Create app.py

app.py

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+import os
+from utils.pdf_processing import extract_text_from_pdf, extract_text_from_scanned_pdf
+from utils.vector_store import add_document, search
+from utils.stock_data import get_stock_data
+from utils.sentiment import analyze_sentiment
+
+# Define PDF folder
+PDF_FOLDER = "data"
+
+# Process and index all PDFs
+def process_pdfs():
+    for pdf_file in os.listdir(PDF_FOLDER):
+        pdf_path = os.path.join(PDF_FOLDER, pdf_file)
+        
+        # Extract text from normal or scanned PDFs
+        text = extract_text_from_pdf(pdf_path) or extract_text_from_scanned_pdf(pdf_path)
+        
+        # Store in FAISS vector store
+        add_document(text, pdf_file)
+
+# Query the system
+def query_system(query):
+    results = search(query)
+
+    print("📌 Top Matching Documents:")
+    for res in results:
+        print(f"\n📄 Document: {res.metadata['id']}\n{text[:500]}...")
+
+    # Perform sentiment analysis
+    print("\n📊 Sentiment Analysis:")
+    for res in results:
+        sentiment = analyze_sentiment(res.page_content)
+        print(f"📄 Document: {res.metadata['id']}, Sentiment: {sentiment}")
+
+# Fetch stock data
+def get_stock_info(ticker):
+    stock_data = get_stock_data(ticker)
+    print(f"\n📈 Stock Price for {ticker}: ${stock_data['price']}")
+
+# Run the application
+if __name__ == "__main__":
+    print("🔄 Indexing PDFs...")
+    process_pdfs()
+
+    query = input("\n🔍 Enter your query: ")
+    query_system(query)
+
+    ticker = input("\n📊 Enter a stock ticker (e.g., AAPL, TSLA): ")
+    get_stock_info(ticker)