Create app.py

app.py

@@ -0,0 +1,199 @@
+import os
+import traceback
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from dotenv import load_dotenv
+from openai import OpenAI
+from langdetect import detect
+from deep_translator import GoogleTranslator
+import subprocess
+
+# Patch huggingface_hub automatically if Gradio overwrote it
+try:
+    import huggingface_hub
+    if not hasattr(huggingface_hub, "cached_download"):
+        subprocess.run(
+            ["pip", "install", "--no-cache-dir", "huggingface-hub==0.24.5", "transformers==4.30.2", "sentence-transformers==2.2.2"],
+            check=True
+        )
+        print("✅ Downgraded huggingface-hub for sentence-transformers compatibility.")
+except Exception as e:
+    print("⚠️ Could not auto-patch huggingface_hub:", e)
+from sentence_transformers import SentenceTransformer
+from pinecone import Pinecone
+
+# ---------- Config ----------
+DATASET_PATH = "data/coaching_millionaer_dataset.json"
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")  # Add this to your .env
+PINECONE_INDEX_NAME = "ebook"
+
+# ---------- App ----------
+app = Flask(__name__)
+CORS(app, resources={r"/ask": {"origins": "*"}})
+
+# ---------- OpenAI Client ----------
+client = None
+if OPENAI_API_KEY:
+    client = OpenAI(api_key=OPENAI_API_KEY)
+else:
+    print("⚠️  OPENAI_API_KEY is missing in .env")
+
+# ---------- Retriever ----------
+retriever = None
+try:
+    if not PINECONE_API_KEY:
+        raise ValueError("PINECONE_API_KEY missing in .env")
+
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+    index = pc.Index(PINECONE_INDEX_NAME)
+    embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+    class PineconeRetriever:
+        def __init__(self, index, embedder):
+            self.index = index
+            self.embedder = embedder
+
+        def retrieve(self, query, top_k=10):
+            emb = self.embedder.encode(query).tolist()
+            res = self.index.query(vector=emb, top_k=top_k, include_metadata=True)
+            matches = res.get("matches", [])
+            results = []
+            for match in matches:
+                meta = match.get("metadata", {})
+                results.append({
+                    "context": meta.get("context", ""),
+                    "page": meta.get("page"),
+                    "score": match.get("score", 0)
+                })
+            return results
+
+    retriever = PineconeRetriever(index, embedder)
+    print("✅ Pinecone retriever initialized successfully.")
+except Exception as e:
+    print("❌ Retriever initialization failed:", e)
+    traceback.print_exc()
+
+# ---------- Translator ----------
+def translate_text(text: str, target_lang: str) -> str:
+    """Translate text using deep-translator (GoogleTranslator)."""
+    try:
+        return GoogleTranslator(source="auto", target=target_lang).translate(text)
+    except Exception:
+        return text
+
+# ---------- Helpers ----------
+def detect_language(question: str) -> str:
+    """Detect the user's language without translation."""
+    try:
+        return detect(question)
+    except Exception:
+        return "unknown"
+
+def normalize_language(lang: str, text: str) -> str:
+    """Fix incorrect language detection like 'wer is' → German."""
+    if lang == "nl" and any(word in text.lower() for word in ["wer", "was", "wie", "javid", "coaching"]):
+        return "de"
+    return lang
+
+def system_prompt_book_only() -> str:
+    return (
+        "You are CoachingBot, a professional mentor trained on the book 'Coaching Millionär' by Javid Niazi-Hoffmann. "
+        "Use only the provided book context to answer the question. "
+        "If the user asks about people like Javid Niazi-Hoffmann, describe them factually using the book content. "
+        "Mention page numbers where possible. "
+        "If the context is not relevant, say you don’t have that information in the book and provide a general, helpful answer. "
+        "Always respond in the same language as the user's question, even if the book content is in another language."
+    )
+
+def system_prompt_fallback() -> str:
+    return (
+        "You are CoachingBot, a helpful business and life mentor. "
+        "The question cannot be answered from the book, so answer using your general coaching knowledge. "
+        "Always respond in the same language as the user's question, even if the book content is in another language. "
+        "Do not invent book citations."
+    )
+
+def format_answers(question: str, answer: str, results):
+    pages = [f"Seite {r.get('page', '')}" for r in results if r.get("page")]
+    source = ", ".join(pages) if pages else "No source"
+    top_score = max([r.get("score", 0.0) for r in results], default=0.0)
+    return {"answers": [{"question": question, "answer": answer, "source": source, "bm25_score": top_score}]}
+
+# ---------- Routes ----------
+@app.route("/", methods=["GET"])
+def health():
+    return jsonify({
+        "status": "running",
+        "retriever_ready": bool(retriever),
+        "openai_key_loaded": bool(OPENAI_API_KEY),
+        "pinecone_key_loaded": bool(PINECONE_API_KEY),
+        "index_name": PINECONE_INDEX_NAME
+    })
+
+@app.route("/ask", methods=["POST", "OPTIONS"])
+def ask():
+    if request.method == "OPTIONS":
+        return ("", 204)
+
+    try:
+        data = request.get_json(force=True) or {}
+        question = (data.get("question") or "").strip()
+    except Exception:
+        return jsonify(format_answers("", "Invalid JSON request", [])), 200
+
+    if not question:
+        return jsonify(format_answers("", "Please enter a question.", [])), 200
+
+    print(f"\n--- User Question ---\n{question}")
+
+    # Detect and normalize language
+    user_lang = normalize_language(detect_language(question), question)
+    print(f"Detected language: {user_lang}")
+
+    # Retrieve context
+    context, results = "", []
+    try:
+        raw_results = retriever.retrieve(question)
+        MIN_SCORE = 0.10  # Pinecone similarity scores are normalized (0–1)
+        results = [r for r in raw_results if r.get("score", 0) >= MIN_SCORE]
+        if results:
+            context = "\n\n---\n\n".join(
+                [f"(Seite {r['page']}) {r['context']}" for r in results]
+            )
+    except Exception as e:
+        traceback.print_exc()
+        return jsonify(format_answers(question, f"Retriever error: {e}", [])), 200
+
+    # Build prompts
+    if context:
+        sys_prompt = system_prompt_book_only()
+        user_content = f"Question: {question}\n\nBook context:\n{context}"
+    else:
+        sys_prompt = system_prompt_fallback()
+        user_content = question
+
+    # Query GPT
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[
+                {"role": "system", "content": sys_prompt},
+                {"role": "user", "content": user_content}
+            ],
+            max_tokens=700,
+        )
+        answer = response.choices[0].message.content.strip()
+    except Exception as e:
+        traceback.print_exc()
+        return jsonify(format_answers(question, f"⚠️ OpenAI call failed: {e}", [])), 200
+
+    return jsonify(format_answers(question, answer, results))
+
+# ---------- Run ----------
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    print(f"🚀 Server started on port {port}")
+    app.run(host="0.0.0.0", port=port)