Update app.py

app.py

@@ -1,61 +1,47 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import gradio as gr
-import torch
+from transformers import pipeline
 
-title = "????AI ChatBot"
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
-examples = [["How are you?"]]
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+# Load conversational pipeline
+conversation = pipeline("conversational")
 
 # Course information
 course_info = {
-    "courses": {
-        "Engineering": ["Civil Engineering", "Mechanical Engineering", "Electrical Engineering", "Software Engineering", "etc."],
-        "Information Technology": ["Computer Science", "Information Systems", "Cybersecurity", "Data Science", "etc."],
-        "Business": ["Business Administration", "Accounting", "Finance", "Marketing", "Management", "etc."],
-        "Health Sciences": ["Nursing", "Pharmacy", "Health Information Management", "Public Health", "etc."],
-        "Design and Architecture": ["Architecture", "Industrial Design", "Visual Communication", "etc."],
-        "Science": ["Environmental Science", "Biotechnology", "Chemistry", "Physics", "etc."],
-        "Law": ["Law", "Legal Studies", "etc."],
-        "Arts and Social Sciences": ["Communication", "Education", "International Studies", "Sociology", "etc."],
-        "Built Environment": ["Urban Planning", "Construction Management", "Property Economics", "etc."],
-        "Creative Industries": ["Animation", "Photography", "Creative Writing", "Film and Television", "etc."]
-    }
+    "Engineering": ["Civil Engineering", "Mechanical Engineering", "Electrical Engineering", "Software Engineering", "etc."],
+    "Information Technology": ["Computer Science", "Information Systems", "Cybersecurity", "Data Science", "etc."],
+    "Business": ["Business Administration", "Accounting", "Finance", "Marketing", "Management", "etc."],
+    "Health Sciences": ["Nursing", "Pharmacy", "Health Information Management", "Public Health", "etc."],
+    "Design and Architecture": ["Architecture", "Industrial Design", "Visual Communication", "etc."],
+    "Science": ["Environmental Science", "Biotechnology", "Chemistry", "Physics", "etc."],
+    "Law": ["Law", "Legal Studies", "etc."],
+    "Arts and Social Sciences": ["Communication", "Education", "International Studies", "Sociology", "etc."],
+    "Built Environment": ["Urban Planning", "Construction Management", "Property Economics", "etc."],
+    "Creative Industries": ["Animation", "Photography", "Creative Writing", "Film and Television", "etc."]
 }
 
-def predict(input, history=[]):
-    # Check if the user is asking about courses
-    if "course" in input.lower():
-        # Extract the subject from the user input
-        for subject in course_info["courses"]:
-            if subject.lower() in input.lower():
-                return course_info["courses"][subject], history
-
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response
-    history = model.generate(
-        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("")
-    response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
-    return response, history
-
-
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
-).launch()
+# Function to generate response using conversational AI
+def generate_response(user_input):
+    return conversation(user_input)[0]['generated_text']
+
+# Function to list courses based on category
+def list_courses(input_text):
+    for category, courses in course_info.items():
+        if category.lower() in input_text:
+            return f"Under {category}, we offer: {', '.join(courses)}"
+    return "I'm sorry, I couldn't find any courses matching that."
+
+# Main function to interact with the chatbot
+def chat():
+    print("Welcome to the Course Chatbot! Ask me about available courses or say goodbye to exit.")
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit", "goodbye"]:
+            print("Goodbye!")
+            break
+        else:
+            if "courses" in user_input:
+                bot_response = list_courses(user_input)
+            else:
+                bot_response = generate_response(user_input)
+            print("Bot:", bot_response)
+
+if __name__ == "__main__":
+    chat()