Upload app.py

app.py

@@ -0,0 +1,123 @@
+import streamlit as st
+from dotenv import load_dotenv
+import os
+import tempfile
+import base64
+from gtts import gTTS  # ✅ Use gTTS instead of pyttsx3
+from langchain_groq import ChatGroq
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# Load environment variables
+load_dotenv()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# ✅ Exact optimized prompt as provided
+optimized_prompt = PromptTemplate(
+    input_variables=["problem"],
+    template=(
+        "You are an advanced AI tutor specializing in solving **math and physics problems** step by step. "
+        "Your goal is to **guide students logically**, ensuring they **understand every step** and its relevance. "
+        "Think like a **patient teacher** who explains concepts with clarity.\n\n"
+
+        "📚 **Guidelines for solving problems:**\n"
+        "1️⃣ **Understanding the Problem:**\n"
+        "   - Restate the problem in simple terms.\n"
+        "   - Identify what is given and what needs to be found.\n\n"
+
+        "2️⃣ **Relevant Concepts & Formulas:**\n"
+        "   - List the key principles, equations, or theorems needed to solve the problem.\n"
+        "   - Explain why they are relevant.\n\n"
+
+        "3️⃣ **Step-by-Step Solution:**\n"
+        "   - Break down the solution into small, logical steps.\n"
+        "   - Show calculations with proper notation.\n"
+        "   - Explain **each transformation, substitution, or simplification** clearly.\n\n"
+
+        "4️⃣ **Final Answer:**\n"
+        "   - ✅ Box or highlight the final result.\n"
+        "   - Include units where applicable.\n\n"
+
+        "5️⃣ **Verification & Insights:**\n"
+        "   - 🔄 Verify the answer using an alternative method (if possible).\n"
+        "   - 🏗️ Provide a real-world analogy or intuition behind the result.\n\n"
+
+        "🎯 **Now, solve the following problem using this structured approach:**\n"
+        "**Problem:** {problem}\n\n"
+        "**Solution:**"
+    )
+)
+
+# Initialize Groq API model
+llm = ChatGroq(api_key=GROQ_API_KEY, model_name="gemma2-9b-it")
+llm_chain = LLMChain(llm=llm, prompt=optimized_prompt)
+
+# Function to generate speech using gTTS and return Base64-encoded audio
+def text_to_speech(text):
+    """Generate gTTS audio and return Base64 encoded string."""
+    tts = gTTS(text, lang="en")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+        tts.save(temp_audio.name)
+        with open(temp_audio.name, "rb") as audio_file:
+            audio_bytes = audio_file.read()
+            encoded_audio = base64.b64encode(audio_bytes).decode()
+        os.remove(temp_audio.name)
+    return encoded_audio
+
+# Streamlit UI Design
+st.set_page_config(page_title="STEM Solver 🤖", layout="centered", page_icon="🧠")
+
+st.markdown("<h1 style='text-align: center;'>📚 STEM Problem Solver 🤖</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center; font-size:18px;'>Enter a math or physics problem, and I'll solve it step by step! 🚀</p>", unsafe_allow_html=True)
+
+# User Input
+problem = st.text_area("📝 Enter your problem:", placeholder="e.g., What is the integral of x?", height=100)
+
+# Solve (Text) Button
+if st.button("🔍 Solve (Text)"):
+    if problem.strip():
+        with st.spinner("Thinking... 🤔"):
+            response = llm_chain.invoke({"problem": problem})
+            solution_text = response['text']
+            st.success("✅ Solution Found!")
+            
+            st.markdown("### ✨ Solution:")
+            st.markdown(f"<div style='background-color:#222831; padding:15px; border-radius:10px; color:white;'>"
+                        f"<p style='font-size:16px;'>{solution_text}</p></div>", unsafe_allow_html=True)
+    else:
+        st.warning("⚠️ Please enter a valid problem.")
+
+# Solve (Speech) Button
+if st.button("🔊 Solve (Speech)"):
+    if problem.strip():
+        with st.spinner("Speaking... 🎤"):
+            response = llm_chain.invoke({"problem": problem})
+            solution_text = response['text']
+
+            audio_base64 = text_to_speech(solution_text)  # Get Base64 audio
+            audio_html = f"""
+                <audio controls>
+                    <source src="data:audio/mp3;base64,{audio_base64}" type="audio/mp3">
+                    Your browser does not support the audio element.
+                </audio>
+            """
+            st.success("✅ Solve (Speech) Started!")
+            st.markdown(audio_html, unsafe_allow_html=True)
+
+            # 📥 Add a download button for mobile users
+            audio_file_name = "solution.mp3"
+            with open(audio_file_name, "wb") as file:
+                file.write(base64.b64decode(audio_base64))
+            
+            with open(audio_file_name, "rb") as file:
+                st.download_button(
+                    label="📥 Download Audio",
+                    data=file,
+                    file_name="solution.mp3",
+                    mime="audio/mp3"
+                )
+    else:
+        st.warning("⚠️ Please enter a valid problem.")
+
+# Footer
+st.markdown("<br><p style='text-align:center; font-size:14px;'>🚀 Created with ❤️ by an AI-powered tutor! 📖</p>", unsafe_allow_html=True)