Update app.py

app.py

@@ -1,128 +1,4 @@
 
-# import gradio as gr
-# import fitz  # PyMuPDF
-# from langchain_text_splitters import RecursiveCharacterTextSplitter
-# from langchain_community.vectorstores import FAISS
-# from langchain_huggingface import HuggingFaceEmbeddings
-# import os
-
-# # --- Backend Logic ---
-
-# class VectorSystem:
-#     def __init__(self):
-#         self.vector_store = None
-#         self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-
-#     def process_file(self, file_obj):
-#         """Extracts text from PDF OR TXT and builds the Vector Index"""
-#         if file_obj is None:
-#             return "No file uploaded."
-
-#         try:
-#             text = ""
-#             file_path = file_obj.name
-            
-#             # --- LOGIC BRANCH: Detect File Type ---
-#             if file_path.lower().endswith('.pdf'):
-#                 # Handle PDF
-#                 doc = fitz.open(file_path)
-#                 for page in doc:
-#                     text += page.get_text()
-#             elif file_path.lower().endswith('.txt'):
-#                 # Handle Text File
-#                 with open(file_path, 'r', encoding='utf-8') as f:
-#                     text = f.read()
-#             else:
-#                 return "❌ Error: Only .pdf and .txt files are supported."
-#             # --------------------------------------
-
-#             # 2. Split Text into Chunks (Logic is identical for both)
-#             text_splitter = RecursiveCharacterTextSplitter(
-#                 chunk_size=800,
-#                 chunk_overlap=150,
-#                 separators=["\n\n", "\n", ".", " ", ""]
-#             )
-#             chunks = text_splitter.split_text(text)
-
-#             if not chunks:
-#                 return "Could not extract text. Is the file empty?"
-
-#             # 3. Build Vector Index (FAISS)
-#             self.vector_store = FAISS.from_texts(chunks, self.embeddings)
-            
-#             return f"✅ Success! Indexed {len(chunks)} text chunks."
-        
-#         except Exception as e:
-#             return f"Error processing file: {str(e)}"
-
-#     def retrieve_evidence(self, question, student_answer):
-#         if not self.vector_store:
-#             return "⚠️ Please upload and process a file first."
-
-#         if not question:
-#             return "⚠️ Please enter a Question."
-
-#         docs = self.vector_store.similarity_search(question, k=3)
-        
-#         output_text = "### 🔍 Relevant Context Found:\n\n"
-#         for i, doc in enumerate(docs):
-#             output_text += f"**Chunk {i+1}:**\n> {doc.page_content}\n\n"
-            
-#         output_text += "---\n*These are the most relevant segments to grade the answer against.*"
-#         return output_text
-
-# # Initialize System
-# system = VectorSystem()
-
-# # --- Gradio UI ---
-
-# with gr.Blocks(title="EduGenius Context Retriever") as demo:
-#     gr.Markdown("# 🎓 EduGenius: Context Retriever")
-#     gr.Markdown("Upload a Chapter (PDF or TXT), ask a question, and see exactly which part of the text proves the answer right or wrong.")
-
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             # UPDATED: Added ".txt" to file_types and changed label
-#             pdf_input = gr.File(label="1. Upload File (PDF or TXT)", file_types=[".pdf", ".txt"])
-#             upload_btn = gr.Button("Process File", variant="primary")
-#             upload_status = gr.Textbox(label="Status", interactive=False)
-
-#         with gr.Column(scale=2):
-#             question_input = gr.Textbox(label="2. Question", placeholder="e.g., What causes the chemical reaction?")
-#             answer_input = gr.Textbox(label="Student Answer (Optional Context)", placeholder="e.g., The heat causes it...")
-#             search_btn = gr.Button("Find Relevant Evidence", variant="secondary")
-            
-#             evidence_output = gr.Markdown(label="Relevant Text Chunks")
-
-#     # Event Handlers
-#     upload_btn.click(
-#         fn=system.process_file,  # Note: Function name changed
-#         inputs=[pdf_input], 
-#         outputs=[upload_status]
-#     )
-
-#     search_btn.click(
-#         fn=system.retrieve_evidence,
-#         inputs=[question_input, answer_input],
-#         outputs=[evidence_output]
-#     )
-
-# if __name__ == "__main__":
-#     demo.launch()
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 import gradio as gr
 import fitz  # PyMuPDF