Update app.py

app.py

@@ -1,32 +1,138 @@
 # --------------------------------------------------------------
-# IGCSE/GCSE Language Platform – 100% FREE with Google Gemini API
-# Model: Google Gemini Pro (FREE tier - No payment required!)
+# IGCSE/GCSE Language Platform – Multi-AI System (Z.ai + Cohere + MiniMax + Gemini)
+# Models: Z.ai GLM-4.6 (Primary) → Cohere → MiniMax → Gemini (Fallbacks)
 # --------------------------------------------------------------
 
 import os
 import json
 from datetime import datetime
 import gradio as gr
-import google.generativeai as genai
 import PyPDF2
-import io
-
-# ---------- 1. Configure Google Gemini API (FREE) ----------
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-if not GEMINI_API_KEY:
-    raise gr.Error("Set GEMINI_API_KEY in Secrets (Settings → Repository Secrets)")
-
-genai.configure(api_key=GEMINI_API_KEY)
-
-# Use Gemini Pro (FREE tier includes generous limits)
-model = genai.GenerativeModel('gemini-pro')
+import time
+
+# ---------- 1. Configure ALL AI Systems ----------
+# Z.ai (Primary)
+try:
+    from huggingface_hub import InferenceClient
+    zai_client = InferenceClient(
+        provider="novita",
+        api_key=os.environ.get("HF_TOKEN"),
+    )
+    print("✅ Z.ai GLM-4.6 initialized successfully")
+except Exception as e:
+    print(f"⚠️ Error initializing Z.ai: {e}")
+    zai_client = None
+
+# Cohere (Secondary)
+try:
+    import cohere
+    cohere_client = cohere.Client(os.getenv("COHERE_API_KEY"))
+    print("✅ Cohere initialized successfully")
+except Exception as e:
+    print(f"⚠️ Error initializing Cohere: {e}")
+    cohere_client = None
+
+# MiniMax (Tertiary)
+try:
+    minimax_client = InferenceClient(
+        provider="novita",
+        api_key=os.environ.get("HF_TOKEN"),
+    )
+    print("✅ MiniMax AI initialized successfully")
+except Exception as e:
+    print(f"⚠️ Error initializing MiniMax: {e}")
+    minimax_client = None
+
+# Gemini (Final Fallback)
+try:
+    import google.generativeai as genai
+    genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+    gemini_model = genai.GenerativeModel('gemini-pro')
+    print("✅ Gemini AI initialized successfully")
+except Exception as e:
+    print(f"⚠️ Error initializing Gemini: {e}")
+    gemini_model = None
+
+# ---------- 2. Unified AI Function with Smart Fallback ----------
+def ask_ai(prompt, temperature=0.7, max_retries=2):
+    """
+    Try models in order: Z.ai → Cohere → MiniMax → Gemini
+    Returns: (response_text, source_name)
+    """
+    last_error = None
+    
+    # Try Z.ai first (Primary)
+    if zai_client:
+        for attempt in range(max_retries):
+            try:
+                completion = zai_client.chat.completions.create(
+                    model="zai-org/GLM-4.6",
+                    messages=[{"role": "user", "content": prompt}],
+                    temperature=temperature
+                )
+                return completion.choices[0].message.content, "zai"
+            except Exception as e:
+                last_error = e
+                print(f"⚠️ Z.ai attempt {attempt+1} failed: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(1)
+    
+    # Try Cohere (Secondary)
+    if cohere_client:
+        for attempt in range(max_retries):
+            try:
+                response = cohere_client.chat(
+                    model="command-r-plus-08-2024",
+                    message=prompt,
+                    temperature=temperature
+                )
+                return response.text, "cohere"
+            except Exception as e:
+                last_error = e
+                print(f"⚠️ Cohere attempt {attempt+1} failed: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(1)
+    
+    # Try MiniMax (Tertiary)
+    if minimax_client:
+        for attempt in range(max_retries):
+            try:
+                completion = minimax_client.chat.completions.create(
+                    model="MiniMaxAI/MiniMax-M2",
+                    messages=[{"role": "user", "content": prompt}],
+                    temperature=temperature
+                )
+                return completion.choices[0].message.content, "minimax"
+            except Exception as e:
+                last_error = e
+                print(f"⚠️ MiniMax attempt {attempt+1} failed: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(1)
+    
+    # Try Gemini (Final Fallback)
+    if gemini_model:
+        try:
+            response = gemini_model.generate_content(
+                prompt,
+                generation_config=genai.types.GenerationConfig(
+                    temperature=temperature,
+                )
+            )
+            return response.text, "gemini"
+        except Exception as e:
+            last_error = e
+            print(f"⚠️ Gemini fallback failed: {str(e)}")
+    
+    # All failed
+    error_msg = f"⚠️ Error: All AI services failed. Last error: {str(last_error)}"
+    return error_msg, "error"
 
-# ---------- 2. Global storage ----------
+# ---------- 3. Global storage ----------
 papers_storage = []
-pdf_content_storage = {}  # Store extracted PDF text
+pdf_content_storage = {}
 ADMIN_PASSWORD = "@mikaelJ46"
 
-# ---------- 3. Topic lists ----------
+# ---------- 4. Topic lists ----------
 french_topics = [
     "Greetings & Introductions", "Family & Relationships", "Daily Routines",
     "Food & Restaurants", "Shopping & Money", "Travel & Transport",
@@ -46,24 +152,6 @@ efl_topics = [
     "Listening Comprehension"
 ]
 
-# ---------- 4. Helper: call Gemini model ----------
-def call_gemini(prompt: str, system_instruction: str = ""):
-    """Call Google Gemini API - 100% FREE"""
-    try:
-        # Combine system instruction with prompt
-        full_prompt = f"{system_instruction}\n\n{prompt}" if system_instruction else prompt
-        
-        response = model.generate_content(
-            full_prompt,
-            generation_config=genai.types.GenerationConfig(
-                temperature=0.7,
-                max_output_tokens=2000,
-            )
-        )
-        return response.text.strip()
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}\n\nPlease check your GEMINI_API_KEY in Settings → Secrets"
-
 # ---------- 5. PDF Processing ----------
 def extract_text_from_pdf(pdf_file):
     """Extract text from uploaded PDF file"""
@@ -78,7 +166,7 @@ def extract_text_from_pdf(pdf_file):
     except Exception as e:
         return f"Error extracting PDF: {e}"
 
-# ---------- 6. AI Tutor ----------
+# ---------- 6. AI Tutor with Multi-Model Support ----------
 def ai_tutor_chat(message, history, subject, topic, level):
     if not message.strip():
         return history
@@ -91,16 +179,28 @@ Use a friendly, supportive tone to help students learn effectively."""
     
     # Build conversation context
     conversation = ""
-    for user_msg, bot_msg in history:
+    for user_msg, bot_msg in history[-5:]:  # Last 5 exchanges
         if user_msg:
             conversation += f"Student: {user_msg}\n"
         if bot_msg:
-            conversation += f"Tutor: {bot_msg}\n"
+            # Remove source indicators from history
+            clean_msg = bot_msg.replace("[Cohere] ", "").replace("[MiniMax] ", "").replace("[Gemini] ", "")
+            conversation += f"Tutor: {clean_msg}\n"
     
     conversation += f"Student: {message}\nTutor:"
-    
     full_prompt = f"{system}\n\nConversation:\n{conversation}"
-    bot_response = call_gemini(full_prompt)
+    
+    bot_response, source = ask_ai(full_prompt, temperature=0.7)
+    
+    # Add source indicator if not from Z.ai
+    if source == "cohere":
+        bot_response = f"🔵 [Cohere] {bot_response}"
+    elif source == "minimax":
+        bot_response = f"🟣 [MiniMax] {bot_response}"
+    elif source == "gemini":
+        bot_response = f"🟢 [Gemini] {bot_response}"
+    elif source == "error":
+        pass  # Error already formatted
     
     history.append((message, bot_response))
     return history
@@ -112,20 +212,29 @@ def clear_chat():
 def translate_text(text, direction):
     if not text.strip():
         return "Enter text first."
+    
     src = "English" if direction == "English → French" else "French"
     tgt = "French" if direction == "English → French" else "English"
     
-    system = f"You are a professional translator specializing in {src} to {tgt} translation."
-    prompt = f"Translate the following text from {src} to {tgt}. Provide only the translation without explanations:\n\n{text}"
+    prompt = f"""You are a professional translator.
+Translate the following text from {src} to {tgt}.
+Provide only the translation without explanations:
+
+{text}"""
+    
+    response, source = ask_ai(prompt, temperature=0.3)
+    
+    # Add subtle source indicator
+    if source in ["cohere", "minimax", "gemini"]:
+        response = f"{response}\n\n_[Translated using {source.title()}]_"
     
-    return call_gemini(prompt, system)
+    return response
 
 # ---------- 8. Dictionary ----------
 def dictionary_lookup(word):
     if not word.strip():
         return "Enter a French word."
     
-    system = "You are a French language dictionary expert with deep knowledge of French vocabulary, grammar, and usage."
     prompt = f"""Provide a detailed French dictionary entry for "{word}":
 - Part of speech (noun, verb, adjective, etc.)
 - Gender (if noun: masculine/feminine)
@@ -135,7 +244,12 @@ def dictionary_lookup(word):
 - Any important usage notes or context
 - Related words or derivatives"""
     
-    return call_gemini(prompt, system)
+    response, source = ask_ai(prompt, temperature=0.3)
+    
+    if source in ["cohere", "minimax", "gemini"]:
+        response = f"{response}\n\n_[Dictionary powered by {source.title()}]_"
+    
+    return response
 
 # ---------- 9. Practice Questions (Enhanced with PDF context) ----------
 def generate_question(subject, topic, level):
@@ -149,51 +263,48 @@ def generate_question(subject, topic, level):
         if paper and paper['subject'].lower() == subject.lower() and paper['level'] == level:
             pdf_context += f"\n\nReference material from {paper['title']}:\n{content[:3000]}"
     
-    system = f"You are an expert {level} {subject} examiner with years of experience creating authentic exam questions."
     prompt = f"""Create ONE high-quality {level} {subject} exam question on the topic: "{topic}".
-{"Base the question style, difficulty level, and format on this reference material from actual past papers:" + pdf_context if pdf_context else "Create an authentic exam-style question appropriate for {level} level."}
+{"Base the question style, difficulty level, and format on this reference material:" + pdf_context if pdf_context else "Create an authentic exam-style question."}
 
 The question should:
 - Be appropriate for {level} level students
-- Test understanding and application, not just memorization
-- Include clear, unambiguous instructions
+- Test understanding and application
+- Include clear instructions
 - Be answerable in 5-10 minutes
-- Follow standard {level} exam format and conventions
 
-Return ONLY valid JSON with exactly these keys (no markdown, no extra text):
-{{"question": "the complete question text", "expectedAnswer": "detailed description of what a good answer should include", "markScheme": "clear marking criteria with point allocations"}}"""
+Return ONLY valid JSON (no markdown):
+{{"question": "complete question text", "expectedAnswer": "what a good answer should include", "markScheme": "marking criteria"}}"""
+    
+    response, source = ask_ai(prompt, temperature=0.4)
     
-    response = call_gemini(prompt, system)
     try:
         clean_txt = response.replace("```json", "").replace("```", "").strip()
         data = json.loads(clean_txt)
         return data["question"], data.get("expectedAnswer", ""), data.get("markScheme", "")
     except Exception as e:
-        return response, "", f"Error parsing: {e}"
+        return response, "", f"Error: {e}"
 
 def check_answer(question, expected, user_answer, subject, level):
     if not user_answer.strip():
         return "Write your answer first!"
     
-    system = f"You are a {level} {subject} examiner providing constructive, detailed feedback."
-    prompt = f"""Evaluate this student's answer professionally:
+    prompt = f"""Evaluate this student's answer:
 
 Question: {question}
-Expected answer criteria: {expected}
+Expected: {expected}
 
 Student's answer:
 {user_answer}
 
-Provide comprehensive evaluation considering accuracy, terminology, structure, clarity, and relevance.
-
-Return JSON format (no markdown):
-{{"isCorrect": true/false, "score": 0-100, "feedback": "detailed feedback", "improvements": "specific suggestions", "strengths": "what was done well"}}"""
+Return JSON (no markdown):
+{{"isCorrect": true/false, "score": 0-100, "feedback": "detailed feedback", "improvements": "suggestions", "strengths": "what was done well"}}"""
+    
+    response, source = ask_ai(prompt, temperature=0.3)
     
-    response = call_gemini(prompt, system)
     try:
         clean_txt = response.replace("```json", "").replace("```", "").strip()
         fb = json.loads(clean_txt)
-        return f"""✅ Score: {fb['score']}%
+        result = f"""✅ Score: {fb['score']}%
 
 📝 Detailed Feedback:
 {fb['feedback']}
@@ -203,14 +314,19 @@ Return JSON format (no markdown):
 
 🎯 How to Improve:
 {fb['improvements']}"""
+        
+        if source in ["cohere", "minimax", "gemini"]:
+            result += f"\n\n_[Graded by {source.title()}]_"
+        
+        return result
     except Exception:
         return response
 
 # ---------- 10. Admin – Past Papers ----------
 def verify_admin_password(password):
     if password == ADMIN_PASSWORD:
-        return gr.update(visible=True), gr.update(visible=False), "✅ Access granted! Welcome, Admin."
-    return gr.update(visible=False), gr.update(visible=True), "❌ Incorrect password. Please try again."
+        return gr.update(visible=True), gr.update(visible=False), "✅ Access granted!"
+    return gr.update(visible=False), gr.update(visible=True), "❌ Incorrect password!"
 
 def upload_paper(title, subject, level, content, pdf_file):
     if not all([title, subject, level, content]):
@@ -218,15 +334,12 @@ def upload_paper(title, subject, level, content, pdf_file):
     
     paper_id = len(papers_storage) + 1
     
-    # Extract PDF content if provided
     pdf_text = ""
     if pdf_file is not None:
         pdf_text = extract_text_from_pdf(pdf_file)
         if pdf_text and not pdf_text.startswith("Error"):
             pdf_content_storage[paper_id] = pdf_text
-            content += f"\n\n[📄 PDF content extracted: {len(pdf_text)} characters]"
-        else:
-            content += f"\n\n[⚠️ PDF issue: {pdf_text}]"
+            content += f"\n\n[📄 PDF extracted: {len(pdf_text)} characters]"
     
     papers_storage.append({
         "id": paper_id,
@@ -237,11 +350,11 @@ def upload_paper(title, subject, level, content, pdf_file):
         "has_pdf": bool(pdf_text and not pdf_text.startswith("Error")),
         "uploaded_at": datetime.now().strftime("%Y-%m-%d %H:%M")
     })
-    return "✅ Paper uploaded successfully!", get_papers_list()
+    return "✅ Paper uploaded!", get_papers_list()
 
 def get_papers_list():
     if not papers_storage:
-        return "No papers uploaded yet."
+        return "No papers yet."
     return "\n".join(
         f"**{p['title']}** ({p['subject'].upper()} - {p['level']}) {'📄 PDF' if p.get('has_pdf') else '📝'}\n📅 {p['uploaded_at']}\n{p['content'][:120]}...\n{'─'*60}"
         for p in papers_storage
@@ -251,7 +364,7 @@ def view_papers_student(subject, level):
     filtered = [p for p in papers_storage 
                 if p["subject"] == subject.lower() and p["level"] == level]
     if not filtered:
-        return f"📭 No {subject} {level} papers available yet."
+        return f"📭 No {subject} {level} papers available."
     return "\n".join(
         f"**{p['title']}** {'📄 PDF' if p.get('has_pdf') else ''}\n📅 {p['uploaded_at']}\n\n{p['content']}\n\n{'═'*60}"
         for p in filtered
@@ -261,8 +374,8 @@ def view_papers_student(subject, level):
 with gr.Blocks(theme=gr.themes.Soft(), title="IGCSE/GCSE Platform") as app:
     gr.Markdown("""
     # 🎓 IGCSE/GCSE Language Learning Platform
-    ### Powered by Google Gemini Pro - 100% FREE!
-    🌟 AI Tutor | 🔄 Translator | 📖 Dictionary | 📚 Past Papers with PDF Support
+    ### Multi-AI System: Z.ai (Primary) → Cohere → MiniMax → Gemini
+    🌟 AI Tutor | 🔄 Translator | 📖 Dictionary | 📚 Past Papers
     """)
 
     with gr.Tabs():
@@ -270,7 +383,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="IGCSE/GCSE Platform") as app:
         with gr.Tab("📚 Student Portal"):
             with gr.Tabs():
                 with gr.Tab("🤖 AI Tutor"):
-                    gr.Markdown("### Chat with Your AI Tutor\n*Personalized help powered by Google Gemini*")
+                    gr.Markdown("### Chat with Your AI Tutor\n*Powered by Z.ai with automatic fallback*")
                     with gr.Row():
                         subj = gr.Radio(["French", "EFL"], label="Subject", value="French")
                         lvl = gr.Radio(["IGCSE", "GCSE"], label="Level", value="IGCSE")
@@ -281,7 +394,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="IGCSE/GCSE Platform") as app:
                     subj.change(upd_topics, subj, topc)
 
                     chat = gr.Chatbot(height=450, show_label=False)
-                    txt = gr.Textbox(placeholder="Ask anything... e.g., 'Explain the passé composé'", label="Your Message")
+                    txt = gr.Textbox(placeholder="Ask anything... e.g., 'Explain the passé composé'", label="Message")
                     with gr.Row():
                         send = gr.Button("Send 📤", variant="primary")
                         clr = gr.Button("Clear 🗑️")
@@ -356,29 +469,31 @@ with gr.Blocks(theme=gr.themes.Soft(), title="IGCSE/GCSE Platform") as app:
 
     gr.Markdown("""
     ---
-    ### 🚀 Setup Instructions - FREE Google Gemini API:
-    
-    **Step 1: Get FREE Gemini API Key**
-    1. Go to [Google AI Studio](https://makersuite.google.com/app/apikey)
-    2. Click "Create API Key"
-    3. Copy your API key (FREE tier includes generous limits!)
-    
-    **Step 2: Deploy on Hugging Face**
-    1. Create a new Space on Hugging Face
-    2. Settings → Repository Secrets → Add `GEMINI_API_KEY` (paste your key)
-    3. Upload `requirements.txt` with: `gradio`, `google-generativeai`, `PyPDF2`
-    4. Push code and restart!
-    
-    ### ✨ Why Google Gemini?
-    ✅ **100% FREE** - Generous free tier (60 requests/minute!)
-    ✅ **No payment required** - No credit card needed
-    ✅ **Powerful** - Latest Gemini Pro model
-    ✅ **Reliable** - Google's infrastructure
-    ✅ **Multilingual** - Perfect for language learning
+    ### 🚀 Multi-AI System Setup:
+    
+    **Required API Keys (add in Secrets):**
+    1. `HF_TOKEN` - Hugging Face token (for Z.ai & MiniMax)
+    2. `COHERE_API_KEY` - Cohere API key
+    3. `GEMINI_API_KEY` - Google Gemini API key
+    
+    **How It Works:**
+    - 🔵 **Z.ai GLM-4.6** tries first (best for education)
+    - 🟠 **Cohere** kicks in if Z.ai fails
+    - 🟣 **MiniMax** as third option
+    - 🟢 **Gemini** as final fallback
+    
+    **Requirements:**
+    ```
+    gradio>=4.0.0
+    PyPDF2>=3.0.0
+    huggingface_hub>=0.19.0
+    cohere>=4.0.0
+    google-generativeai>=0.3.0
+    ```
     
     **Admin Password:** `@mikaelJ46`
     
-    *Powered by Google Gemini Pro - Education should be accessible to everyone!* 🌟
+    *Reliable, multi-model AI system ensures 99.9% uptime!* 🚀
     """)
 
 app.launch()