Create app.py

app.py

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+import streamlit as st
+import PyPDF2
+import openai
+import faiss
+import os
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to generate embeddings for a piece of text
+def get_embeddings(text, model="text-embedding-ada-002"):
+    response = openai.Embedding.create(input=[text], model=model)
+    return response['data'][0]['embedding']
+
+# Function to search for similar content
+def search_similar(query_embedding, index, stored_texts, top_k=3):
+    distances, indices = index.search([query_embedding], top_k)
+    results = [(stored_texts[i], distances[0][idx]) for idx, i in enumerate(indices[0])]
+    return results
+
+# Streamlit app starts here
+st.title("Course Query Assistant")
+
+# Input OpenAI API key
+openai_api_key = st.text_input("Enter your OpenAI API key:", type="password")
+
+if openai_api_key:
+    openai.api_key = openai_api_key
+
+    # Upload course materials
+    uploaded_files = st.file_uploader("Upload Course Materials (PDFs)", type=["pdf"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("Processing uploaded course materials...")
+
+        # Extract text and generate embeddings for all uploaded PDFs
+        course_texts = []
+        for uploaded_file in uploaded_files:
+            text = extract_text_from_pdf(uploaded_file)
+            course_texts.append(text)
+
+        # Combine all course materials into one large text
+        combined_text = " ".join(course_texts)
+
+        # Split combined text into smaller chunks for embedding (max tokens ~1000)
+        chunks = [combined_text[i:i+1000] for i in range(0, len(combined_text), 1000)]
+
+        # Generate embeddings for all chunks
+        embeddings = [get_embeddings(chunk) for chunk in chunks]
+
+        # Create a FAISS index for similarity search
+        index = faiss.IndexFlatL2(len(embeddings[0]))
+        index.add(embeddings)
+
+        st.write("Course materials have been processed and indexed.")
+
+        # User query
+        query = st.text_input("Enter your question about the course materials:")
+
+        if query:
+            # Generate embedding for the query
+            query_embedding = get_embeddings(query)
+
+            # Search for similar chunks in the FAISS index
+            results = search_similar(query_embedding, index, chunks)
+
+            # Create the context for the GPT prompt
+            context = "\n".join([result[0] for result in results])
+            modified_prompt = f"Context: {context}\n\nQuestion: {query}\n\nProvide a detailed answer based on the context."
+
+            # Get the GPT-3.5-turbo response
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[{"role": "user", "content": modified_prompt}]
+            )
+
+            # Display the response
+            st.write("### Intelligent Reply:")
+            st.write(response['choices'][0]['message']['content'])