Update app.py

app.py

@@ -124,134 +124,8 @@
 
 
 
-# import gradio as gr
-# import fitz  # PyMuPDF
-# from langchain_text_splitters import RecursiveCharacterTextSplitter
-# from langchain_community.vectorstores import FAISS
-# from langchain_huggingface import HuggingFaceEmbeddings
-
-# class VectorSystem:
-#     def __init__(self):
-#         self.vector_store = None
-#         self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-#         # NEW: We keep a copy of all chunks in a list so we can access neighbors by index
-#         self.all_chunks = []
-
-#     def process_file(self, file_obj):
-#         """Extracts text, preserves order, and builds the Vector Index"""
-#         if file_obj is None:
-#             return "No file uploaded."
-
-#         try:
-#             # 1. Extract Text
-#             text = ""
-#             file_path = file_obj.name
-            
-#             if file_path.lower().endswith('.pdf'):
-#                 doc = fitz.open(file_path)
-#                 for page in doc: text += page.get_text()
-#             elif file_path.lower().endswith('.txt'):
-#                 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
-#             text_splitter = RecursiveCharacterTextSplitter(
-#                 chunk_size=800,
-#                 chunk_overlap=150,
-#                 separators=["\n\n", "\n", ".", " ", ""]
-#             )
-#             # Store chunks in the class so we can look them up by ID later
-#             self.all_chunks = text_splitter.split_text(text)
-
-#             if not self.all_chunks:
-#                 return "Could not extract text. Is the file empty?"
-
-#             # 3. Build Vector Index with ID Metadata
-#             # We attach the index ID (0, 1, 2...) to every vector
-#             metadatas = [{"id": i} for i in range(len(self.all_chunks))]
-            
-#             self.vector_store = FAISS.from_texts(
-#                 self.all_chunks, 
-#                 self.embeddings, 
-#                 metadatas=metadatas
-#             )
-            
-#             return f"✅ Success! Indexed {len(self.all_chunks)} 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."
-
-#         # Lower Score = Better Match
-#         results = self.vector_store.similarity_search_with_score(question, k=3)
-        
-#         output_text = "### 🔍 Expanded Context Analysis:\n"
-        
-#         for i, (doc, score) in enumerate(results):
-#             chunk_id = doc.metadata['id']
-            
-#             # Retrieve Previous and Next chunks
-#             # Logic: If it's the first chunk (ID 0), there is no 'prev', so returns empty string
-#             prev_chunk = self.all_chunks[chunk_id - 1] if chunk_id > 0 else "(Start of Text)"
-#             next_chunk = self.all_chunks[chunk_id + 1] if chunk_id < len(self.all_chunks) - 1 else "(End of Text)"
-            
-#             output_text += f"\n#### 🎯 Match #{i+1} (Distance Score: {score:.4f})\n"
-            
-#             # --- CHANGED HERE: Removed [-200:] and [:200] ---
-            
-#             output_text += f"> **Preceding Context:**\n{prev_chunk}\n\n" 
-#             output_text += f"> **MATCH:**\n**{doc.page_content}**\n\n"
-#             output_text += f"> **Succeeding Context:**\n{next_chunk}\n"
-            
-#             output_text += "---\n"
-
-#         return output_text
-
-# # Initialize System
-# system = VectorSystem()
-
-# # --- Gradio UI ---
-# with gr.Blocks(title="EduGenius Context Retriever") as demo:
-#     gr.Markdown("# 🎓 EduGenius: Smart Context Retriever")
-#     gr.Markdown("Upload a Chapter. This version finds the best match AND shows you the text immediately before and after it.")
-
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             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)", placeholder="e.g., The heat causes it...")
-#             search_btn = gr.Button("Find Context + Neighbors", variant="secondary")
-            
-#             evidence_output = gr.Markdown(label="Relevant Text Chunks")
-
-#     upload_btn.click(fn=system.process_file, 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()
-
-
-
-
-
-
-
-
-
 import gradio as gr
 import fitz  # PyMuPDF
-import numpy as np
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -260,6 +134,7 @@ class VectorSystem:
     def __init__(self):
         self.vector_store = None
         self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        # NEW: We keep a copy of all chunks in a list so we can access neighbors by index
         self.all_chunks = []
 
     def process_file(self, file_obj):
@@ -286,12 +161,14 @@ class VectorSystem:
                 chunk_overlap=150,
                 separators=["\n\n", "\n", ".", " ", ""]
             )
+            # Store chunks in the class so we can look them up by ID later
             self.all_chunks = text_splitter.split_text(text)
 
             if not self.all_chunks:
                 return "Could not extract text. Is the file empty?"
 
             # 3. Build Vector Index with ID Metadata
+            # We attach the index ID (0, 1, 2...) to every vector
             metadatas = [{"id": i} for i in range(len(self.all_chunks))]
             
             self.vector_store = FAISS.from_texts(
@@ -308,46 +185,30 @@ class VectorSystem:
     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."
 
-        # 1. Get Initial Results (Core Matches)
-        # FAISS returns L2 distance (Lower is better)
+        # Lower Score = Better Match
         results = self.vector_store.similarity_search_with_score(question, k=3)
         
-        # We need the vector for the QUESTION to do our own math later
-        q_vector = np.array(self.embeddings.embed_query(question))
+        output_text = "### 🔍 Expanded Context Analysis:\n"
         
-        output_text = "### 🔍 Smart Context Analysis:\n"
-        
-        for i, (doc, core_score) in enumerate(results):
+        for i, (doc, score) in enumerate(results):
             chunk_id = doc.metadata['id']
             
-            # 2. Identify Neighbors
-            prev_chunk = self.all_chunks[chunk_id - 1] if chunk_id > 0 else ""
-            next_chunk = self.all_chunks[chunk_id + 1] if chunk_id < len(self.all_chunks) - 1 else ""
-            
-            # 3. Create the "Super Chunk" (Prev + Core + Next)
-            super_chunk_text = f"{prev_chunk} {doc.page_content} {next_chunk}"
+            # Retrieve Previous and Next chunks
+            # Logic: If it's the first chunk (ID 0), there is no 'prev', so returns empty string
+            prev_chunk = self.all_chunks[chunk_id - 1] if chunk_id > 0 else "(Start of Text)"
+            next_chunk = self.all_chunks[chunk_id + 1] if chunk_id < len(self.all_chunks) - 1 else "(End of Text)"
             
-            # 4. Calculate "Super Score" (Re-embedding on the fly)
-            # We embed the Super Chunk and measure distance to Question
-            super_vector = np.array(self.embeddings.embed_query(super_chunk_text))
-            super_score = np.linalg.norm(q_vector - super_vector) # Euclidean Distance
+            output_text += f"\n#### 🎯 Match #{i+1} (Distance Score: {score:.4f})\n"
             
-            output_text += f"\n#### 🎯 Match #{i+1}\n"
+            # --- CHANGED HERE: Removed [-200:] and [:200] ---
             
-            # 5. The Logic Test: Does Context Improve the Score?
-            # Remember: LOWER score is BETTER (closer distance)
-            
-            if super_score < core_score:
-                # CASE A: Context Helps! (Distance Reduced)
-                output_text += f"**✅ Context Added:** The surrounding text made the match stronger (Score improved from {core_score:.3f} to {super_score:.3f}).\n\n"
-                output_text += f"> {prev_chunk} **{doc.page_content}** {next_chunk}\n"
-            else:
-                # CASE B: Context Dilutes! (Distance Increased or Same)
-                output_text += f"**⏹️ Context Ignored:** Surrounding text was irrelevant or noisy (Score worsened from {core_score:.3f} to {super_score:.3f}). Showing Core Match only.\n\n"
-                output_text += f"> **{doc.page_content}**\n"
+            output_text += f"> **Preceding Context:**\n{prev_chunk}\n\n" 
+            output_text += f"> **MATCH:**\n**{doc.page_content}**\n\n"
+            output_text += f"> **Succeeding Context:**\n{next_chunk}\n"
             
             output_text += "---\n"
 
@@ -358,8 +219,8 @@ system = VectorSystem()
 
 # --- Gradio UI ---
 with gr.Blocks(title="EduGenius Context Retriever") as demo:
-    gr.Markdown("# 🎓 EduGenius: Intelligent Context Retriever")
-    gr.Markdown("Upload a Chapter. This system intelligently decides if it needs to read the surrounding paragraphs to answer your question.")
+    gr.Markdown("# 🎓 EduGenius: Smart Context Retriever")
+    gr.Markdown("Upload a Chapter. This version finds the best match AND shows you the text immediately before and after it.")
 
     with gr.Row():
         with gr.Column(scale=1):
@@ -370,7 +231,7 @@ with gr.Blocks(title="EduGenius Context Retriever") as demo:
         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)", placeholder="e.g., The heat causes it...")
-            search_btn = gr.Button("Find Evidence", variant="secondary")
+            search_btn = gr.Button("Find Context + Neighbors", variant="secondary")
             
             evidence_output = gr.Markdown(label="Relevant Text Chunks")
 
@@ -378,4 +239,143 @@ with gr.Blocks(title="EduGenius Context Retriever") as demo:
     search_btn.click(fn=system.retrieve_evidence, inputs=[question_input, answer_input], outputs=[evidence_output])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+
+
+
+
+
+
+
+
+
+# import gradio as gr
+# import fitz  # PyMuPDF
+# import numpy as np
+# from langchain_text_splitters import RecursiveCharacterTextSplitter
+# from langchain_community.vectorstores import FAISS
+# from langchain_huggingface import HuggingFaceEmbeddings
+
+# class VectorSystem:
+#     def __init__(self):
+#         self.vector_store = None
+#         self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+#         self.all_chunks = []
+
+#     def process_file(self, file_obj):
+#         """Extracts text, preserves order, and builds the Vector Index"""
+#         if file_obj is None:
+#             return "No file uploaded."
+
+#         try:
+#             # 1. Extract Text
+#             text = ""
+#             file_path = file_obj.name
+            
+#             if file_path.lower().endswith('.pdf'):
+#                 doc = fitz.open(file_path)
+#                 for page in doc: text += page.get_text()
+#             elif file_path.lower().endswith('.txt'):
+#                 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
+#             text_splitter = RecursiveCharacterTextSplitter(
+#                 chunk_size=800,
+#                 chunk_overlap=150,
+#                 separators=["\n\n", "\n", ".", " ", ""]
+#             )
+#             self.all_chunks = text_splitter.split_text(text)
+
+#             if not self.all_chunks:
+#                 return "Could not extract text. Is the file empty?"
+
+#             # 3. Build Vector Index with ID Metadata
+#             metadatas = [{"id": i} for i in range(len(self.all_chunks))]
+            
+#             self.vector_store = FAISS.from_texts(
+#                 self.all_chunks, 
+#                 self.embeddings, 
+#                 metadatas=metadatas
+#             )
+            
+#             return f"✅ Success! Indexed {len(self.all_chunks)} 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."
+
+#         # 1. Get Initial Results (Core Matches)
+#         # FAISS returns L2 distance (Lower is better)
+#         results = self.vector_store.similarity_search_with_score(question, k=3)
+        
+#         # We need the vector for the QUESTION to do our own math later
+#         q_vector = np.array(self.embeddings.embed_query(question))
+        
+#         output_text = "### 🔍 Smart Context Analysis:\n"
+        
+#         for i, (doc, core_score) in enumerate(results):
+#             chunk_id = doc.metadata['id']
+            
+#             # 2. Identify Neighbors
+#             prev_chunk = self.all_chunks[chunk_id - 1] if chunk_id > 0 else ""
+#             next_chunk = self.all_chunks[chunk_id + 1] if chunk_id < len(self.all_chunks) - 1 else ""
+            
+#             # 3. Create the "Super Chunk" (Prev + Core + Next)
+#             super_chunk_text = f"{prev_chunk} {doc.page_content} {next_chunk}"
+            
+#             # 4. Calculate "Super Score" (Re-embedding on the fly)
+#             # We embed the Super Chunk and measure distance to Question
+#             super_vector = np.array(self.embeddings.embed_query(super_chunk_text))
+#             super_score = np.linalg.norm(q_vector - super_vector) # Euclidean Distance
+            
+#             output_text += f"\n#### 🎯 Match #{i+1}\n"
+            
+#             # 5. The Logic Test: Does Context Improve the Score?
+#             # Remember: LOWER score is BETTER (closer distance)
+            
+#             if super_score < core_score:
+#                 # CASE A: Context Helps! (Distance Reduced)
+#                 output_text += f"**✅ Context Added:** The surrounding text made the match stronger (Score improved from {core_score:.3f} to {super_score:.3f}).\n\n"
+#                 output_text += f"> {prev_chunk} **{doc.page_content}** {next_chunk}\n"
+#             else:
+#                 # CASE B: Context Dilutes! (Distance Increased or Same)
+#                 output_text += f"**⏹️ Context Ignored:** Surrounding text was irrelevant or noisy (Score worsened from {core_score:.3f} to {super_score:.3f}). Showing Core Match only.\n\n"
+#                 output_text += f"> **{doc.page_content}**\n"
+            
+#             output_text += "---\n"
+
+#         return output_text
+
+# # Initialize System
+# system = VectorSystem()
+
+# # --- Gradio UI ---
+# with gr.Blocks(title="EduGenius Context Retriever") as demo:
+#     gr.Markdown("# 🎓 EduGenius: Intelligent Context Retriever")
+#     gr.Markdown("Upload a Chapter. This system intelligently decides if it needs to read the surrounding paragraphs to answer your question.")
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             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)", placeholder="e.g., The heat causes it...")
+#             search_btn = gr.Button("Find Evidence", variant="secondary")
+            
+#             evidence_output = gr.Markdown(label="Relevant Text Chunks")
+
+#     upload_btn.click(fn=system.process_file, 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()