Update app.py

app.py

@@ -1,21 +1,7 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import pandas as pd
 
-# Direct link to the image
-image_url = "https://drive.google.com/uc?export=view&id=1AB7sFKxPLkJE_RmyUDap6fFaDlu1XGJl"
-
-# Define the system message
-system_message = """
-You are a Career Counseling Chatbot. Analyze the student's academic performance and extracurricular activities to provide career guidance. Based on the provided data, respond in the following format and must include the following headings:
-# **Student's Primary Interest with Reason**
-# **Career Opportunities in the field**
-# **Universities in Pakistan for related field**
-# **Conclusion with name of field**
-Ensure that the analysis is based on the student's performance in subjects and extracurriculars, and suggest relevant career options with details on possible high ranking universities in Pakistan.
-"""
-
-# CSS to hide footer, customize button, and center image
+# CSS to hide footer and customize button
 css = """
 footer {display:none !important}
 .output-markdown{display:none !important}
@@ -80,25 +66,10 @@ footer {display:none !important}
     --tw-text-opacity: 1 !important;
     color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
 }
-#image-container {
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    height: auto; /* Adjust the height as needed */
-    margin-top: 20px; /* Adjust the margin as needed */
-}
-#compass-image {
-    max-width: 800px; /* Adjust the width as needed */
-    max-height: 600px; /* Adjust the height as needed */
-    object-fit: contain; /* Maintains aspect ratio */
-}
 """
 
 # Initialize the InferenceClient for chatbot
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Global variable to store chat history for the current session
-current_chat_history = []
+client = InferenceClient("HuggingFaceH4/zephyr-7b-alpha")
 
 # Define the function for chatbot response
 def respond(
@@ -109,8 +80,6 @@ def respond(
     temperature,
     top_p,
 ):
-    global current_chat_history
-    
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -118,11 +87,9 @@ def respond(
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-            current_chat_history.append(f"Assistant: {val[1]}")
-    
+
     messages.append({"role": "user", "content": message})
-    current_chat_history.append(f"User: {message}")
-    
+
     response = ""
 
     for message in client.chat_completion(
@@ -136,23 +103,6 @@ def respond(
         response += token
         yield response
 
-    # Append the assistant's final response to the history
-    current_chat_history.append(f"Assistant: {response}")
-
-def download_chat_history():
-    # Join the current chat history into a single string
-    history_str = "\n".join(current_chat_history)
-    # Save the chat history to a text file
-    with open("chat_history.txt", "w") as f:
-        f.write(history_str)
-    return "chat_history.txt"
-
-def clear_chat_history():
-    # Reset the current chat history
-    global current_chat_history
-    current_chat_history.clear()  # Clear the chat history
-    return "Chat history cleared."
-        
 def send_message(message, history, system_message, max_tokens, temperature, top_p):
     if message:
         history.append((message, ""))
@@ -167,86 +117,78 @@ def send_message(message, history, system_message, max_tokens, temperature, top_
         response_text = ""
         for r in response:
             response_text = r
-        # Apply HTML formatting to headings
-        formatted_response_text = response_text.replace(
-            "Student's Primary Interest with Reason:", "<h2><strong>Student's Primary Interest with Reason</strong></h2>"
-        ).replace(
-            "Career Opportunities in the field:", "<h2><strong>Career Opportunities in the field</strong></h2>"
-        ).replace(
-            "Universities in Pakistan for related field:", "<h2><strong>Universities in Pakistan for related field</strong></h2>"
-        ).replace(
-            "Conclusion with name of field:", "<h2><strong>Conclusion with name of field</strong></h2>"
-        )
-        history[-1] = (message, formatted_response_text)
+        history[-1] = (message, response_text)
     return history, gr.update(value="")
 
-# Excel reading function
-def read_excel(file):
-    df = pd.read_excel(file.name)
-    return df.to_string()
+# Description for the chatbot
+description = """
+Hello! I'm here to support you emotionally and answer any questions. How are you feeling today?
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
+
+# Motivational tagline for the new tab
+motivational_tagline = """
+Welcome to the Motivational Quotes tab! Let’s ignite your day with some inspiration. What do you need motivation for today?
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
+
+# Emotions Detector tagline for the new tab
+emotions_detector_tagline = """
+Know how your message sounds and how to improve the tone of the message with Emotions Detector.
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
+
+# Jokes tagline for the new tab
+jokes_tagline = """
+Ready for a good laugh? Ask me for a joke to lighten up your mood!
+<div style='color: green;'>Developed by Hashir Ehtisham</div>
+"""
 
-# Create the Gradio interface
+# Define the Gradio Blocks interface
 with gr.Blocks(css=css) as demo:
-    # Introduction Tab
-    with gr.Tab("Career Compass"):
-        with gr.Row(elem_id="image-container"):
-            gr.Image(image_url, elem_id="compass-image")
+    with gr.Tab("Emotional Support Chatbot"):
+        gr.Markdown("# Emotional Support Chatbot")
+        gr.Markdown(description)
         
-        gr.Markdown("# **Career Compass**")
-        gr.Markdown("### **Developed by Hashir (A student of APS DHA II Sec -D)**")
-        gr.Markdown("""
-        **Career Compass** is a cutting-edge AI-powered tool designed to provide personalized career guidance based on students' academic performance and extracurricular activities. The key features of this tool include:
-        - **Personalized Analysis:** Delivers career advice tailored to individual student profiles.
-        - **Streamlined Interface:** Simple and intuitive user experience.
-        - **Detailed Reports:** Offers insights into suitable career paths, relevant universities, and job opportunities.
-        - **Aptitude Test:** Take the Aptitude Test to determine your interest and find out the relevent field.
-        **Libraries Used:**
-        - **Gradio:** For creating the user interface.
-        - **Pandas:** For reading and analyzing Excel files.
-        - **Hugging API and LLM:** Zephyr-7b-beta For utilizing state-of-the-art language models.
+        system_message = gr.Textbox(value="You are a friendly Emotional Support Chatbot.", visible=False)
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label="Your message")
+        clear = gr.Button("Clear")
         
-        **How It Works:**
-        - **Detailed Analysis**
-        1. Upload your academic records.
-        2. Input your query regarding career guidance.
-        3. Get detailed recommendations and potential career paths!
-         - **Aptitude test**
-        1. Or choose to take "Aptitude test"
-        2. Click on "generate an aptitude test for me (10 questions)"
-        3. After that, 10 questions would appear.
-        4. Answer those questions and submit the response.
-        5. Get the AI analyzed answer and recommendations and potential career paths!
-        """)
-    
-    # Detailed Analysis Tab
-    with gr.Tab("Detailed Analysis"):
-        gr.Markdown("# Detailed Analysis")
-        gr.Markdown("Get personalized career guidance based on academic performance and extracurricular activities with Detailed Analysis.\n<div style='color: green;'>Developed by Hashir Ehtisham</div>")
-        
-        system_message_career = gr.Textbox(value=system_message, visible=False)
-        chatbot_career = gr.Chatbot()
-        msg_career = gr.Textbox(label="Enter the Excel Copied Data here")
-        
-        with gr.Row():
-            clear_career = gr.Button("New Chat")
-            download_button = gr.Button("Download Chat History")
-            submit_career = gr.Button("Submit")
+        with gr.Accordion("Additional Inputs", open=False):
+            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
 
-        with gr.Row():
-            download_output = gr.File(label="Download")
-            download_button.click(download_chat_history, outputs=download_output)
+        def respond_wrapper(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+            chat_history, _ = send_message(
+                message=message,
+                history=chat_history,
+                system_message=system_message_val,
+                max_tokens=max_tokens_val,
+                temperature=temperature_val,
+                top_p=top_p_val,
+            )
+            return gr.update(value=""), chat_history
 
-        with gr.Row():
-            clear_button = gr.Button("Clear Chat History")
-            status_output = gr.Textbox(label="Status", interactive=False)  # Add a Textbox for status output
-            clear_button.click(clear_chat_history, outputs=status_output)  # Use the status Textbox for output
+        msg.submit(respond_wrapper, [msg, chatbot, system_message, max_tokens, temperature, top_p], [msg, chatbot])
+        clear.click(lambda: None, None, chatbot, queue=False)
+    
+    with gr.Tab("Motivational Quotes"):
+        gr.Markdown("# Motivational Quotes")
+        gr.Markdown(motivational_tagline)
+        
+        system_message_motivational = gr.Textbox(value="You are a friendly Motivational Quotes Chatbot.", visible=False)
+        chatbot_motivational = gr.Chatbot()
+        msg_motivational = gr.Textbox(label="Your message")
+        clear_motivational = gr.Button("Clear")
         
         with gr.Accordion("Additional Inputs", open=False):
-            max_tokens_career = gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens")
-            temperature_career = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-            top_p_career = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+            max_tokens_motivational = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature_motivational = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p_motivational = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
 
-        def respond_wrapper_career(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+        def respond_wrapper_motivational(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
             chat_history, _ = send_message(
                 message=message,
                 history=chat_history,
@@ -255,48 +197,26 @@ with gr.Blocks(css=css) as demo:
                 temperature=temperature_val,
                 top_p=top_p_val,
             )
-            return gr.update(value=chat_history), gr.update(value="")
+            return gr.update(value=""), chat_history
 
-        submit_career.click(
-            respond_wrapper_career,
-            inputs=[msg_career, chatbot_career, system_message_career, max_tokens_career, temperature_career, top_p_career],
-            outputs=[chatbot_career, msg_career],
-        )
-        
-        clear_career.click(lambda: None, None, chatbot_career, queue=False)
-
-    # File Upload Tab
-    with gr.Tab("Upload Data"):
-        gr.Markdown("# Upload Data")
-        file_input = gr.File(label="Upload Excel file")
-        excel_output = gr.Textbox(label="Excel Content")
-        file_input.change(read_excel, inputs=file_input, outputs=excel_output)
+        msg_motivational.submit(respond_wrapper_motivational, [msg_motivational, chatbot_motivational, system_message_motivational, max_tokens_motivational, temperature_motivational, top_p_motivational], [msg_motivational, chatbot_motivational])
+        clear_motivational.click(lambda: None, None, chatbot_motivational, queue=False)
     
-   # Aptitude Test Tab
-    with gr.Tab("Aptitude Test"):
-        gr.Markdown("# Aptitude Test")
-        gr.Markdown("""
-        Take the Aptitude Test to determine your interest and find out your interests towards different professional fields.
-        <div style='color: gray;'>After generating the test, use the format e.g 1c,2b,3a,4c and more to answer the questions</div>
-        <div style='color: gray;'> </div>
-        <div style='color: green;'>Developed by Hashir Ehtisham</div>
-        """)
-
-        system_message_aptitude = gr.Textbox(value="You are an Aptitude Test Chatbot", visible=False)
-        chatbot_aptitude = gr.Chatbot()
-        msg_aptitude = gr.Textbox(label="Your message")
-
-        with gr.Row():
-            clear_aptitude = gr.Button("Clear")
-            submit_aptitude = gr.Button("Submit")
-            example_button = gr.Button("Generate Aptitude Test (10 questions)")
-
+    with gr.Tab("Emotions Detector"):
+        gr.Markdown("# Emotions Detector")
+        gr.Markdown(emotions_detector_tagline)
+        
+        system_message_emotions = gr.Textbox(value="You are an Emotions Detector Chatbot. Analyze the tone of the message (happy, sad, angry, neutral) and answer back.", visible=False)
+        chatbot_emotions = gr.Chatbot()
+        msg_emotions = gr.Textbox(label="Your message")
+        clear_emotions = gr.Button("Clear")
+        
         with gr.Accordion("Additional Inputs", open=False):
-            max_tokens_aptitude = gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max new tokens")
-            temperature_aptitude = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-            top_p_aptitude = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+            max_tokens_emotions = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature_emotions = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p_emotions = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
 
-        def respond_wrapper_aptitude(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+        def respond_wrapper_emotions(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
             chat_history, _ = send_message(
                 message=message,
                 history=chat_history,
@@ -305,62 +225,36 @@ with gr.Blocks(css=css) as demo:
                 temperature=temperature_val,
                 top_p=top_p_val,
             )
-            return chat_history
+            return gr.update(value=""), chat_history
 
-        submit_aptitude.click(
-            respond_wrapper_aptitude,
-            inputs=[
-                msg_aptitude,
-                chatbot_aptitude,
-                system_message_aptitude,
-                max_tokens_aptitude,
-                temperature_aptitude,
-                top_p_aptitude,
-            ],
-            outputs=[chatbot_aptitude],
-        )
-
-        clear_aptitude.click(lambda: None, None, chatbot_aptitude)
-
-        def copy_to_message():
-            return gr.update(value="Create a 10-question aptitude test designed to assess a student's interests in various academic fields such as Engineering, Medicine, Computer Science, Law, Finance and Arts. Each question should be multiple-choice with six options, and should ask the student about their preferences, interests, or inclinations towards activities, subjects, or scenarios related to these fields. Avoid questions that require prior knowledge in any specific subject. The test should be suitable for high school students exploring potential career paths.")
-        submit_aptitude.click(
-            respond_wrapper_aptitude,
-            inputs=[
-                msg_aptitude,
-                chatbot_aptitude,
-                system_message_aptitude,
-                max_tokens_aptitude,
-                temperature_aptitude,
-                top_p_aptitude,
-            ],
-            outputs=[chatbot_aptitude],
-        )
-
-        example_button.click(copy_to_message, [], [msg_aptitude])
-
-    # Simple Chatbot Tab (new tab integration)
-    with gr.Tab("General Guidance & Emotional Support"):
-        gr.Markdown("# General Guidance & Emotional Support")
-        gr.Markdown("""
-        A compassionate career counseling chatbot providing personalized guidance on career paths and emotional support for your journey.
-        <div style='color: green;'>Developed by Hashir Ehtisham</div>
-        """)
-
-        system_message_simple = gr.Textbox(value="You are an AI powered chatbot named as Career Compass built by Hashir Ehtisham who is a student of APS DHA II Sec -D to help students, teachers, and parents find the best career paths based on students' interests and academic performance.", visible=False)
-        chatbot_simple = gr.Chatbot()
-        msg_simple = gr.Textbox(label="Type a message")
+        msg_emotions.submit(respond_wrapper_emotions, [msg_emotions, chatbot_emotions, system_message_emotions, max_tokens_emotions, temperature_emotions, top_p_emotions], [msg_emotions, chatbot_emotions])
+        clear_emotions.click(lambda: None, None, chatbot_emotions, queue=False)
+    
+    with gr.Tab("Jokes for You"):
+        gr.Markdown("# Jokes for You")
+        gr.Markdown(jokes_tagline)
         
-        with gr.Row():
-            clear_simple = gr.Button("Clear")
-            submit_simple = gr.Button("Submit")
+        system_message_jokes = gr.Textbox(value="You are a friendly Jokes Chatbot. Provide a joke when asked.", visible=False)
+        chatbot_jokes = gr.Chatbot()
+        msg_jokes = gr.Textbox(label="Your message")
+        clear_jokes = gr.Button("Clear")
         
+        with gr.Accordion("Examples", open=False):
+            gr.Examples(
+                examples=[
+                    ["Tell me a joke"],
+                    ["Make me laugh"],
+                    ["Say something funny"],
+                ],
+                inputs=msg_jokes,
+            )
+
         with gr.Accordion("Additional Inputs", open=False):
-            max_tokens_simple = gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens")
-            temperature_simple = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-            top_p_simple = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
-        
-        def respond_wrapper_simple(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
+            max_tokens_jokes = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature_jokes = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p_jokes = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        def respond_wrapper_jokes(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
             chat_history, _ = send_message(
                 message=message,
                 history=chat_history,
@@ -369,22 +263,10 @@ with gr.Blocks(css=css) as demo:
                 temperature=temperature_val,
                 top_p=top_p_val,
             )
-            return chat_history
-
-        submit_simple.click(
-            respond_wrapper_simple,
-            inputs=[
-                msg_simple,
-                chatbot_simple,
-                system_message_simple,
-                max_tokens_simple,
-                temperature_simple,
-                top_p_simple,
-            ],
-            outputs=[chatbot_simple],
-        )
+            return gr.update(value=""), chat_history
 
-        clear_simple.click(lambda: None, None, chatbot_simple)
+        msg_jokes.submit(respond_wrapper_jokes, [msg_jokes, chatbot_jokes, system_message_jokes, max_tokens_jokes, temperature_jokes, top_p_jokes], [msg_jokes, chatbot_jokes])
+        clear_jokes.click(lambda: None, None, chatbot_jokes, queue=False)
 
-# Launch the Gradio app
-demo.launch()
+# Launch the Gradio interface
+demo.launch()