Update app.py

app.py

@@ -1,178 +1,173 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 import requests
+from huggingface_hub import InferenceClient
 
-# Main + Fallback Models
+# ---------- Chat & Career Models ----------
 MODELS = [
     "meta-llama/Meta-Llama-3-8B-Instruct",
     "mistralai/Mistral-7B-Instruct-v0.3",
-    "google/gemma-7b-it",
-    "HuggingFaceH4/zephyr-7b-beta"  # reliable fallback
+    "google/gemma-7b-it"
 ]
 
-# ---------- Chat ----------
-def chat_with_ai(message, history):
+def ai_chat(message, history):
     if history is None:
         history = []
     for model in MODELS:
         try:
             client = InferenceClient(model)
-            conversation = "".join(
-                [f"User: {h[0]}\nStudyMate: {h[1]}\n" for h in history]
-            )
+            conversation = ""
+            for h in history:
+                conversation += f"User: {h[0]}\nStudyMate: {h[1]}\n"
             prompt = (
-                "You are StudyMate AI — a friendly, multilingual personal tutor and assistant. "
-                "You answer *every* question completely in the user’s language (English or Urdu). "
-                "If unsure, provide a helpful general explanation or example.\n\n"
-                f"{conversation}User: {message}\nStudyMate:"
+                "You are StudyMate AI, a multilingual tutor and career mentor. "
+                "Always reply clearly in the same language as the user. "
+                "If you are unsure, give your best explanation and encourage learning.\n\n"
+                + conversation
+                + f"User: {message}\nStudyMate:"
             )
+
             reply = ""
             for chunk in client.text_generation(
                 prompt, max_new_tokens=512, stream=True, temperature=0.7
             ):
                 reply += chunk.token
-            if reply.strip():
-                history.append([message, reply])
-                return history, ""
+            history.append((message, reply))
+            return history, ""
         except Exception:
             continue
-    # Ultimate fallback response
-    history.append([message, "I'm having a little trouble fetching a detailed response, but here's a quick idea:\n"
-                             f"{generate_smart_fallback(message)}"])
+    history.append((message, "Sorry 😔 I couldn’t generate a full answer right now. Try again."))
     return history, ""
 
-def generate_smart_fallback(question):
-    # Simple built-in emergency logic
-    if "job" in question.lower():
-        return "Try searching for this in the Job Finder tab — it's updated daily with remote and local roles."
-    if "career" in question.lower():
-        return "Focus on upskilling and building a small portfolio — it opens many doors faster than degrees alone."
-    if "love" in question.lower():
-        return "Love and respect go hand in hand — nurture both and life feels lighter."
-    return "I’ll keep learning from your feedback — please rephrase or ask again."
-
-# ---------- Job Finder ----------
-def find_jobs(skill):
+# ---------- Career Compass ----------
+def career_compass(skill):
     if not skill:
-        return "Please enter a skill or job title."
-    url = f"https://remotive.com/api/remote-jobs?search={skill}"
+        return "Please enter a skill or field."
+    return (
+        f"📘 **Career Plan for {skill.title()}**\n\n"
+        f"**1️⃣ Learn:** Take beginner-to-advanced online courses (Coursera, YouTube, freecodecamp).\n"
+        f"**2️⃣ Build:** Create 2-3 small portfolio projects related to {skill}.\n"
+        f"**3️⃣ Connect:** Join LinkedIn groups or Pakistani Facebook communities for {skill}.\n"
+        f"**4️⃣ Earn:** Search for remote jobs or gigs via StudyMate AI’s Job Finder tab.\n"
+        f"**5️⃣ Scale:** Once confident, teach others — it multiplies learning.\n\n"
+        f"✨ Tip: Consistency for 90 days = visible progress."
+    )
+
+# ---------- Voice ----------
+def tts_generate(text):
+    if not text:
+        return None
     try:
-        response = requests.get(url).json()
-        jobs = response.get("jobs", [])
-        if not jobs:
-            return "No jobs found. Try a different keyword."
-        result = ""
-        for job in jobs[:5]:
-            result += f"🔹 **{job['title']}**\n🏢 {job['company_name']}\n🌍 {job['candidate_required_location']}\n🔗 [Apply Here]({job['url']})\n\n"
-        return result
-    except Exception as e:
-        return f"Error fetching jobs: {e}"
-
-job_posts = []
-
-def post_job(name, title, description, contact):
-    if not name or not title or not description:
-        return "Please fill in all fields."
-    job_posts.append({
-        "name": name,
-        "title": title,
-        "description": description,
-        "contact": contact
-    })
-    return "✅ Job post added successfully!"
-
-def show_job_posts():
-    if not job_posts:
-        return "No community job posts yet."
-    result = ""
-    for j in job_posts[-10:][::-1]:
-        result += f"**{j['title']}** by {j['name']}\n📄 {j['description']}\n📞 {j['contact']}\n\n"
-    return result
-
-# ---------- Career Compass ----------
-def career_guidance(skills, goal, education):
-    if not skills and not goal:
-        return "Please provide your skills or career goal."
-    for model in MODELS:
-        try:
-            client = InferenceClient(model)
-            prompt = (
-                "You are StudyMate AI — a professional career coach. "
-                "Give career path suggestions, high-demand roles, and study resources. "
-                "Write in clear, bullet-point form. If Urdu is detected, reply in Urdu.\n\n"
-                f"Skills: {skills}\nGoal: {goal}\nEducation: {education}\n\nCareer Advice:"
-            )
-            reply = ""
-            for chunk in client.text_generation(
-                prompt, max_new_tokens=600, stream=True, temperature=0.7
-            ):
-                reply += chunk.token
-            if reply.strip():
-                return reply
-        except Exception:
-            continue
-    return "Sorry, I couldn’t load a detailed career plan, but here’s a quick tip:\n" \
-           "Pick one skill you enjoy most and start offering it online — consistent practice beats overthinking."
-
-# ---------- UI Theme ----------
-css_code = """
+        client = InferenceClient("coqui-ai/TTS-1-en")
+        audio = client.audio_generation(text)
+        return (audio, )
+    except Exception:
+        return None
+
+# ---------- CSS (iOS Glass Theme + Animations) ----------
+css_style = """
 body {
-  background: linear-gradient(120deg, rgba(15,15,25,0.9), rgba(25,25,35,0.9)), 
-              url('https://images.unsplash.com/photo-1523050854058-8df90110c9f1?auto=format&fit=crop&w=1920&q=80') 
-              no-repeat center center fixed;
-  background-size: cover;
+  background: radial-gradient(circle at top left, #0a0f1c, #111827);
+  overflow: hidden;
+  font-family: 'Poppins', sans-serif;
   color: white;
 }
+
 .gradio-container {
-  backdrop-filter: blur(18px);
   background: rgba(255, 255, 255, 0.05);
-  border-radius: 18px;
-  padding: 20px;
+  backdrop-filter: blur(20px);
+  border-radius: 24px;
+  padding: 24px;
+  box-shadow: 0 0 20px rgba(255,255,255,0.05);
+  animation: fadeIn 1.5s ease-in;
+}
+
+button:hover {
+  transform: scale(1.05);
+  transition: 0.3s;
+}
+
+@keyframes fadeIn {
+  from { opacity: 0; transform: translateY(20px); }
+  to { opacity: 1; transform: translateY(0); }
+}
+
+/* Floating background icons */
+.floating {
+  position: absolute;
+  width: 60px;
+  height: 60px;
+  opacity: 0.15;
+  animation: float 12s ease-in-out infinite;
+}
+
+@keyframes float {
+  0% { transform: translateY(0) rotate(0deg); }
+  50% { transform: translateY(-30px) rotate(20deg); }
+  100% { transform: translateY(0) rotate(0deg); }
+}
+
+#icon1 { top: 10%; left: 20%; animation-delay: 0s; }
+#icon2 { top: 60%; left: 70%; animation-delay: 2s; }
+#icon3 { top: 40%; left: 10%; animation-delay: 4s; }
+
+.loader {
+  display: inline-block;
+  width: 20px;
+  height: 20px;
+  border: 3px solid rgba(255,255,255,0.3);
+  border-radius: 50%;
+  border-top-color: #ffffff;
+  animation: spin 1s ease-in-out infinite;
+}
+
+@keyframes spin {
+  to { transform: rotate(360deg); }
 }
 """
 
-# ---------- BUILD APP ----------
-with gr.Blocks(css=css_code, theme=gr.themes.Soft(primary_hue="blue")) as demo:
+# ---------- UI ----------
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=css_style) as demo:
     gr.HTML("""
-        <div style="text-align:center; font-size:30px; font-weight:700; color:white;">
-            StudyMate AI
-        </div>
+    <div style='text-align:center; position:relative;'>
+        <h1 style='font-size:2.5em; color:white;'>StudyMate AI</h1>
+        <p style='opacity:0.7;'>Your smart study, career, and voice companion</p>
+        <img id='icon1' class='floating' src='https://cdn-icons-png.flaticon.com/512/1048/1048945.png'/>
+        <img id='icon2' class='floating' src='https://cdn-icons-png.flaticon.com/512/616/616408.png'/>
+        <img id='icon3' class='floating' src='https://cdn-icons-png.flaticon.com/512/1828/1828778.png'/>
+    </div>
     """)
 
     with gr.Tab("💬 Chat"):
-        chatbot = gr.Chatbot(label="Chat", height=480, bubble_full_width=False)
-        msg = gr.Textbox(placeholder="Ask anything in Urdu or English…")
+        chatbot = gr.Chatbot(label="Ask anything", height=480)
+        msg = gr.Textbox(placeholder="Type or speak your question…")
+        voice_in = gr.Microphone(label="🎤 Speak", type="filepath")
+        voice_out = gr.Audio(label="🔊 Reply Voice", type="filepath")
+
         clear = gr.Button("Clear Chat")
-        msg.submit(chat_with_ai, [msg, chatbot], [chatbot, msg])
+
+        msg.submit(ai_chat, [msg, chatbot], [chatbot, msg])
         clear.click(lambda: None, None, chatbot, queue=False)
 
-    with gr.Tab("💼 Job Finder"):
-        gr.Markdown("### 🔍 Find Remote Jobs")
-        skill = gr.Textbox(label="Enter a skill (e.g. Python, Graphic Design)")
-        search_btn = gr.Button("Find Jobs")
-        job_output = gr.Markdown()
-        search_btn.click(find_jobs, skill, job_output)
-
-        gr.Markdown("---\n### 📢 Post a Local Job (Community Board)")
-        name = gr.Textbox(label="Your Name")
-        title = gr.Textbox(label="Job Title")
-        desc = gr.Textbox(label="Description")
-        contact = gr.Textbox(label="Contact Info")
-        post_btn = gr.Button("Post Job")
-        post_output = gr.Markdown()
-        post_btn.click(post_job, [name, title, desc, contact], post_output)
-
-        gr.Markdown("### 🗂️ Recent Community Job Posts")
-        refresh = gr.Button("Refresh Job List")
-        posts_display = gr.Markdown()
-        refresh.click(show_job_posts, None, posts_display)
+        def voice_to_text(audio, chat_history):
+            if audio is None:
+                return chat_history, ""
+            try:
+                client = InferenceClient("openai/whisper-tiny.en")
+                result = client.automatic_speech_recognition(audio)
+                text = result.get("text", "")
+                chat_history, _ = ai_chat(text, chat_history)
+                reply_audio = tts_generate(chat_history[-1][1])
+                return chat_history, reply_audio
+            except Exception:
+                chat_history.append(("", "Sorry, I couldn’t process voice input."))
+                return chat_history, None
+
+        voice_in.change(voice_to_text, [voice_in, chatbot], [chatbot, voice_out])
 
     with gr.Tab("🧭 Career Compass"):
-        skills = gr.Textbox(label="Your Skills (e.g. Video Editing, Marketing)")
-        goal = gr.Textbox(label="Career Goal (e.g. Doctor, Developer, Designer)")
-        education = gr.Textbox(label="Education Level (e.g. Intermediate, Graduate)")
-        generate = gr.Button("Get Career Plan")
-        output = gr.Markdown()
-        generate.click(career_guidance, [skills, goal, education], output)
+        skill = gr.Textbox(label="Enter your skill or goal")
+        plan_btn = gr.Button("Generate Career Plan")
+        plan_output = gr.Markdown()
+        plan_btn.click(career_compass, skill, plan_output)
 
 demo.launch()