Update app.py

app.py

@@ -1,178 +1,97 @@
+import os
 import gradio as gr
-from transformers import pipeline
-import pandas as pd
+from huggingface_hub import InferenceClient
 import spaces
 
-# Load dataset
-from datasets import load_dataset
-ds = load_dataset('ZennyKenny/demo_customer_nps')
-df = pd.DataFrame(ds['train'])
-
-# Initialize model pipeline
-from huggingface_hub import login
-import os
-
-# Login using the API key stored as an environment variable
-hf_api_key = os.getenv("API_KEY")
-login(token=hf_api_key)
-
-classifier = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
-generator = pipeline("text2text-generation", model="google/flan-t5-base")
-
-# Function to classify customer comments
-@spaces.GPU
-def classify_comments(categories):
-    global df  # Ensure we're modifying the global DataFrame
-    sentiments = []
-    assigned_categories = []
-    for comment in df['customer_comment']:
-        # Classify sentiment
-        sentiment = classifier(comment)[0]['label']
-        # Generate category
-        category_str = ', '.join(categories)
-        prompt = f"What category best describes this comment? '{comment}' Please answer using only the name of the category: {category_str}."
-        category = generator(prompt, max_length=30)[0]['generated_text']
-        assigned_categories.append(category)
-        sentiments.append(sentiment)
-    df['comment_sentiment'] = sentiments
-    df['comment_category'] = assigned_categories
-    return df.to_html(index=False)  # Return all fields with appended sentiment and category
-
-# Function to add a category
-def add_category(categories, new_category):
-    if new_category.strip() != "" and len(categories) < 5:  # Limit to 5 categories
-        categories.append(new_category.strip())
-    return categories, "", f"**Categories:**\n" + "\n".join([f"- {cat}" for cat in categories])
-
-# Function to reset categories
-def reset_categories():
-    return [], "**Categories:**\n- None"
-
-# Function to load data from uploaded CSV
-def load_data(file):
-    global df  # Ensure we're modifying the global DataFrame
-    if file is not None:
-        file.seek(0)  # Reset file pointer
-        if file.name.endswith('.csv'):
-            custom_df = pd.read_csv(file, encoding='utf-8')
-        else:
-            return "Error: Uploaded file is not a CSV."
-        # Check for required columns
-        required_columns = ['customer_comment']
-        if not all(col in custom_df.columns for col in required_columns):
-            return f"Error: Uploaded CSV must contain the following column: {', '.join(required_columns)}"
-        df = custom_df
-        return "Custom CSV loaded successfully!"
-    else:
-        return "No file uploaded."
-
-# Function to use template categories
-def use_template():
-    template_categories = ["Product Experience", "Customer Support", "Price of Service", "Other"]
-    return template_categories, f"**Categories:**\n" + "\n".join([f"- {cat}" for cat in template_categories])
-
-# Custom CSS for button styling
+# Load API token from environment variable
+API_TOKEN = os.getenv("API_TOKEN")
+
+if not API_TOKEN:
+    raise ValueError("API_TOKEN environment variable is not set.")
+
+client = InferenceClient(model="mistralai/Mistral-Nemo-Instruct-2407", token=API_TOKEN)
+
+SYSTEM_MESSAGE = ("""
+Your name is Monty, and you represent montebello.ai. Your goal is to engage customers about montebello.ai’s AI solutions and encourage them to either schedule a call by clicking the "Call Me" button or by calling (415)-862-1695.
+**About montebello.ai:**
+We provide affordable, tailored AI solutions for small businesses, helping them save time, reduce costs, and improve efficiency.
+**Our Services:**
+- **AI Phone Assistant:** Set up in days for as little as $200/month to handle calls, customer inquiries, and appointment scheduling.
+- **Data Insights:** Analyze sales, inventory, and customer behavior for smarter business decisions.
+- **Productivity Boost:** Streamline workflows to free up your team’s time.
+- **Customer Experience:** Enhance engagement with AI-powered chatbots and personalized marketing.
+**Your Approach:**
+1. **Start by Asking:** 
+   - 'What would you like to know about montebello.ai?'
+2. **Provide Specific Examples:** 
+   - 'An AI Phone Agent can save you hours each week by handling common inquiries.’
+   - 'An AI Email Agent can reduce the amount of incoming emails that your team needs to handle by a significant amount.'
+   - 'An AI Appointment Agent can help you manage your time in the most effective way possible, freeing you up to work with your customers.'
+   
+3. **Highlight Affordability and Speed:** 
+   - 'We can set up you AI Agent in just a few days for as little as $200/month.’
+4. **Encourage Action:** 
+   - 'To experience our AI Phone Agent firsthand, call (415)-862-1695.’
+   - 'To experience our AI Email Agent firsthand, write us at hello@montebello.ai.’
+**Tone and Style:**
+- Friendly, energetic, and professional.
+- Keep responses very concise and focused on solving their problems.
+- Avoid technical jargon unless the customer is familiar with AI.
+**Closing:** 
+End by reiterating how montebello.ai can help and encourage them to take the next step by calling us, emailing us, or setting up an appointment.
+""")
+
+
+MAX_TOKENS = 256
+TEMPERATURE = 0.7
+TOP_P = 0.95
+
+# @spaces.GPU
+def respond(message, history: list[tuple[str, str]]):
+    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
+
+# Custom CSS
 custom_css = """
-button {
-    background-color: #4c6bfd !important;
-    border: none !important;
-    color: white !important;
-    padding: 10px 20px !important;
-    text-align: center !important;
-    text-decoration: none !important;
-    display: inline-block !important;
-    font-size: 16px !important;
-    border-radius: 5px !important;
-    transition: background-color 0.3s ease !important;
-}
-
-button:hover {
-    background-color: #3a52cc !important;
+body {
+    background-color: #f9d029 !important;
 }
 footer {
     visibility: hidden;
 }
+.submit-button {
+    background-color: #f9d029 !important;
+    color: #000000 !important;
+    border: none !important;
+    border-radius: 5px;
+    padding: 10px 20px;
+    font-weight: bold;
+}
+.submit-button:hover {
+    background-color: #e6b800 !important;
+}
 """
 
-# Gradio Interface
-with gr.Blocks(css=custom_css) as nps:
-    # State to store categories
-    categories = gr.State([])
-
-    # App title
-    gr.Markdown("# 🎉 Customer Comment Classifier 🎉")
-
-    # Short explanation
-    gr.Markdown("""
-    This app classifies customer comments into categories and assigns sentiment labels (Positive/Negative). 
-    You can upload your own dataset or use the provided template. The app will append the generated 
-    `comment_sentiment` and `comment_category` fields to your dataset.
-    """)
-
-    # File upload and instructions
-    with gr.Row():
-        with gr.Column(scale=1):
-            uploaded_file = gr.File(label="📂 Upload CSV", type="filepath", scale=1)
-        with gr.Column(scale=1):
-            gr.Markdown("""
-            **📝 Instructions:**
-            - Upload a CSV file with at least one column: `customer_comment`.
-            - If you don't have your own data, click **Use Template** to load a sample dataset.
-            """)
-            template_btn = gr.Button("✨ Use Template", size="sm")
-
-    gr.Markdown("---")
-
-    # Category section
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Category input and buttons
-            category_input = gr.Textbox(label="📝 New Category", placeholder="Enter category name", scale=1)
-            with gr.Row():
-                add_category_btn = gr.Button("➕ Add Category", size="sm")
-                reset_btn = gr.Button("🔄 Reset Categories", size="sm")
-            # Category display
-            category_status = gr.Markdown("**📂 Categories:**\n- None")
-        with gr.Column(scale=1):
-            gr.Markdown("""
-            **📝 Instructions:**
-            - Enter a category name and click **Add Category** to add it to the list.
-            - Click **Reset Categories** to clear the list.
-            - The `customer_comment` field will be categorized based on the categories you provide.
-            """)
-
-    gr.Markdown("---")
-
-    # Classify button and output
-    with gr.Row():
-        with gr.Column(scale=1):
-            classify_btn = gr.Button("🔍 Classify", size="sm")
-        with gr.Column(scale=3):  # Center the container and make it 75% of the window width
-            output = gr.HTML()
-
-    # Event handlers
-    add_category_btn.click(
-        fn=add_category,
-        inputs=[categories, category_input],
-        outputs=[categories, category_input, category_status]
-    )
-    reset_btn.click(
-        fn=reset_categories,
-        outputs=[categories, category_status]
-    )
-    uploaded_file.change(
-        fn=load_data,
-        inputs=uploaded_file,
-        outputs=output
-    )
-    template_btn.click(
-        fn=use_template,
-        outputs=[categories, category_status]
-    )
-    classify_btn.click(
-        fn=classify_comments,
-        inputs=categories,
-        outputs=output
-    )
+montebello = gr.ChatInterface(respond, css=custom_css)
 
-nps.launch(share=True)
+if __name__ == "__main__":
+    montebello.launch()