Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,9 +2,12 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 from langdetect import detect
+from PIL import Image
+import numpy as np
+import pyttsx3
 
 # ----------------------------
-# Load Model (Hugging Face)
+# Load Model
 # ----------------------------
 MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 
@@ -20,6 +23,15 @@ def load_model():
 
 tokenizer, model = load_model()
 
+# ----------------------------
+# TTS (Offline)
+# ----------------------------
+engine = pyttsx3.init()
+
+def speak(text):
+    engine.say(text)
+    engine.runAndWait()
+
 # ----------------------------
 # Language Detection
 # ----------------------------
@@ -30,20 +42,20 @@ def detect_lang(text):
         return "en"
 
 # ----------------------------
-# Tutor Prompt
+# Multilingual Prompt
 # ----------------------------
 def build_prompt(user_input, lang):
     if lang == "fr":
-        system = "Tu es un tuteur de mathématiques pour enfants. Explique simplement."
-    elif lang == "sw":  # fallback for region
+        system = "Tu es un tuteur de mathématiques pour enfants. Réponds simplement avec des exemples."
+    elif lang == "sw":
         system = "Wewe ni mwalimu wa hesabu kwa watoto. Eleza kwa urahisi."
     else:
-        system = "You are a friendly math tutor for kids. Explain simply."
+        system = "You are a friendly math tutor for kids. Explain step by step."
 
     return f"{system}\nUser: {user_input}\nAssistant:"
 
 # ----------------------------
-# Generate Response
+# Generate LLM Response
 # ----------------------------
 def generate(prompt):
     inputs = tokenizer(prompt, return_tensors="pt")
@@ -56,19 +68,103 @@ def generate(prompt):
     return tokenizer.decode(output[0], skip_special_tokens=True)
 
 # ----------------------------
-# Streamlit UI
+# Visual Counting (Lightweight)
+# ----------------------------
+def count_objects(image):
+    img = np.array(image.convert("L"))
+    binary = img > 128
+    count = int(binary.sum() / 500)
+    return max(1, count)
+
+# ----------------------------
+# UI DASHBOARD
+# ----------------------------
+st.set_page_config(layout="wide")
+st.title("🧠📊 AI Math Tutor Dashboard")
+
+col1, col2 = st.columns(2)
+
+# ----------------------------
+# LEFT PANEL — INTERACTION
+# ----------------------------
+with col1:
+    st.header("👧 Student Interaction")
+
+    mode = st.radio("Choose Mode", ["Text", "Image (Count)", "Voice (Simulated)"])
+
+    if mode == "Text":
+        user_input = st.text_input("Ask a math question:")
+
+        if user_input:
+            lang = detect_lang(user_input)
+            prompt = build_prompt(user_input, lang)
+
+            response = generate(prompt)
+
+            st.write("### 📘 Answer")
+            st.write(response)
+
+            if st.button("🔊 Speak Answer"):
+                speak(response)
+
+    # ----------------------------
+    # IMAGE MODE (Visual Learning)
+    # ----------------------------
+    elif mode == "Image (Count)":
+        uploaded = st.file_uploader("Upload image with objects", type=["png", "jpg"])
+
+        if uploaded:
+            image = Image.open(uploaded)
+            st.image(image, caption="Uploaded Image")
+
+            count = count_objects(image)
+
+            st.write(f"### 🧮 I see about: {count} objects")
+
+            explanation = f"There are about {count} objects. Let's count together!"
+            st.write(explanation)
+
+            if st.button("🔊 Speak"):
+                speak(explanation)
+
+    # ----------------------------
+    # VOICE MODE (SIMULATED)
+    # ----------------------------
+    elif mode == "Voice (Simulated)":
+        st.write("🎤 Voice input simulation (type what child says)")
+
+        voice_input = st.text_input("Child says:")
+
+        if voice_input:
+            lang = detect_lang(voice_input)
+
+            prompt = build_prompt(voice_input, lang)
+            response = generate(prompt)
+
+            st.write("### 🎧 Tutor Response")
+            st.write(response)
+
+            if st.button("🔊 Speak Response"):
+                speak(response)
+
+# ----------------------------
+# RIGHT PANEL — PROGRESS
 # ----------------------------
-st.title("🧠 AI Math Tutor (Hackathon Demo)")
-st.write("Multilingual • Simple • CPU Friendly")
+with col2:
+    st.header("📈 Learning Progress")
 
-user_input = st.text_input("Ask a math question:")
+    # Fake metrics (replace with SQLite later)
+    st.metric("Questions Answered", 12)
+    st.metric("Accuracy", "75%")
+    st.metric("Level", "Beginner → Improving")
 
-if user_input:
-    lang = detect_lang(user_input)
-    prompt = build_prompt(user_input, lang)
+    st.subheader("📊 Skill Breakdown")
+    st.progress(0.7)
 
-    with st.spinner("Thinking..."):
-        response = generate(prompt)
+    st.subheader("🌍 Language Detected")
+    st.write("Auto-detected per input")
 
-    st.write("### 📘 Answer")
-    st.write(response)
+    st.subheader("⚡ System Info")
+    st.write("CPU Mode ✔")
+    st.write("Offline-ready ✔")
+    st.write("Multilingual ✔")