Update app.py

app.py

@@ -10,6 +10,7 @@ import random
 import nltk
 from nltk.tokenize import sent_tokenize
 import PyPDF2
+import io
 from joblib import dump, load
 
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -29,7 +30,11 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
-nltk.download('punkt')
+# Download NLTK data
+try:
+    nltk.download('punkt', quiet=True)
+except Exception as e:
+    logger.warning(f"Failed to download NLTK data: {e}")
 
 class Config:
     MODEL_NAME = "microsoft/DialoGPT-medium"
@@ -38,8 +43,11 @@ class Config:
     REQUEST_TIMEOUT = 10
     MAX_DEPTH = 1
     SIMILARITY_THRESHOLD = 0.5
-    # INDEXED_URLS = {"https://calendar.google.com/calendar/ical/south.spencer%40sspencer.k12.in.us/public/basic.ics", "https://calendar.google.com/calendar/ical/hs.spencer%40sspencer.k12.in.us/public/basic.ics", "https://calendar.google.com/calendar/ical/le.spencer%40sspencer.k12.in.us/public/basic.ics", "https://calendar.google.com/calendar/ical/re.spencer%40sspencer.k12.in.us/public/basic.ics", "https://drive.google.com/file/d/1gNTdzCSRG8h-GY21zMsmuyQEv_16eiq_/view?usp=drive_link", "https://drive.google.com/file/d/1wxQKZuI77-3cPceJ8mKbREQDSgEKXCFX/view?usp=drive_link", "https://drive.google.com/file/d/1vDix0a2shD7U46IVOprqnNWMzC7D01vl/view?usp=drive_link", "https://drive.google.com/file/d/1ShYZl7re5cdNoORTCJ-6vVp2rKM9xa_d/view?usp=drive_link", "https://drive.google.com/file/d/1Gky4kY1KGt6P4wxx7OPatmfSc_VZGx6v/view?usp=drive_link", "https://drive.google.com/file/d/1aoK7oA45je8Tkl7v2dEnYseHXFaisJ-W/view?usp=drive_link", "https://drive.google.com/file/d/1NG3ak3YqLtNEbYwT5U52tnmgyTK_6NMo/view?usp=drive_link", "https://drive.google.com/file/d/1FH8r8JeOEXhS7BUIWKM-FyrLN8Nu3oHm/view?usp=drive_link", "https://drive.google.com/file/d/1YZ4dushzkbmQPI98rnUJqfmDQJoVfvpn/view?usp=drive_link", "https://drive.google.com/file/d/1PJj23lAF45N0sBg6PknJLHl-OYsF0QWU/view?usp=drive_link", "https://drive.google.com/file/d/1DeJ3fTy61I7n86ofmrZUg6jzAqOsg-w-/view?usp=drive_link", "https://drive.google.com/file/d/1aunHxceGluv4l9cWWEKadWzNwnpEeFX2/view?usp=drive_link", "https://drive.google.com/file/d/1FBhLaXc1To-I21eqhVMzneDnmsZSfi3r/view?usp=drive_link", "https://drive.google.com/file/d/1Kvjpiq9Y_zUviyxYN9SERJ8dHxco_br8/view?usp=drive_link", "https://drive.google.com/file/d/18YN4uX8o108FpYqiWHbOqynnMT-w7fIe/view?usp=drive_link", "https://drive.google.com/file/d/1ju5O6J_3w5ffjWJcIfzQmTa2JFiK4rG_/view?usp=drive_link", "https://drive.google.com/file/d/1awbYPSnxSIx2Gb08DSJgUyx-dGDkgmHH/view?usp=drive_link", 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"https://drive.google.com/file/d/1lOxe6wXipNrVlaKSnnsJbIONL6Uy-d8g/view?usp=drive_link", "https://drive.google.com/file/d/1kUQbdlun7envlUdoPUceRkj6I8ZgKeMp/view?usp=drive_link", "https://drive.google.com/file/d/1gSqgQ2l0rH2jnVz-ARSUw4-U3XantMkO/view?usp=drive_link", "https://drive.google.com/file/d/1V7dd-6XDNFMEIbN3VD22piZh9WZCcaOB/view?usp=drive_link", "https://drive.google.com/file/d/1IzgGN5rJeg3OsVRJ6hUAhuU71hxka7Ar/view?usp=drive_link", "https://drive.google.com/file/d/1BRnPEbTxmngMrcIrXo281IZxUd8nXZA9/view?usp=drive_link", "https://drive.google.com/file/d/1Zjw8UUng_K-HCUn7PL1yEeAdB-cmIbV6/view?usp=drive_link", "https://drive.google.com/file/d/12yTbrvBGzw8-oOsGB1lg9sNIQShEcWa8/view?usp=drive_link", "https://drive.google.com/file/d/1U-SXNthDWWouZTZOJs9goQi56K3JgnjB/view?usp=drive_link", "https://drive.google.com/file/d/1I94yYVlrFw-8tEdn2MDu2rq_HEVdwy-9/view?usp=drive_link", "https://drive.google.com/file/d/1ix0dXN-mrIL8j6rVcd74x-XnLiBt5QJp/view?usp=drive_link", "https://drive.google.com/file/d/1QKwTKws8-Z2LT6LmMTxf_Cwhpr5H-2-M/view?usp=drive_link", "https://drive.google.com/file/d/1deyrvKHB3NiJLIbOgKTMk8lzYjrMaiVS/view?usp=drive_link", "https://drive.google.com/file/d/1yEhbB1zHfjzcyYPL2nc0cG-M1YrT0nQY/view?usp=drive_link", "https://drive.google.com/file/d/1CbiHM6uDIPs4NcUo9URevEH2t-1UO2Iv/view?usp=drive_link", "https://drive.google.com/file/d/1n5ZfeLQMl6S4ioCLrxuuDZgIxAELba6S/view?usp=drive_link", "https://drive.google.com/file/d/1twK-7ijaK-lEbaO1ASaM4cqCaS-a8Lzx/view?usp=drive_link", "https://drive.google.com/file/d/1GjzScaq_hyUinN9F-_2HiwqsbkZuAmxv/view?usp=drive_link", "https://drive.google.com/file/d/17hEDmpHAMOH1W0TlbhYP-xEVCO2FZ_Dv/view?usp=drive_link", "https://drive.google.com/file/d/1nH55ggPRX9IAey6BTEpIt-tXGfFquCmO/view?usp=drive_link”}
-    INDEXED_URLS =  {"https://drive.google.com/file/d/1d5kkqaQkdiA2SwJ0JFrTuKO9zauiUtFz/**/view?usp=sharing"}          
+    CHUNK_SIZE = 512
+    MAX_WORKERS = 5
+    INDEXED_URLS = {
+        "https://drive.google.com/file/d/1d5kkqaQkdiA2SwJ0JFrTuKO9zauiUtFz/**/view?usp=sharing"
+    }
 
 class ResourceItem:
     def __init__(self, url: str, content: str, resource_type: str):
@@ -47,14 +55,25 @@ class ResourceItem:
         self.content = content
         self.type = resource_type
         self.embedding = None
+        self.chunks = []
+        self.chunk_embeddings = []
 
     def __str__(self):
         return f"ResourceItem(type={self.type}, url={self.url}, content_length={len(self.content)})"
 
+    def create_chunks(self, chunk_size=Config.CHUNK_SIZE):
+        """Split content into overlapping chunks for better context preservation"""
+        words = self.content.split()
+        overlap = chunk_size // 4  # 25% overlap
+        
+        for i in range(0, len(words), chunk_size - overlap):
+            chunk = ' '.join(words[i:i + chunk_size])
+            if chunk:
+                self.chunks.append(chunk)
+
 class RobustCrawler:
-    def __init__(self, max_retries=3, backoff_factor=0.3, proxies=None):
+    def __init__(self, max_retries=3, backoff_factor=0.3):
         self.ua = UserAgent()
-        self.proxies = proxies or {}
         self.session = self._create_robust_session(max_retries, backoff_factor)
 
     def _create_robust_session(self, max_retries, backoff_factor):
@@ -82,7 +101,7 @@ class RobustCrawler:
             "Upgrade-Insecure-Requests": "1"
         }
 
-    def crawl_with_exponential_backoff(self, url, timeout=10):
+    def crawl_with_exponential_backoff(self, url, timeout=Config.REQUEST_TIMEOUT):
         try:
             time.sleep(random.uniform(0.5, 2.0))
             response = self.session.get(
@@ -98,52 +117,57 @@ class RobustCrawler:
 
 class SchoolChatbot:
     def __init__(self):
-        print("Initializing SchoolChatbot...")
+        logger.info("Initializing SchoolChatbot...")
         self.setup_models()
         self.resources = []
         self.visited_urls = set()
         self.crawl_and_index_resources()
 
     def setup_models(self):
-        logger.info("Setting up models...")
-        self.tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)
-        self.model = AutoModelForCausalLM.from_pretrained(Config.MODEL_NAME)
-        self.embedding_model = SentenceTransformer(Config.EMBEDDING_MODEL)
-        logger.info("Models setup completed successfully.")
+        try:
+            logger.info("Setting up models...")
+            self.tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME)
+            self.model = AutoModelForCausalLM.from_pretrained(Config.MODEL_NAME)
+            self.embedding_model = SentenceTransformer(Config.EMBEDDING_MODEL)
+            logger.info("Models setup completed successfully.")
+        except Exception as e:
+            logger.error(f"Failed to setup models: {e}")
+            raise RuntimeError("Failed to initialize required models")
 
     def crawl_and_index_resources(self):
         logger.info("Starting to crawl and index resources...")
-        with ThreadPoolExecutor(max_workers=5) as executor:
-            for url in Config.INDEXED_URLS:
-                executor.submit(self.crawl_url, url, 0)
-        logger.info("Crawling and indexing completed.")
+        with ThreadPoolExecutor(max_workers=Config.MAX_WORKERS) as executor:
+            futures = [executor.submit(self.crawl_url, url, 0) for url in Config.INDEXED_URLS]
+            for future in futures:
+                try:
+                    future.result()
+                except Exception as e:
+                    logger.error(f"Error in crawling thread: {e}")
+        logger.info(f"Crawling completed. Indexed {len(self.resources)} resources.")
 
     def crawl_url(self, url, depth):
         if depth > Config.MAX_DEPTH or url in self.visited_urls:
             return
         self.visited_urls.add(url)
+        
         crawler = RobustCrawler()
         response = crawler.crawl_with_exponential_backoff(url)
         if not response:
-            logger.warning(f"Failed to retrieve {url}")
             return
+
         content_type = response.headers.get("Content-Type", "").lower()
-        if "text/calendar" in content_type or url.endswith(".ics"):
-            self.extract_ics_content(url, response.text)
-        elif "text/html" in content_type:
-            soup = BeautifulSoup(response.content, 'html.parser')
-            self.extract_page_content(url, soup)
-            if depth < Config.MAX_DEPTH:
-                for link in soup.find_all('a', href=True):
-                    full_url = urljoin(url, link['href'])
-                    if self.is_valid_url(full_url):
-                        time.sleep(random.uniform(0.5, 2.0))
-                        self.crawl_url(full_url, depth + 1)
-        elif "application/pdf" in content_type:
-            self.extract_pdf_content(url, response.content)
-        else:
-            logger.warning(f"Unrecognized content-type ({content_type}). Storing as raw.")
-            self.store_resource(url, response.text, 'unknown')
+        
+        try:
+            if "text/calendar" in content_type or url.endswith(".ics"):
+                self.extract_ics_content(url, response.text)
+            elif "text/html" in content_type:
+                self.extract_html_content(url, response)
+            elif "application/pdf" in content_type:
+                self.extract_pdf_content(url, response.content)
+            else:
+                self.store_resource(url, response.text, 'unknown')
+        except Exception as e:
+            logger.error(f"Error processing {url}: {e}")
 
     def extract_ics_content(self, url, ics_text):
         try:
@@ -151,117 +175,225 @@ class SchoolChatbot:
             events = []
             for component in cal.walk():
                 if component.name == "VEVENT":
-                    summary = component.get("SUMMARY", "No Summary")
-                    dtstart = component.get("DTSTART", "No Start Date")
-                    dtend = component.get("DTEND", "No End Date")
-                    event_str = f"{summary} from {dtstart} to {dtend}"
-                    events.append(event_str)
+                    event = self._format_calendar_event(component)
+                    if event:
+                        events.append(event)
             if events:
-                combined_text = "\n".join(events)
-                self.store_resource(url, combined_text, 'calendar')
+                self.store_resource(url, "\n".join(events), 'calendar')
         except Exception as e:
             logger.error(f"Error parsing ICS from {url}: {e}")
 
-    def extract_page_content(self, url, soup):
-        content_sections = []
-        for tag in soup.find_all(['main', 'article', 'section']):
-            text_found = tag.get_text(strip=True)
-            if text_found:
-                content_sections.append(text_found)
-        for heading in soup.find_all(['h1', 'h2', 'h3']):
-            section_content = [heading.get_text(strip=True)]
-            next_elem = heading.find_next_sibling()
-            while next_elem and next_elem.name in ['p', 'ul', 'ol']:
-                section_content.append(next_elem.get_text(strip=True))
-                next_elem = next_elem.find_next_sibling()
-            if section_content:
-                content_sections.append(' '.join(section_content))
-        if content_sections:
-            full_content = ' '.join(content_sections)
-            self.store_resource(url, full_content, 'webpage')
+    def _format_calendar_event(self, event):
+        try:
+            summary = event.get("SUMMARY", "No Summary")
+            start = event.get("DTSTART", "").dt
+            end = event.get("DTEND", "").dt
+            description = event.get("DESCRIPTION", "")
+            location = event.get("LOCATION", "")
+            
+            event_details = [f"Event: {summary}"]
+            if start:
+                event_details.append(f"Start: {start}")
+            if end:
+                event_details.append(f"End: {end}")
+            if location:
+                event_details.append(f"Location: {location}")
+            if description:
+                event_details.append(f"Description: {description}")
+                
+            return " | ".join(event_details)
+        except Exception:
+            return None
+
+    def extract_html_content(self, url, response):
+        try:
+            soup = BeautifulSoup(response.content, 'html.parser')
+            
+            # Remove unwanted elements
+            for element in soup.find_all(['script', 'style', 'nav', 'footer']):
+                element.decompose()
+            
+            content_sections = []
+            
+            # Extract main content
+            main_content = soup.find(['main', 'article', 'div'], class_=['content', 'main-content'])
+            if main_content:
+                content_sections.append(main_content.get_text(strip=True, separator=' '))
+            
+            # Extract headings and their associated content
+            for heading in soup.find_all(['h1', 'h2', 'h3']):
+                section = [heading.get_text(strip=True)]
+                next_elem = heading.find_next_sibling()
+                while next_elem and next_elem.name in ['p', 'ul', 'ol', 'div']:
+                    section.append(next_elem.get_text(strip=True))
+                    next_elem = next_elem.find_next_sibling()
+                content_sections.append(' '.join(section))
+            
+            if content_sections:
+                self.store_resource(url, ' '.join(content_sections), 'webpage')
+                
+            # Process links if within depth limit
+            if len(self.visited_urls) < Config.MAX_DEPTH:
+                self._process_links(soup, url)
+                
+        except Exception as e:
+            logger.error(f"Error extracting HTML content from {url}: {e}")
+
+    def _process_links(self, soup, base_url):
+        try:
+            for link in soup.find_all('a', href=True):
+                full_url = urljoin(base_url, link['href'])
+                if self.is_valid_url(full_url) and full_url not in self.visited_urls:
+                    time.sleep(random.uniform(0.5, 2.0))
+                    self.crawl_url(full_url, len(self.visited_urls))
+        except Exception as e:
+            logger.error(f"Error processing links from {base_url}: {e}")
 
     def extract_pdf_content(self, url, pdf_content):
         try:
-            pdf_reader = PyPDF2.PdfFileReader(pdf_content)
-            text = ''
-            for page in range(pdf_reader.getNumPages()):
-                text += pdf_reader.getPage(page).extractText()
-            if text:
-                self.store_resource(url, text, 'pdf')
+            pdf_file = io.BytesIO(pdf_content)
+            pdf_reader = PyPDF2.PdfReader(pdf_file)
+            text_content = []
+            
+            for page in pdf_reader.pages:
+                try:
+                    text_content.append(page.extract_text())
+                except Exception as e:
+                    logger.error(f"Error extracting text from PDF page: {e}")
+                    continue
+            
+            if text_content:
+                self.store_resource(url, ' '.join(text_content), 'pdf')
         except Exception as e:
             logger.error(f"Error extracting PDF content from {url}: {e}")
 
     def store_resource(self, url, text_data, resource_type):
         try:
-            text_chunks = [text_data[i:i+512] for i in range(0, len(text_data), 512)]
-            embeddings = [self.embedding_model.encode(chunk) for chunk in text_chunks]
-            average_embedding = np.mean(embeddings, axis=0)
+            # Create resource item and split into chunks
             item = ResourceItem(url, text_data, resource_type)
-            item.embedding = average_embedding
-            self.resources.append(item)
-            logger.debug(f"Stored resource: {url} (type={resource_type}). Total resources: {len(self.resources)}")
+            item.create_chunks()
+            
+            # Generate embeddings for chunks
+            item.chunk_embeddings = [
+                self.embedding_model.encode(chunk) 
+                for chunk in item.chunks
+            ]
+            
+            # Calculate average embedding
+            if item.chunk_embeddings:
+                item.embedding = np.mean(item.chunk_embeddings, axis=0)
+                self.resources.append(item)
+                logger.debug(f"Stored resource: {url} (type={resource_type})")
+            
         except Exception as e:
             logger.error(f"Error storing resource {url}: {e}")
 
     def is_valid_url(self, url):
-        parsed = urlparse(url)
-        return bool(parsed.scheme) and bool(parsed.netloc)
+        try:
+            parsed = urlparse(url)
+            return bool(parsed.scheme) and bool(parsed.netloc)
+        except Exception:
+            return False
 
-    def find_best_matching_resource(self, query):
+    def find_best_matching_chunks(self, query, n_chunks=3):
         if not self.resources:
-            return None, -1.0
-        query_embedding = self.embedding_model.encode(query)
-        best_resource, best_score = None, -1.0
-        for resource in self.resources:
-            if resource.embedding is not None:
-                score = cosine_similarity([query_embedding], [resource.embedding])[0][0]
-                if score > best_score:
-                    best_score = score
-                    best_resource = resource
-        return best_resource, best_score
-
-    def extract_relevant_snippet(self, content, query):
-        sentences = sent_tokenize(content)
-        query_embedding = self.embedding_model.encode(query)
-        sentence_embeddings = self.embedding_model.encode(sentences)
-        similarities = cosine_similarity([query_embedding], sentence_embeddings)[0]
-        best_idx = np.argmax(similarities)
-        start_idx = max(0, best_idx - 1)
-        end_idx = min(len(sentences), best_idx + 2)
-        snippet = ' '.join(sentences[start_idx:end_idx])
-        return snippet
+            return []
+        
+        try:
+            query_embedding = self.embedding_model.encode(query)
+            all_chunks = []
+            
+            for resource in self.resources:
+                for chunk, embedding in zip(resource.chunks, resource.chunk_embeddings):
+                    score = cosine_similarity([query_embedding], [embedding])[0][0]
+                    if score > Config.SIMILARITY_THRESHOLD:
+                        all_chunks.append((chunk, score, resource.url))
+            
+            # Sort by similarity score and get top n chunks
+            all_chunks.sort(key=lambda x: x[1], reverse=True)
+            return all_chunks[:n_chunks]
+            
+        except Exception as e:
+            logger.error(f"Error finding matching chunks: {e}")
+            return []
 
     def generate_response(self, user_input):
-        best_resource, similarity_score = self.find_best_matching_resource(user_input)
-        if best_resource is None or similarity_score < Config.SIMILARITY_THRESHOLD:
-            return "Great question! According to my resources, I couldn't find a direct answer. Please rephrase your query or contact support for more information."
-        snippet = self.extract_relevant_snippet(best_resource.content, user_input)
-        conversation_history = f"User: {user_input}\nAssistant: {snippet}"
-        new_user_input_ids = self.tokenizer.encode(conversation_history + self.tokenizer.eos_token, return_tensors='pt')
-        chat_history_ids = self.model.generate(new_user_input_ids, max_length=Config.MAX_TOKENS, pad_token_id=self.tokenizer.eos_token_id)
-        response = self.tokenizer.decode(chat_history_ids[:, new_user_input_ids.shape[-1]:][0], skip_special_tokens=True)
-        return f"Great question! According to my resources, {response}. For more details, see the answer in full context here: {best_resource.url}"
+        try:
+            # Find best matching chunks
+            best_chunks = self.find_best_matching_chunks(user_input)
+            
+            if not best_chunks:
+                return "I apologize, but I couldn't find any relevant information in my knowledge base. Could you please rephrase your question or ask about something else?"
+            
+            # Prepare context from best matching chunks
+            context = "\n".join([chunk[0] for chunk in best_chunks])
+            
+            # Prepare conversation history
+            conversation = f"Context: {context}\nUser: {user_input}\nAssistant:"
+            
+            # Generate response
+            input_ids = self.tokenizer.encode(conversation, return_tensors='pt')
+            response_ids = self.model.generate(
+                input_ids,
+                max_length=Config.MAX_TOKENS,
+                pad_token_id=self.tokenizer.eos_token_id,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True
+            )
+            
+            response = self.tokenizer.decode(
+                response_ids[:, input_ids.shape[-1]:][0],
+                skip_special_tokens=True
+            )
+            
+            # Format response with source
+            source_urls = list(set(chunk[2] for chunk in best_chunks))
+            sources = "\n\nSources:\n" + "\n".join(source_urls)
+            
+            return response + sources
+            
+        except Exception as e:
+            logger.error(f"Error generating response: {e}")
+            return "I apologize, but I encountered an error while processing your question. Please try again."
 
 def create_gradio_interface(chatbot):
     def respond(user_input):
         return chatbot.generate_response(user_input)
+        
     interface = gr.Interface(
         fn=respond,
-        inputs=gr.Textbox(label="Ask a Question", placeholder="Type your question here..."),
-        outputs=gr.Textbox(label="Answer", placeholder="Response will appear here..."),
+        inputs=gr.Textbox(
+            label="Ask a Question",
+            placeholder="Type your question here...",
+            lines=2
+        ),
+        outputs=gr.Textbox(
+            label="Answer",
+            placeholder="Response will appear here...",
+            lines=5
+        ),
         title="School Information Chatbot",
-        description="Ask about school events, holidays, or other information. Supports calendar and website queries.",
+        description="Ask about school events, policies, or other information. The chatbot will provide answers based on available school documents and resources.",
         examples=[
             ["What events are happening this week?"],
             ["When is the next board meeting?"],
-            ["Are there any upcoming holidays?"]
+            ["What is the school's attendance policy?"]
         ],
-        theme=gr.themes.Monochrome(),
-        flagging_mode="never"  # Replaced allow_flagging with flagging_mode
+        theme=gr.themes.Soft(),
+        flagging_mode="never"
     )
     return interface
 
 if __name__ == "__main__":
-    chatbot = SchoolChatbot()
-    interface = create_gradio_interface(chatbot)
-    interface.launch(server_name="0.0.0.0", server_port=7860, inbrowser=True)
+    try:
+        chatbot = SchoolChatbot()
+        interface = create_gradio_interface(chatbot)
+        interface.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            debug=True
+        )
+    except Exception as e:
+        logger.error(f"Failed to start application: {e}")