Create app.py

app.py

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+import streamlit as st
+import PyPDF2
+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+import faiss
+from gtts import gTTS
+import os
+
+# Initialize the model and tokenizer
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to get embeddings
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors='pt')
+    with torch.no_grad():
+        embeddings = model(**inputs).last_hidden_state.mean(dim=1).numpy()
+    return embeddings
+
+# Initialize FAISS index
+embeddings_dimension = 384  # for MiniLM
+index = faiss.IndexFlatL2(embeddings_dimension)
+
+# Title of the app
+st.title("Study Assistant for Grade 9")
+
+# File uploader widget
+uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
+
+if uploaded_file is not None:
+    # Read the uploaded PDF file
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    text = ""
+
+    # Extract text from each page
+    for page in pdf_reader.pages:
+        text += page.extract_text() if page.extract_text() else ""
+
+    st.subheader("Extracted Text:")
+    st.write(text)
+
+    # Generate embedding for the extracted text
+    embeddings = get_embedding(text)
+    index.add(embeddings)  # Add embedding to the FAISS index
+
+    st.success("Text extracted and embeddings generated!")
+
+# Subject selection and query input
+subject = st.selectbox("Select Subject", ["Math", "Science", "English"])
+query = st.text_input("Type your query")
+
+if st.button("Submit"):
+    if query:
+        # Get embedding for the query
+        query_embedding = get_embedding(query)
+        
+        # Search for the nearest neighbors in the FAISS index
+        D, I = index.search(query_embedding, k=5)  # Retrieve top 5 matches
+        
+        st.subheader("Top Matches:")
+        for idx in I[0]:
+            if idx < len(embeddings):  # Ensure index is valid
+                st.write(f"Match Index: {idx}, Distance: {D[0][idx]}")  # You can display the match details
+
+        # Convert response to speech
+        response_text = f"You asked about '{query}' in {subject}. Here are your top matches."
+        tts = gTTS(text=response_text, lang='en')
+        tts.save("response.mp3")
+        os.system("start response.mp3")  # Adjust for different OS
+
+        st.success("Response generated and spoken!")
+
+# Note: To handle errors or improve this further, add appropriate try-except blocks and validations.