make chatbot

app.py

@@ -1,44 +1,43 @@
 import os
 from dotenv import load_dotenv
-import gradio as gr  # Changed from streamlit to gradio
+import gradio as gr
+
 
-# ─── 1. Load environment variables ─────────────────────────────────────────
 load_dotenv()
 COHERE_API_KEY = os.getenv("COHERE_API_KEY")
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 
 if not COHERE_API_KEY or not GEMINI_API_KEY:
-    raise ValueError("❗️ Missing COHERE_API_KEY or GEMINI_API_KEY in environment")
+    raise ValueError("COHERE_API_KEY or GEMINI_API_KEY is missing")
+
 
-# ─── 2. Initialize vector store and embedder clients ───────────────────────
 import cohere
 import chromadb
 from google import genai
 from google.genai import types
 
-# Cohere client for embeddings
+
 co = cohere.Client(COHERE_API_KEY)
 
-# Gemini client for generation
+
 genai_client = genai.Client(api_key=GEMINI_API_KEY)
 
-# Chroma vector store client
+
 client = chromadb.Client()
 
-# Create or get existing collection
+
 collection = client.get_or_create_collection(name="inha-well", embedding_function=None)
 
-# ─── 3. Ingestion & Embedding (run only once) ──────────────────────────────
-# Check if collection is empty to avoid re-ingesting on each run
+
 total_docs = collection.count() if hasattr(collection, 'count') else len(collection.get()['documents'])
 
 if total_docs == 0:
     content_chunks = []
     for i in range(1, 4):
-        # Build the absolute path to each docs folder
+
         folder_path = os.path.join(os.getcwd(), "docs", f"p0000{i}")
         
-        # Add error handling for missing folders
+
         if not os.path.exists(folder_path):
             print(f"Warning: Folder {folder_path} not found")
             continue
@@ -63,7 +62,6 @@ if total_docs == 0:
             embeddings=embeddings
         )
 
-# ─── 4. Retrieval & Prompt Utilities ────────────────────────────────────────
 def retrieve_context(question, collection, top_k=2):
     qr = co.embed(
         texts=[question],
@@ -79,7 +77,7 @@ def get_prompt_plain(context: str, question: str) -> str:
 <<START>>
 You are a responsible person for answering Inha University (South Korea) information. Using the context below, answer within 300 tokens.
 Provide concise, well-structured, answer-oriented responses using markdown formatting for better readability.
-Use bullet points, bold text, and proper formatting to make the information clear and easy to read.
+Use bullet points, bold text, and proper formatting to make the information interactive, clear and easy to read.
 Do not repeat the prompt text in your output.
 
 Context:
@@ -101,14 +99,14 @@ def generate_agent_answer(context: str, question: str) -> str:
             stop_sequences=["<<END>>", "<<START>>"]
         )
     )
-    # Keep the markdown formatting for nice display
+
     return response.text.strip()
 
 def rag_answer(question: str, collection) -> str:
     context = retrieve_context(question, collection, top_k=2)
     return generate_agent_answer(context, question)
 
-# ─── 5. Gradio Interface Function ─────────────────────────────────────────────
+
 def answer_question(question):
     """
     Main function that processes the question and returns the answer
@@ -122,33 +120,42 @@ def answer_question(question):
     except Exception as e:
         return f"Sorry, I encountered an error: {str(e)}"
 
-# ─── 6. Gradio Frontend ─────────────────────────────────────────────────────
-# Create the Gradio interface
-demo = gr.Interface(
+
+import gradio as gr
+
+def answer_question(message, history):
+    history = history + [[message, ""]]
+    
+    # Replace this with your real logic / LLM call
+    response = "Here is the answer about SGCS..."  # ← Dummy response
+    history[-1][1] = response
+    return history, history
+
+# Define interface as chatbot-style inside Interface-like wrapper
+chat_interface = gr.ChatInterface(
     fn=answer_question,
-    inputs=gr.Textbox(
+    chatbot=gr.Chatbot(label="📚 Inha University SGCS Info Assistant"),
+    textbox=gr.Textbox(
         label="Ask me anything about Inha University SGCS…",
-        placeholder="e.g. How many Major Required credits should I take for graduation? ",
+        placeholder="e.g. How many Major Required credits should I take for graduation?",
         lines=2
     ),
-    outputs=gr.Markdown(
-        label="📌 Answer",
-        show_copy_button=True
-    ),
     title="📚 Inha University SGCS Info Assistant",
-    description="Get answers to your questions about Inha University SGCS .",
+    description="Get answers to your questions about Inha University SGCS.",
     theme=gr.themes.Soft(),
     examples=[
-        ["What classes should I normally take as 3nd semester ISE student?"],
-        ["Tell me about student organizations and activities"],
-        ["What percentage scholarship could I recieve with IELTS 7.0"]
+        "What classes should I normally take as 3rd semester ISE student?",
+        "Tell me about student organizations and activities",
+        "What percentage scholarship could I receive with IELTS 7.0"
     ]
 )
 
-# Launch the app
+demo = chat_interface
+
+
 if __name__ == "__main__":
     demo.launch(
-        share=True,  # Creates a public link
-        server_name="0.0.0.0",  # Allows external access
+        share=True,  
+        server_name="0.0.0.0", 
         
     )