Update app.py

app.py

@@ -1,36 +1,69 @@
 import gradio as gr
 from transformers import pipeline
 import PyPDF2
-import os
+import json
 
-# 📌 Load syllabus from PDF
+# 📌 Step 1: Extract text from PDF
 def read_pdf(file_path):
-    if not os.path.exists(file_path):
-        return "Error: Syllabus file not found!"
-    
     try:
         with open(file_path, "rb") as file:
             reader = PyPDF2.PdfReader(file)
             text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
-        return text if text.strip() else "Error: Could not extract text from syllabus."
+        return text
     except Exception as e:
         return f"Error loading syllabus: {str(e)}"
 
-syllabus_text = read_pdf("Syllabus.pdf")  # 🛑 Ensure the filename matches exactly
+syllabus_text = read_pdf("Syllabus.pdf")
 
-# 📌 Load AI Model
+# 📌 Step 2: Extract subjects and topics
+def extract_subjects_and_topics(text):
+    subjects = {}
+    current_subject = None
+
+    for line in text.split("\n"):
+        line = line.strip()
+        if line.isupper():  # Assuming subject names are in uppercase
+            current_subject = line
+            subjects[current_subject] = []
+        elif current_subject and line:
+            subjects[current_subject].append(line)
+
+    return subjects
+
+subjects_data = extract_subjects_and_topics(syllabus_text)
+
+# 📌 Step 3: Convert to JSON format for easy searching
+subjects_json = json.dumps(subjects_data, indent=4)
+
+# 📌 Load AI Model for Chatbot
 chatbot = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
 
-# 📌 Define Chat Function
+# 📌 Step 4: Chat Function
 def chat_response(message):
-    if "syllabus" in message.lower():
-        return syllabus_text
+    message = message.lower()
+
+    # If user asks for subjects
+    if "subjects" in message:
+        return "📚 Available Subjects:\n\n" + "\n".join(subjects_data.keys())
+
+    # If user asks for topics under a subject
+    for subject, topics in subjects_data.items():
+        if subject.lower() in message:
+            return f"📖 Topics under {subject}:\n\n" + "\n".join(topics)
+
+    # If chatbot response is needed
     response = chatbot(message, max_length=100, do_sample=True)
     return response[0]['generated_text']
 
-# 📌 Create Gradio Interface
-iface = gr.Interface(fn=chat_response, inputs="text", outputs="text", title="Bit GPT 0.2.8")
+# 📌 Step 5: Create Gradio Interface
+iface = gr.Interface(
+    fn=chat_response,
+    inputs="text",
+    outputs="text",
+    title="Bit GPT 0.2.8",
+    description="Ask me about syllabus subjects, topics, or general questions!"
+)
 
-# 📌 Launch App
+# 📌 Step 6: Launch App
 if __name__ == "__main__":
     iface.launch()