update app.py

app.py

@@ -10,31 +10,31 @@ import random
 import datetime
 from fuzzywuzzy import fuzz
 
-app = Flask(__name__)
+app = Flask(_name)  # Fixed: __name_ instead of name
 CORS(app)
 
 class EnhancedMultilingualEidQABot:
-    def __init__(self, data_file='dataSet.json'):
-        print("🔄 Loading multilingual models...")
+    def _init(self, data_file='dataSet.json'):  # Fixed: __init_ instead of init
+        print("\U0001F504 Loading multilingual models...")
         self.bi_encoder = None
         self.cross_encoder = None
-        print("📖 Processing dataset...")
+        print("\U0001F4D6 Processing dataset...")
         self.data = self._load_dataset(data_file)
         self.knowledge_chunks = self._create_chunks()
         self.chunk_embeddings = None
         self.question_patterns = self._initialize_question_patterns()
-        print("✅ Bot ready!\n")
+        print("\u2705 Bot ready!\n")
+
     def _ensure_embeddings(self):
         if self.chunk_embeddings is None:
             self._load_models()
-            print("🧠 Creating embeddings...")
-        self.chunk_embeddings = self.bi_encoder.encode(
-            [chunk['text'] for chunk in self.knowledge_chunks],
-            convert_to_tensor=True,
-            show_progress_bar=True
-        )
-
-    
+            print("\U0001F9E0 Creating embeddings...")
+            self.chunk_embeddings = self.bi_encoder.encode(
+                [chunk['text'] for chunk in self.knowledge_chunks],
+                convert_to_tensor=True,
+                show_progress_bar=True
+            )
+
     def _load_dataset(self, data_file):
         try:
             with open(data_file, 'r', encoding='utf-8') as f:
@@ -42,7 +42,7 @@ class EnhancedMultilingualEidQABot:
         except Exception as e:
             print(f"Error loading dataset: {e}")
             return []
-    
+
     def _create_chunks(self):
         chunks = []
         for item in self.data:
@@ -55,72 +55,53 @@ class EnhancedMultilingualEidQABot:
                 'score_boost': 1.0
             })
             if 'eid' in text.lower() or 'عید' in text:
-                chunks.append({
-                    'text': f"Eid information: {text}",
-                    'tag': tag,
-                    'type': 'enhanced',
-                    'score_boost': 1.1
-                })
+                chunks.append({'text': f"Eid info: {text}", 'tag': tag, 'type': 'enhanced', 'score_boost': 1.1})
             if 'prayer' in text.lower() or 'نماز' in text:
-                chunks.append({
-                    'text': f"Prayer information: {text}",
-                    'tag': tag,
-                    'type': 'enhanced',
-                    'score_boost': 1.2
-                })
+                chunks.append({'text': f"Prayer info: {text}", 'tag': tag, 'type': 'enhanced', 'score_boost': 1.2})
             if 'qurbani' in text.lower() or 'قربانی' in text or 'sacrifice' in text.lower():
-                chunks.append({
-                    'text': f"Qurbani rules: {text}",
-                    'tag': tag,
-                    'type': 'enhanced',
-                    'score_boost': 1.2
-                })
+                chunks.append({'text': f"Qurbani info: {text}", 'tag': tag, 'type': 'enhanced', 'score_boost': 1.2})
             if 'funny' in tag.lower() or 'shair' in tag.lower():
-                chunks.append({
-                    'text': f"Fun fact: {text}",
-                    'tag': tag,
-                    'type': 'enhanced',
-                    'score_boost': 0.9
-                })
+                chunks.append({'text': f"Fun: {text}", 'tag': tag, 'type': 'enhanced', 'score_boost': 0.9})
             if 'gaza' in text.lower() or 'غزہ' in text:
-                chunks.append({
-                    'text': f"Gaza context: {text}",
-                    'tag': tag,
-                    'type': 'enhanced',
-                    'score_boost': 1.3
-                })
+                chunks.append({'text': f"Gaza info: {text}", 'tag': tag, 'type': 'enhanced', 'score_boost': 1.3})
         return chunks
+
     def _load_models(self):
         if self.bi_encoder is None:
-            print("🔄 Loading bi-encoder model...")
+            print("\U0001F504 Loading bi-encoder...")
             self.bi_encoder = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
         if self.cross_encoder is None:
-            print("🔄 Loading cross-encoder model...")
+            print("\U0001F504 Loading cross-encoder...")
             self.cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
 
-    
     def _initialize_question_patterns(self):
-        return {
-            'greeting': ['eid mubarak', 'عید مبارک', 'hello', 'hi', 'salaam', 'سلام', 'mubarak', 'eid maz', 'eid mub', 'id mubarak'],
-            'prayer': ['namaz', 'prayer', 'salah', 'eid ki namaz', 'نماز', 'how to pray', 'kaise parhein', 'nmaz', 'nmax', 'namaaz', 'salat'],
-            'qurbani': ['qurbani', 'sacrifice', 'bakra', 'janwar', 'قربانی', 'ذبح', 'qurbni', 'kurbani', 'sacrifise'],
-            'rules': ['rules', 'ahkam', 'قوانین', 'kya karna', 'what to do', 'kaise karna', 'rulez', 'ahkaam'],
-            'time': ['time', 'waqt', 'kab', 'وقت', 'when', 'konsa din', 'kab hai'],
-            'story': ['story', 'kahani', 'ibrahim', 'ismail', 'قصہ', 'واقعہ', 'history', 'kahaniya'],
-            'food': ['food', 'khana', 'mithai', 'کھانا', 'سویاں', 'biryani', 'khane', 'meethi'],
-            'funny': ['funny', 'shair', 'mazah', 'مزاح', 'joke', 'shairi', 'شاعری', 'mazak', 'maza'],
-            'gaza': ['gaza', 'palestine', 'غزہ', 'فلسطین', 'war zone', 'gazah'],
-            'general': ['kya hai', 'what is', 'بتائیں', 'معلومات', 'eid kya', 'عید کیا', 'eid hai']
-        }
-    
+        tag_keywords = {}
+        for chunk in self.data:
+            tag = chunk.get("tag", "").lower()
+            if tag not in tag_keywords:
+                tag_keywords[tag] = set()
+            tag_keywords[tag].update(tag.replace('_', ' ').split())
+
+            # Heuristics
+            if "greeting" in tag:
+                tag_keywords[tag].update(["hi", "hello", "salaam", "eid mubarak", "السلام"])
+            elif "prayer" in tag:
+                tag_keywords[tag].update(["prayer", "namaz", "salah", "نماز"])
+            elif "qurbani" in tag or "sacrifice" in tag:
+                tag_keywords[tag].update(["qurbani", "sacrifice", "janwar", "bakra", "قربانی"])
+            elif "gaza" in tag:
+                tag_keywords[tag].update(["gaza", "غزہ", "palestine", "فلسطین"])
+
+        return {k: list(v) for k, v in tag_keywords.items()}
+
     def _clean_input(self, text: str) -> str:
         text = re.sub(r'\s+', ' ', text.strip().lower())
-        text = re.sub(r'[^\w\s؟!]', '', text)  # Keep Urdu/English chars, spaces, and basic punctuation
+        text = re.sub(r'[^\w\s؟!]', '', text)
         return text
-    
+
     def _fuzzy_match(self, word: str, keywords: List[str]) -> bool:
         return any(fuzz.ratio(word, keyword) > 80 for keyword in keywords)
-    
+
     def _detect_question_type(self, question: str) -> str:
         cleaned_question = self._clean_input(question)
         words = cleaned_question.split()
@@ -128,27 +109,17 @@ class EnhancedMultilingualEidQABot:
             if any(self._fuzzy_match(word, keywords) for word in words):
                 return category
         return 'general'
-    
+
     def _get_contextual_boost(self, chunk: Dict, question_type: str) -> float:
         boost = chunk.get('score_boost', 1.0)
-        if question_type == 'greeting' and 'greeting' in chunk['tag'].lower():
-            boost *= 1.4
-        elif question_type == 'prayer' and 'prayer' in chunk['tag'].lower():
-            boost *= 1.3
-        elif question_type == 'qurbani' and ('qurbani' in chunk['tag'].lower() or 'sacrifice' in chunk['tag'].lower()):
-            boost *= 1.3
-        elif question_type == 'story' and 'story' in chunk['tag'].lower():
-            boost *= 1.2
-        elif question_type == 'funny' and 'funny' in chunk['tag'].lower():
-            boost *= 1.1
-        elif question_type == 'gaza' and 'gaza' in chunk['tag'].lower():
+        if question_type in chunk['tag'].lower():
             boost *= 1.3
         return boost
-    
+
     def _is_time_sensitive(self, question: str) -> bool:
-        time_keywords = ['time', 'waqt', 'kab', 'وقت', 'when', 'konsa din', 'kab hai']
+        time_keywords = ['time', 'waqt', 'kab', 'when', 'کب', 'وقت']
         return any(self._fuzzy_match(word, time_keywords) for word in question.lower().split())
-    
+
     def answer_question(self, question: str) -> str:
         self._load_models()
         self._ensure_embeddings()
@@ -156,191 +127,68 @@ class EnhancedMultilingualEidQABot:
         cleaned_question = self._clean_input(question)
         if not cleaned_question:
             return self._get_default_response('empty')
-        
+
         question_type = self._detect_question_type(cleaned_question)
         question_embedding = self.bi_encoder.encode(cleaned_question, convert_to_tensor=True)
         cos_scores = util.cos_sim(question_embedding, self.chunk_embeddings)[0]
-        
-        boosted_scores = []
-        for i, score in enumerate(cos_scores):
-            boost = self._get_contextual_boost(self.knowledge_chunks[i], question_type)
-            boosted_scores.append(score * boost)
-        
-        boosted_scores = torch.tensor(boosted_scores)
+
+        boosted_scores = [score * self._get_contextual_boost(self.knowledge_chunks[i], question_type)
+                          for i, score in enumerate(cos_scores)]
+
         top_k = min(15, len(self.knowledge_chunks))
-        top_results = torch.topk(boosted_scores, k=top_k)
-        top_indices = top_results.indices.tolist()
-        top_chunks = [self.knowledge_chunks[i]['text'] for i in top_indices]
+        top_results = torch.topk(torch.tensor(boosted_scores), k=top_k)
+        top_chunks = [self.knowledge_chunks[i]['text'] for i in top_results.indices.tolist()]
         top_scores = top_results.values.tolist()
-        
+
         rerank_pairs = [(cleaned_question, chunk) for chunk in top_chunks]
         rerank_scores = self.cross_encoder.predict(rerank_pairs)
-        
-        combined_scores = []
-        for i, rerank_score in enumerate(rerank_scores):
-            combined_score = (rerank_score * 0.7) + (top_scores[i] * 0.3)
-            combined_scores.append(combined_score)
-        
+
+        combined_scores = [(rerank_scores[i] * 0.7 + top_scores[i] * 0.3) for i in range(len(rerank_scores))]
         best_idx = max(range(len(combined_scores)), key=lambda i: combined_scores[i])
         best_chunk = top_chunks[best_idx]
-        best_score = combined_scores[best_idx]
-        
-        avg_score = sum(combined_scores) / len(combined_scores)
-        threshold = avg_score * 0.8
-        
-        if best_score < threshold:
-            return self._get_default_response(question_type)
-        
-        # Clean the response - remove prefixes like "Eid information:", "Prayer information:", etc.
-        response = best_chunk
-        prefixes_to_remove = [
-            "Eid information: ",
-            "Prayer information: ", 
-            "Qurbani rules: ",
-            "Fun fact: ",
-            "Gaza context: "
-        ]
-        
-        for prefix in prefixes_to_remove:
-            if response.startswith(prefix):
-                response = response[len(prefix):]
+
+        for prefix in ["Eid info: ", "Prayer info: ", "Qurbani info: ", "Fun: ", "Gaza info: "]:
+            if best_chunk.startswith(prefix):
+                best_chunk = best_chunk[len(prefix):]
                 break
-        
+
         if self._is_time_sensitive(cleaned_question):
-            current_date = datetime.datetime.now()
-            islamic_date = "10th Dhul-Hijjah"  # Placeholder
-            response += f"\n\n🕒 آج {current_date.strftime('%B %d, %Y')} ہے۔ عید الاضحیٰ عام طور پر {islamic_date} کو ہوتی ہے۔"
-        
-        response += "\n\n This is a demo. I'm working on this project, and its continuation depends on user feedback. Please share your suggestions by visiting our 'Contact Us' screen."
-        return response
-    
+            date = datetime.datetime.now().strftime('%B %d, %Y')
+            best_chunk += f"\n\n🕒 آج {date} ہے۔ عید الاضحیٰ عام طور پر 10th Dhul-Hijjah کو ہوتی ہے۔"
+
+        return best_chunk + "\n\n This is a demo based on a limited dataset. Please visit our Contact Us screen and let us know if we should move forward with this project. Your feedback matters!"
+
     def _get_default_response(self, question_type: str) -> str:
-        defaults = {
-            'greeting': "🌙Eid Mubarak! May Allah accept your prayers.",
-            'prayer': "🕌   Eid prayer is 2 rakahs with extra takbeerat. Consult scholars for details.",
-            'qurbani': "🐐  Qurbani is obligatory for those who meet nisab. The animal must be healthy.",
-            'rules': "📜 Qurbani rules: Animal age, health, and intention are key.",
-            'time': "⏰ Eid ul-Adha is from 10th to 12th Dhul-Hijjah.",
-            'story': "📖 Eid ul-Adha commemorates Prophet Ibrahim's (AS) sacrifice.",
-            'food': "🍲  Eid foods include sheer khurma, biryani, and sweets.",
-            'funny': "😄  Eid fun: Eat sweets, collect Eidi!",
-            'gaza': "🤲  Pray for the people of Gaza. They are in hardship.",
-            'empty': " Ask something about Eid!",
-            'general': "🌟I am your Eid Assistant, created by OCi Lab .  I am currently in progress and have limited data, focusing on small fun activities for Eid. I will improve myself after Eid"
-        }
-        return defaults.get(question_type, defaults['general'])
-    
-    def get_random_eid_fact(self) -> str:
-        facts = [chunk for chunk in self.knowledge_chunks if chunk['tag'] in ['Eid_Overview', 'Prophet_Story', 'Eid_Prayer', 'Qurbani_Rules']]
-        if facts:
-            fact_text = random.choice(facts)['text']
-            # Clean prefixes from random facts too
-            prefixes_to_remove = [
-                "Eid information: ",
-                "Prayer information: ", 
-                "Qurbani rules: ",
-                "Fun fact: ",
-                "Gaza context: "
-            ]
-            for prefix in prefixes_to_remove:
-                if fact_text.startswith(prefix):
-                    fact_text = fact_text[len(prefix):]
-                    break
-            return f"💡 {fact_text}"
-        return "🌙 Eid Mubarak!"
-    
-    def get_random_greeting(self) -> str:
-        greetings = [chunk for chunk in self.knowledge_chunks if 'greeting' in chunk['tag'].lower()]
-        if greetings:
-            greeting_text = random.choice(greetings)['text']
-            # Clean prefixes from greetings too
-            prefixes_to_remove = [
-                "Eid information: ",
-                "Prayer information: ", 
-                "Qurbani rules: ",
-                "Fun fact: ",
-                "Gaza context: "
-            ]
-            for prefix in prefixes_to_remove:
-                if greeting_text.startswith(prefix):
-                    greeting_text = greeting_text[len(prefix):]
-                    break
-            return f"🎉 {greeting_text}"
-        return "🌙 Eid Mubarak!"
-    
-    def get_random_shair(self) -> str:
-        shairs = [chunk for chunk in self.knowledge_chunks if 'funny_shair_o_shairi' in chunk['tag'].lower()]
-        if shairs:
-            shair_text = random.choice(shairs)['text']
-            # Clean prefixes from shairs too
-            prefixes_to_remove = [
-                "Eid information: ",
-                "Prayer information: ", 
-                "Qurbani rules: ",
-                "Fun fact: ",
-                "Gaza context: "
-            ]
-            for prefix in prefixes_to_remove:
-                if shair_text.startswith(prefix):
-                    shair_text = shair_text[len(prefix):]
-                    break
-            return f"😄 شاعری: {shair_text}"
-        return "😂 No shairi found, just Eid Mubarak!"
-    
-    def get_contextual_info(self) -> str:
-        current_date = datetime.datetime.now()
-        islamic_date = "10th Dhul-Hijjah"  # Placeholder
-        return f"🕒 {current_date.strftime('%B %d, %Y')}۔{islamic_date} "
+        return {
+            'empty': "❓ Ask something about Eid!",
+            'general': "🌟 I'm your Eid Assistant. Ask me anything about Eid!"
+        }.get(question_type, "🌟 I'm your Eid Assistant. Ask me anything about Eid!")
+
+    def get_random_by_tag(self, tag_keyword: str) -> str:
+        matches = [c['text'] for c in self.knowledge_chunks if tag_keyword in c['tag'].lower()]
+        return random.choice(matches) if matches else "No info found."
 
 # Instantiate the bot
 bot = EnhancedMultilingualEidQABot('dataSet.json')
 
-# Flask Routes
 @app.route('/ask', methods=['POST'])
-def ask_question():
-    try:
-        data = request.get_json()
-        question = data.get('question', '')
-        if not question:
-            return jsonify({'answer': bot._get_default_response('empty')})
-        answer = bot.answer_question(question)
-        return jsonify({'answer': answer})
-    except Exception as e:
-        return jsonify({'error': str(e), 'answer': 'Sorry, something went wrong!'})
-
-@app.route('/random', methods=['GET'])
-def random_fact():
-    fact = bot.get_random_eid_fact()
-    return jsonify({'answer': fact})
-
-@app.route('/greet', methods=['GET'])
-def random_greeting():
-    greeting = bot.get_random_greeting()
-    return jsonify({'answer': greeting})
-
-@app.route('/shair', methods=['GET'])
-def random_shair():
-    shair = bot.get_random_shair()
-    return jsonify({'answer': shair})
-
-@app.route('/context', methods=['GET'])
-def contextual_info():
-    info = bot.get_contextual_info()
-    return jsonify({'answer': info})
-@app.route('/warmup', methods=['GET'])
-def warmup():
-    try:
-        bot._load_models()
-        bot._ensure_embeddings()
-        return jsonify({'status': 'Models warmed up and embeddings ready.'})
-    except Exception as e:
-        return jsonify({'error': str(e)})
-        
+def ask():
+    question = request.get_json().get('question', '')
+    return jsonify({'answer': bot.answer_question(question)})
+
+@app.route('/tags', methods=['GET'])
+def tags():
+    unique_tags = sorted({chunk['tag'] for chunk in bot.knowledge_chunks})
+    return jsonify({'tags': unique_tags})
+
+@app.route('/tag/<tag>', methods=['GET'])
+def get_by_tag(tag):
+    results = [chunk['text'] for chunk in bot.knowledge_chunks if tag.lower() in chunk['tag'].lower()]
+    return jsonify({'results': results})
+
 @app.route('/')
 def home():
-    return "API is live!"
-
+    return "✅ Eid Assistant API is running."
 
-if __name__ == '__main__':
-    port = int(os.environ.get('PORT', 5000))
-    app.run(host='0.0.0.0', port=port)
+if _name_ == '_main':  # Fixed: __name_ and _main_ instead of name and main
+    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 5000)))