Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import pandas as pd
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("Qwen/Qwen2.5-7B-Instruct")
+#load the book knowledge base
+books = pd.read_csv("knowledge_base.csv")
+books = books[["Title", "Author", "Genre", "Age_Range", "Description"]]
+books = books.dropna(subset=["Title", "Author", "Genre", "Description"])
+
+def search_books(ques, max_givenbooks=5):
+    """Searches the dataframe for rows matching keywords in the user's message. Looks across Title, Genre, and Description columns. """
+    if not ques:
+        return ""
+        
+    ques = str(ques).lower()
+    keywords = ques.split()
+    matches = pd.Series(False, index=books.index)
+
+    for word in keywords:
+        matches = matches | (
+            books["Title"].str.lower().str.contains(word, na=False) |
+            books["Genre"].str.lower().str.contains(word, na=False) |
+            books["Description"].str.lower().str.contains(word, na=False)
+        )
+
+    matched_books = books[matches].head(max_givenbooks)
+    if matched_books.empty:
+        return "No exact matches found in the catalog for this description."
+        
+    results_in = "Available books in our catalog matching your request:\n"
+    for idx, row in matched_books.iterrows():
+        results_in += f" - **Title** : {row['Title']} | **Author** : {row['Author']} | **Genre** : {row['Genre']} | **Age** : {row['Age_Range']}\n"
+        results_in += f" *Description* : {row['Description']}\n\n"
+    return results_in
+def respond(message, history):
+    catalog_info = search_books(message)
+
+    instructions = f"""You are the BookBuddy, who is a specialized book recommendation chatbot. 
+    Your goal is to recommend books to the user based on what they want. Use the catalog  from our database to answer the user. 
+    Prioritize the recommending books from this list if they match the user's perferences:
+    
+    {catalog_info}"""
+    
+    messages = [{"role": "system", "content": instructions}]
+    if history:
+        messages.extend(history)
+        
+    messages.append({"role": "user", "content": message})
+    
+    response = client.chat_completion(
+        messages=messages,
+        max_tokens=220
+    )
+    
+    return response.choices[0].message.content.strip()
+
+chatbot = gr.ChatInterface(
+    fn=respond,
+    title="BookBuddy",
+    description="Ask me for book recommendation on genre, age range, or what kind of story you want"
+)
+if __name__ == "__main__":
+    chatbot.launch()