Update app.py

app.py

@@ -1,24 +1,19 @@
 import os
 import gradio as gr
 from langchain_community.document_loaders import YoutubeLoader, PyPDFLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
 from langchain_community.vectorstores import FAISS
 from langchain.chains import RetrievalQA
 
 # --- CONFIGURATION ---
 hf_token = os.getenv("HF_TOKEN")
-
-# Global variable
-vector_db_state = None
-
-# Using Mistral-7B-Instruct-v0.3
 repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
+# --- HELPER: LLM SETUP ---
 def get_llm():
     if not hf_token:
         raise ValueError("HF_TOKEN not found in secrets.")
-        
     return HuggingFaceEndpoint(
         repo_id=repo_id,
         max_new_tokens=512,
@@ -26,6 +21,9 @@ def get_llm():
         huggingfacehub_api_token=hf_token
     )
 
+# --- STATE ---
+vector_db_state = None
+
 # --- 1. PROCESSING ENGINE ---
 def process_content(url, file_obj):
     global vector_db_state
@@ -84,14 +82,9 @@ def generate_summary():
         docs = retriever.invoke("Summary")
         context = "\n\n".join([d.page_content for d in docs])
         
-        prompt = f"""
-        [INST] You are an expert tutor. Summarize the following content into concise bullet points.
-        
-        Content:
+        prompt = f"""[INST] Summarize this content into bullet points:
         {context}
-        
-        Summary: [/INST]
-        """
+        [/INST]"""
         return llm.invoke(prompt)
     except Exception as e:
         return f"Error: {str(e)}"
@@ -103,18 +96,12 @@ def generate_quiz():
     try:
         llm = get_llm()
         retriever = vector_db_state.as_retriever(search_kwargs={"k": 3})
-        docs = retriever.invoke("Quiz concepts")
+        docs = retriever.invoke("Key Concepts")
         context = "\n\n".join([d.page_content for d in docs])
         
-        prompt = f"""
-        [INST] Generate 3 multiple-choice questions based on this text.
-        Format: Question, Options, Correct Answer.
-        
-        Text:
+        prompt = f"""[INST] Create 3 multiple choice questions (with answers) based on this:
         {context}
-        
-        Questions: [/INST]
-        """
+        [/INST]"""
         return llm.invoke(prompt)
     except Exception as e:
         return f"Error: {str(e)}"
@@ -129,64 +116,37 @@ def generate_mindmap():
         docs = retriever.invoke("Structure")
         context = "\n\n".join([d.page_content for d in docs])
         
-        prompt = f"""
-        [INST] Create a simple Mermaid.js mindmap (graph TD) based on this text.
-        Output ONLY the code inside.
-        
-        Text:
+        prompt = f"""[INST] Create a Mermaid.js mindmap (graph TD) code block based on this:
         {context}
-        
-        Mermaid Code: [/INST]
-        """
+        [/INST]"""
         return llm.invoke(prompt)
     except Exception as e:
         return f"Error: {str(e)}"
 
-# --- 3. GRADIO INTERFACE ---
-custom_css = """
-#component-0 {max-width: 1200px; margin: auto;}
-.chat-window {height: 400px; overflow-y: scroll;}
-"""
-
-with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
-    gr.Markdown("# 🧠 OpenLearn AI (Free Edition)\n*Powered by Mistral-7B & Hugging Face*")
+# --- 3. UI ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 OpenLearn AI (Stable Build)")
     
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### 1. Upload Source Material")
-            yt_input = gr.Textbox(label="YouTube URL", placeholder="Paste video link here...")
-            pdf_input = gr.File(label="Or Upload PDF", file_types=[".pdf"])
-            process_btn = gr.Button("🚀 Process Content", variant="primary")
+            yt_input = gr.Textbox(label="YouTube URL")
+            pdf_input = gr.File(label="Upload PDF")
+            process_btn = gr.Button("🚀 Process", variant="primary")
             status_output = gr.Textbox(label="Status", interactive=False)
             
-            gr.Markdown("### 2. Smart Tools")
-            with gr.Accordion("Generate Study Aids", open=True):
-                summ_btn = gr.Button("📝 Generate Summary")
-                quiz_btn = gr.Button("❓ Create Quiz")
-                map_btn = gr.Button("🗺️ Generate Mind Map")
+            summ_btn = gr.Button("📝 Summary")
+            quiz_btn = gr.Button("❓ Quiz")
+            map_btn = gr.Button("🗺️ Mind Map")
 
         with gr.Column(scale=2):
-            gr.Markdown("### 3. Chat with your Content")
-            chatbot = gr.ChatInterface(
-                fn=chat_engine,
-                type="messages",
-                examples=["What is the main idea?", "Explain the key concepts."]
-            )
+            chatbot = gr.ChatInterface(fn=chat_engine)
             
     with gr.Row():
-        with gr.Tab("Summary"):
-            summ_output = gr.Markdown("Summary will appear here...")
-        with gr.Tab("Quiz"):
-            quiz_output = gr.Markdown("Quiz will appear here...")
-        with gr.Tab("Mind Map"):
-            map_output = gr.Code(language="mermaid", label="Mermaid Code")
-
-    process_btn.click(
-        process_content, 
-        inputs=[yt_input, pdf_input], 
-        outputs=[status_output]
-    )
-    
+        summ_output = gr.Markdown(label="Summary")
+        quiz_output = gr.Markdown(label="Quiz")
+        map_output = gr.Code(label="Mind Map Code")
+
+    process_btn.click(process_content, inputs=[yt_input, pdf_input], outputs=[status_output])
     summ_btn.click(generate_summary, inputs=None, outputs=summ_output)
     quiz_btn.click(generate_quiz, inputs=None, outputs=quiz_output)
     map_btn.click(generate_mindmap, inputs=None, outputs=map_output)