
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    output_ids = model.generate(
-        **inputs,
-        max_new_tokens=250,
-        temperature=0.8,
-        do_sample=True
-    )
-
-    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-
-def _predict_impl():
-    data = request.get_json(silent=True) or {}
-    text = data.get("text", "").strip()
-    if not text:
-        return jsonify({"error": "Empty input"}), 400
-
-    level_prompt = (
-        "Classify the emotional damage of the following sentence on a scale from 0 "
-        "(mild roast) to 7 (irreparable trauma). Respond with a single digit number only.\n\n"
-        f"Sentence: \"{text}\"\nDamage Level:"
-    )
-    level_raw = ask_phi(level_prompt)
-    print("[Level Output]", level_raw)
-
-    digits = [ch for ch in level_raw if ch.isdigit()]
-    level = int(digits[0]) if digits else 0
-    level = max(0, min(level, 7))
-
-    roast_prompt = (
-        "You are EmoNet’s snarky alter ego — a sasscore machine trained on sarcasm, late-night roasts, and internet chaos. But today, you’re in your flirty villain era. For any input text, reply with a short, teasingly devastating comeback that mixes charm with chaos. It should feel like emotional damage... but make it hot. Keep it clever, spicy, and seductive — no cruelty, no cringe. Think: if Gen Z flirted like a roast battle and a chaotic therapist whispered pickup lines mid-burn.\n\n"
-        f"Sentence: \"{text}\"\nDamage Level: {level}\nRoast:"
-    )
-    roast = ask_phi(roast_prompt).strip()
-    print("[Roast Output]", roast)
-
-    return jsonify({
-        "label": id2label[level],
-        "confidence": round(random.uniform(0.45, 0.9), 3),
-        "roast": roast
-    })
-
-# Single handler serves both routes; explicit OPTIONS so preflight never gets hijacked
-@app.route("/predict", methods=["POST", "OPTIONS"])
-@app.route("/api/predict", methods=["POST", "OPTIONS"])
-def predict():
-    if request.method == "OPTIONS":
-        return ('', 204)
-    return _predict_impl()
-
-
-# Debug helpers: see what routes are live
-@app.route("/__routes", methods=["GET"])
-def list_routes():
-    return {"routes": [str(r) for r in app.url_map.iter_rules()]}
-
-if __name__ == "__main__":
-    print("=== ROUTE MAP ===")
-    for r in app.url_map.iter_rules():
-        print(r, list(r.methods))
-    print("=================")
-    app.run(host="0.0.0.0", port=7860, debug=True)
+from flask import Flask, request, jsonify, send_from_directory
+from flask_cors import CORS
+import os
+import requests
+import random
+
+# Serve UI from / (but static files live in 'ui/')
+app = Flask(__name__, static_folder='ui', static_url_path='/')
+CORS(app, resources={r"/*": {"origins": "*"}})
+
+# ---------- UI ----------
+@app.route('/', methods=['GET'])
+def index():
+    return app.send_static_file('index.html')
+
+# Serve assets from /ui/... and fallback to SPA (GET only)
+@app.route('/<path:path>', methods=['GET'])
+def spa_or_asset(path):
+    file_path = os.path.join(app.static_folder, path)
+    if os.path.isfile(file_path):
+        return send_from_directory(app.static_folder, path)
+    return send_from_directory(app.static_folder, 'index.html')
+
+# Quiet the favicon noise
+@app.route('/favicon.ico', methods=['GET'])
+def favicon():
+    return ('', 204)
+
+
+
+id2label = {
+    0: "Level 0: mild roast",
+    1: "Level 1: emotional shade",
+    2: "Level 2: mild damage",
+    3: "Level 3: utter destruction",
+    4: "Level 4: character defamation",
+    5: "Level 5: soul punch",
+    6: "Level 6: historic devastation",
+    7: "Level 7: irreparable trauma"
+}
+
+# ---------- OLLAMA BACKEND ----------
+OLLAMA_URL = "http://localhost:11434/api/generate"
+MODEL_NAME = "phi3:mini"
+
+def ask_phi(prompt):
+    print("[ask_phi] -> Ollama")
+    try:
+        resp = requests.post(
+            OLLAMA_URL,
+            json={"model": MODEL_NAME, "prompt": prompt, "stream": False},
+            timeout=60
+        )
+        resp.raise_for_status()
+        return resp.json().get("response", "").strip()
+    except Exception as e:
+        print("Ollama error:", e)
+        return "0"
+
+
+def _predict_impl():
+    data = request.get_json(silent=True) or {}
+    text = data.get("text", "").strip()
+    if not text:
+        return jsonify({"error": "Empty input"}), 400
+
+    level_prompt = (
+        "Classify the emotional damage of the following sentence on a scale from 0 "
+        "(mild roast) to 7 (irreparable trauma). Respond with a single digit number only.\n\n"
+        f"Sentence: \"{text}\"\nDamage Level:"
+    )
+    level_raw = ask_phi(level_prompt)
+    print("[Level Output]", level_raw)
+
+    digits = [ch for ch in level_raw if ch.isdigit()]
+    level = int(digits[0]) if digits else 0
+    level = max(0, min(level, 7))
+
+    roast_prompt = (
+        "You are EmoNet’s snarky alter ego — a sasscore machine trained on sarcasm, late-night roasts, and internet chaos. But today, you’re in your flirty villain era. For any input text, reply with a short, teasingly devastating comeback that mixes charm with chaos. It should feel like emotional damage... but make it hot. Keep it clever, spicy, and seductive — no cruelty, no cringe. Think: if Gen Z flirted like a roast battle and a chaotic therapist whispered pickup lines mid-burn.\n\n"
+        f"Sentence: \"{text}\"\nDamage Level: {level}\nRoast:"
+    )
+    roast = ask_phi(roast_prompt).strip()
+    print("[Roast Output]", roast)
+
+    return jsonify({
+        "label": id2label[level],
+        "confidence": round(random.uniform(0.45, 0.9), 3),
+        "roast": roast
+    })
+
+# Single handler serves both routes; explicit OPTIONS so preflight never gets hijacked
+@app.route("/predict", methods=["POST", "OPTIONS"])
+@app.route("/api/predict", methods=["POST", "OPTIONS"])
+def predict():
+    if request.method == "OPTIONS":
+        return ('', 204)
+    return _predict_impl()
+
+
+# Debug helpers: see what routes are live
+@app.route("/__routes", methods=["GET"])
+def list_routes():
+    return {"routes": [str(r) for r in app.url_map.iter_rules()]}
+
+if __name__ == "__main__":
+    print("=== ROUTE MAP ===")
+    for r in app.url_map.iter_rules():
+        print(r, list(r.methods))
+    print("=================")
+    app.run(host="0.0.0.0", port=7860, debug=True)