Update app.py

app.py

@@ -1,132 +1,130 @@
 import gradio as gr
-from transformers import pipeline, set_seed
+from transformers import pipeline
 import re
-
-# Initialize translator and simple chatbot
-translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")
-chatbot = pipeline("text-generation", model="gpt2")
-set_seed(42)
-
-# Resume data
+from typing import Dict, List, Tuple
+
+# ========== STEP 1: INITIALIZE OPTIMIZED MODELS ==========
+# Using smaller, faster models
+chatbot = pipeline(
+    "text-generation", 
+    model="distilgpt2",  # 6-layer distilled version of GPT-2 (faster)
+    device="cpu"  # Remove if you have GPU
+)
+
+# Lightweight translation model for Bangla
+translator = pipeline(
+    "translation", 
+    model="Helsinki-NLP/opus-mt-bn-en",  # Faster than nllb
+    device="cpu"
+)
+
+# ========== STEP 2: OPTIMIZED DATA STRUCTURES ==========
 resume_data = {
+    # Strings instead of nested dicts for faster access
     "name": "MD. Abdur Rahim (Ratul)",
     "profession": "Programmer",
-    "education": {
-        "SSC": "Independent Model School and College, Bogra (2019-2021) - GPA 4.29",
-        "HSC": "Shahzadpur Govt. College (2021-2023) - GPA 4.92",
-        "BSc": "Khwaja Yunus Ali University (2025–) - B.Sc in CSE, 2nd Semester, GPA 3.75"
-    },
-    "skills": {
-        "Programming Languages": "C++, C",
-        "Core Skills": "Algorithm design, Problem Solving, Logic Building",
-        "Soft Skills": "SEO, Clear verbal and written communication"
-    },
-    "languages": ["English", "Bangla"],
-    "coding_profiles": {
-        "Codeforces": "Max rating 831 (RATUL_CSE18)",
-        "Beecrowd": "448.58 points (RATUL_CSE18)",
-        "Links": "https://codeforces.com/profile/ratul_cse18"
-    },
-    "experience": [
-        "Solved 500+ problems across Codeforces, Beecrowd, and HackerRank",
-        "Regular online contest participant",
-        "Experience in data structures, algorithms, time complexity optimization",
-        "Strong debugging skills and efficient coding habits"
-    ],
-    "contact": {
-        "Phone": "+8801786500883",
-        "Email": "ratul1.cse18kyau@gmail.com",
-        "Location": "Barabil, Shahzadpur, Sirajganj, Bangladesh"
-    },
-    "birth_year": 2004,
-    "relationship": "single"
+    "education": "\n".join([
+        "SSC: Independent Model School, Bogra (4.29)",
+        "HSC: Shahzadpur Govt. College (4.92)", 
+        "BSc CSE: Khwaja Yunus Ali University (Current, 3.75)"
+    ]),
+    "skills": "\n".join([
+        "Programming: C++, C",
+        "Core: Algorithms, Problem Solving",
+        "Other: SEO, Communication"
+    ]),
+    "coding": "\n".join([
+        "Codeforces: 831 rating (RATUL_CSE18)",
+        "Beecrowd: 448.58 points",
+        "Profile: codeforces.com/profile/ratul_cse18"
+    ]),
+    "contact": "\n".join([
+        "Phone: +8801786500883",
+        "Email: ratul1.cse18kyau@gmail.com",
+        "Location: Shahzadpur, Sirajganj"
+    ])
 }
 
-# Trisha's safety filter
-def is_abusive(text):
-    abusive_keywords = ["fuck", "motherfucker", "asshole", "bitch", "suck", "rape", "shit", "dick", "pussy", "f***", "f*ck", "sex"]
-    return any(word in text.lower() for word in abusive_keywords)
-
-# Q&A
-qa_pairs = {
-    "who are you": f"I'm {resume_data['name']}, a {resume_data['profession']}.",
-    "what is your education": "\n".join(resume_data['education'].values()),
-    "what are your skills": "\n".join([f"{k}: {v}" for k, v in resume_data['skills'].items()]),
-    "coding profiles": "\n".join([f"{k}: {v}" for k, v in resume_data['coding_profiles'].items()]),
-    "experience": "\n".join(resume_data['experience']),
-    "contact": "\n".join([f"{k}: {v}" for k, v in resume_data['contact'].items()]),
-    "what languages do you speak": ", ".join(resume_data["languages"]),
-    "where are you from": resume_data["contact"]["Location"],
-    "are you single": "Sorry, I don't share personal relationship status.",
-    "what is your birth year": "That's private. Please ask about my boss Ratul."
+# ========== STEP 3: PRE-COMPUTED ANSWERS ==========
+QA = {
+    # Direct matches (fastest)
+    "who": f"I'm {resume_data['name']}, a {resume_data['profession']}.",
+    "education": resume_data["education"],
+    "skills": resume_data["skills"],
+    "code": resume_data["coding"],
+    "contact": resume_data["contact"],
+    
+    # Pattern-based
+    "single": "I don't share relationship status.",
+    "age": "That's private. Ask about my coding skills!",
+    "project": "Ratul offers affordable projects. Interested?",
+    
+    # Default
+    "fallback": "Ask about my education, skills, or coding profiles!"
 }
 
-# Detect and translate Bangla to English
-def detect_and_translate(text):
-    bangla_chars = set('অআইঈউঊঋএঐওঔকখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়')
-    if any(char in bangla_chars for char in text):
-        translated = translator(text, src="ben_Beng", tgt="eng_Latn")
-        return translated[0]['translation_text']
-    return text
-
-# Response logic
-warned_once = False
-
-def answer_question(question, history):
-    global warned_once
-    question = question.strip()
-    question_en = detect_and_translate(question.lower())
-
-    if is_abusive(question_en):
-        if not warned_once:
-            warned_once = True
-            return "My boss Ratul says: If you use abusive language, we call 999 (Bangladesh National Helpline). Please stop joking and ask about my boss Ratul."
-        else:
-            return "Hey mother fuc*ker !! Stop joking. I am coming to your home for f*ck your mom !! STOP !!", gr.update(visible=False)
-
-    for q, a in qa_pairs.items():
-        if q in question_en:
-            return a
-
-    # Smart response
-    prompt = f"""You're Trisha, a helpful assistant of MD. Abdur Rahim (Ratul), a programmer.
-If user gets too personal, politely decline. If they ask about work, projects, or skills — answer smartly.
-Occasionally mention: "My boss Ratul offers many small projects at very usual prices. If needed, ask me to contact him."
-
-Resume Summary:
-Name: {resume_data['name']}
-Profession: {resume_data['profession']}
-Education: {", ".join(resume_data['education'].values())}
-Skills: {", ".join(resume_data['skills'].values())}
-Languages: {", ".join(resume_data['languages'])}
-Experience: {"; ".join(resume_data['experience'])}
-Contact: {", ".join(resume_data['contact'].values())}
+# ========== STEP 4: OPTIMIZED PROCESSING ==========
+ABUSIVE_WORDS = {"fuck", "ass", "bitch", "shit", "rape"}  # Set for O(1) lookups
 
-User: {question_en}
-Trisha:"""
+def is_abusive(text: str) -> bool:
+    return any(word in text.lower() for word in ABUSIVE_WORDS)
 
-    response = chatbot(prompt, max_length=200, do_sample=True)
-    text = response[0]['generated_text'].split("Trisha:")[-1].strip()
+def detect_bangla(text: str) -> bool:
+    return any('\u0980' <= char <= '\u09FF' for char in text)
 
-    # Sometimes say the promo
-    if "project" not in text.lower() and "ratul" not in text.lower():
-        if "?" not in question_en and "you" not in question_en:
-            text += "\n\nP.S. My boss Ratul offers many small projects at a very low price. Ask if interested!"
+def fast_translate(text: str) -> str:
+    if detect_bangla(text):
+        return translator(text, max_length=60)[0]['translation_text']
     return text
 
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Hi! I am Trisha, Ratul's assistant. How can I help you today?")
-    chatbot_output = gr.Textbox(label="Answer", interactive=False, lines=6)
-    chatbot_input = gr.Textbox(label="You", placeholder="Type in English or Bangla...")
-    submit = gr.Button("Send")
+def generate_response(prompt: str) -> str:
+    response = chatbot(
+        prompt,
+        max_new_tokens=100,
+        do_sample=False,  # Faster than sampling
+        num_beams=1  # Faster than beam search
+    )
+    return response[0]['generated_text'].split("Trisha:")[-1].strip()
+
+# ========== STEP 5: OPTIMIZED CHAT LOGIC ==========
+def answer_question(question: str) -> str:
+    question = question.strip().lower()
+    question_en = fast_translate(question)
     
-    def process_chat(text):
-        result = answer_question(text, None)
-        if isinstance(result, tuple):
-            return result[0], gr.update(visible=False)
-        return result
-
-    submit.click(process_chat, inputs=chatbot_input, outputs=chatbot_output)
-
-demo.launch()
+    # 1. Safety check
+    if is_abusive(question_en):
+        return "⚠️ Keep it professional or I'll disconnect!"
+    
+    # 2. Check pre-computed answers (fast path)
+    if "who" in question_en: return QA["who"]
+    if "educat" in question_en: return QA["education"]
+    if "skill" in question_en: return QA["skills"]
+    if "code" in question_en: return QA["code"]
+    if "contact" in question_en: return QA["contact"]
+    if "single" in question_en: return QA["single"]
+    if "age" in question_en or "birth" in question_en: return QA["age"]
+    if "project" in question_en: return QA["project"]
+    
+    # 3. Generate only when necessary (slower path)
+    prompt = f"""Trisha (Ratul's Assistant):
+Q: {question_en}
+A:"""
+    return generate_response(prompt)
+
+# ========== STEP 6: LIGHTNING-FAST GRADIO UI ==========
+with gr.Blocks(title="⚡ Trisha Chatbot") as demo:
+    gr.Markdown(f"## 🤖 Hi! I'm Trisha, {resume_data['name']}'s assistant")
+    
+    with gr.Row():
+        user_input = gr.Textbox(label="Ask anything...", placeholder="Type in English/Bangla")
+        output = gr.Textbox(label="Response", lines=5)
+    
+    user_input.submit(
+        fn=answer_question,
+        inputs=user_input,
+        outputs=output,
+        api_name="chat"
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_port=7860)