Create app.py

app.py

@@ -0,0 +1,137 @@
+# learning_with_fun_app.py
+
+import streamlit as st
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.docstore.document import Document
+from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredImageLoader
+from gtts import gTTS
+import os
+import tempfile
+import base64
+import requests
+
+# -------------------------------
+# 1. Load documents
+# -------------------------------
+def load_documents(uploaded_files):
+    docs = []
+    for uploaded_file in uploaded_files:
+        file_path = os.path.join(tempfile.gettempdir(), uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        if uploaded_file.name.endswith(".pdf"):
+            loader = PyPDFLoader(file_path)
+        elif uploaded_file.name.endswith(".docx"):
+            loader = Docx2txtLoader(file_path)
+        elif uploaded_file.name.endswith(('.jpg', '.jpeg', '.png')):
+            loader = UnstructuredImageLoader(file_path)
+        else:
+            st.warning("Unsupported file format")
+            continue
+
+        docs.extend(loader.load())
+    return docs
+
+# -------------------------------
+# 2. Chunking documents
+# -------------------------------
+def chunk_documents(docs):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    return splitter.split_documents(docs)
+
+# -------------------------------
+# 3. Create embeddings
+# -------------------------------
+def create_embeddings():
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# -------------------------------
+# 4. Create and save FAISS index
+# -------------------------------
+def create_faiss_index(chunks, embedding):
+    vectorstore = FAISS.from_documents(chunks, embedding)
+    return vectorstore
+
+# -------------------------------
+# 5. Query vectorstore and generate answers
+# -------------------------------
+def query_faiss(vectorstore, question, embedding, grade, subject):
+    relevant_docs = vectorstore.similarity_search(question, k=3)
+    context = "\n".join([doc.page_content for doc in relevant_docs])
+    return generate_answers(context, question)
+
+# -------------------------------
+# 6. Generate answer using GROQ + LLaMA 3
+# -------------------------------
+def generate_answers(context, question):
+    prompt = f"""
+    You are a helpful teacher. Use the context below to answer the question in two formats:
+
+    1. Beginner explanation (Urdu + English mix)
+    2. Storytelling style (Urdu-English mix with a fun tone)
+
+    Context:
+    {context}
+
+    Question:
+    {question}
+
+    Answer:
+    """
+    headers = {"Authorization": f"Bearer YOUR_GROQ_API_KEY"}
+    payload = {
+        "model": "llama3-8b-8192",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.7
+    }
+    response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=payload)
+    result = response.json()
+    return result["choices"][0]["message"]["content"]
+
+# -------------------------------
+# 7. Convert storytelling to voice
+# -------------------------------
+def text_to_speech(text):
+    tts = gTTS(text=text, lang='ur')
+    file_path = os.path.join(tempfile.gettempdir(), "story.mp3")
+    tts.save(file_path)
+    return file_path
+
+# -------------------------------
+# Streamlit UI
+# -------------------------------
+def main():
+    st.set_page_config(page_title="Learning with Fun")
+    st.title("📚 Learning with Fun - Ask Questions & Hear Stories!")
+
+    grade = st.selectbox("Select Grade:", ["Grade 5", "Grade 6"])
+    subject = st.selectbox("Select Subject:", ["Science", "Math", "English"])
+
+    uploaded_files = st.file_uploader("Upload Book Files (PDF, DOCX, Images)", accept_multiple_files=True)
+
+    question = st.text_input("Ask a Question (English or Urdu):")
+
+    if st.button("Generate Answer") and uploaded_files and question:
+        with st.spinner("Processing documents and generating answer..."):
+            docs = load_documents(uploaded_files)
+            chunks = chunk_documents(docs)
+            embedding = create_embeddings()
+            vectorstore = create_faiss_index(chunks, embedding)
+            answer = query_faiss(vectorstore, question, embedding, grade, subject)
+
+            st.markdown("### 📘 Answer:")
+            parts = answer.split("2. Storytelling style")
+            st.markdown(f"**1. Explanation:**\n\n{parts[0]}")
+            if len(parts) > 1:
+                story_text = parts[1].strip()
+                st.markdown(f"**2. Storytelling Style:**\n\n{story_text}")
+
+                audio_file = text_to_speech(story_text)
+                audio_bytes = open(audio_file, 'rb').read()
+                st.audio(audio_bytes, format='audio/mp3')
+
+if __name__ == "__main__":
+    main()