Update app.py

app.py

@@ -4,61 +4,40 @@ from huggingface_hub import InferenceClient
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# Load a pre-trained conversational AI model
+# This model can generate responses based on user inputs
+chatbot = pipeline("conversational", model="microsoft/DialoGPT-medium")
+
+# Customize the bot's knowledge base with predefined responses
+faq_responses = {
+    "study tips": "Here are some study tips: 1) Break your study sessions into 25-minute chunks (Pomodoro Technique). 2) Test yourself frequently. 3) Stay organized using planners or apps like Notion or Todoist.",
+    "resources for studying": "You can find free study resources on websites like Khan Academy, Coursera, and edX. For research papers, check Google Scholar.",
+    "how to focus": "To improve focus, try studying in a quiet place, remove distractions like your phone, and use apps like Forest or Focus@Will.",
+    "time management tips": "Start by creating a to-do list each morning. Prioritize tasks using methods like Eisenhower Matrix and allocate specific time blocks for each task.",
+    "how to avoid procrastination": "Break tasks into smaller steps, set deadlines, and reward yourself after completing milestones. Tools like Trello can help you stay organized."
+}
+
+# Define the chatbot's response function
+def faq_chatbot(user_input):
+    # Check if the user's input matches any FAQ keywords
+    for key, response in faq_responses.items():
+        if key in user_input.lower():
+            return response
+
+    # If no FAQ match, use the AI model to generate a response
+    conversation = chatbot(user_input)
+    return conversation[0]['generated_text']
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=faq_chatbot,  # The function to handle user input
+    inputs=gr.Textbox(lines=2, placeholder="Ask me about studying tips or resources..."),  # Input text box
+    outputs="text",  # Output as text
+    title="Student FAQ Chatbot",
+    description="Ask me for study tips, time management advice, or about resources to help with your studies!"
 )
 
+# Launch the chatbot
+interface.launch()
+
 
-if __name__ == "__main__":
-    demo.launch()