Update app.py

app.py

@@ -1,66 +1,166 @@
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, TextClassificationPipeline
-import torch
 import gradio as gr
-from openpyxl import load_workbook
-from numpy import mean
+import pandas as pd
+import numpy as np
+import random
 
-tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
-model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
+def show_stars():
+    """ Function that shows the HTML script for stars -Janika"""
+    stars_html = """
+    <style>
+        /* Title */
+        .title-text {
+        color: white;
+        font-family: sans-serif;
+        font-size: 14px;
+        font-weight: bold;
+        }
 
-tokenizer_keywords = AutoTokenizer.from_pretrained("transformer3/H2-keywordextractor")
-model_keywords = AutoModelForSeq2SeqLM.from_pretrained("transformer3/H2-keywordextractor")
+        /* Empty stars */
+        .star-rating {
+            display: inline-block;
+            font-size: 1.5em;
+            color: lightgray;
+        }
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-# Load the fine-tuned model and tokenizer
-new_model = AutoModelForSequenceClassification.from_pretrained('roberta-rating')
-new_tokenizer = AutoTokenizer.from_pretrained('roberta-rating')
+        /* Filled stars */
+        .star-rating .star.filled {
+            color: gold;
+        }
 
+        /* Box for the whole star rating component */
+        .rating-box {
+            border: 1px transparent;
+            background-color: #1f2937;
+            padding: 10px;
+            border-radius: 5px;
+            width: 100%;
+            margin-left: 0;
+        }
 
-# Create a classification pipeline
-classifier = TextClassificationPipeline(model=new_model, tokenizer=new_tokenizer, device=device)
+        /* Box for the title */
+        .title-box {
+            border: 1px transparent;
+            background-color: #ca8a04;
+            padding: 3px;
+            border-radius: 8px;
+            display: inline-block;
+            margin-bottom: 6px;
+        }
 
-# Add label mapping for sentiment analysis
-label_mapping = {1: '1/5', 2: '2/5', 3: '3/5', 4: '4/5', 5: '5/5'}
+        /* Box for stars */
+        .star-box {
+            border: 1px transparent;
+            background-color: #374151;
+            padding: 10px;
+            border-radius: 5px;
+            width: fit-content;
+            margin: 0;
+        }
+    </style>
 
-def parse_xl(file_path):
-    cells = []
+    <div class="rating-box">
+        <div class="title-box">
+            <p class="title-text">Star rating</p>
+        </div>
+        <div class="star-box">
+            <div class="star-rating" data-rating="4">
+                <span class="star">★</span>
+                <span class="star">★</span>
+                <span class="star">★</span>
+                <span class="star">★</span>
+                <span class="star">★</span>
+            </div>
+        </div>
+    </div>
 
-    workbook = load_workbook(filename=file_path)
-    for sheet in workbook.worksheets:
-        for row in sheet.iter_rows():
-            for cell in row:
-                if cell.value != None:
-                    cells.append(cell.value)
+    <script>
+        document.addEventListener('DOMContentLoaded', function() {
+            const ratingElement = document.querySelector('.star-rating');
+            const rating = parseInt(ratingElement.getAttribute('data-rating'));
+            const stars = ratingElement.querySelectorAll('.star');
+            for (let i = 0; i < rating; i++) {
+                stars[i].classList.add('filled');
+            }
+        });
+    </script>
+    """
+    return stars_html
 
-    return cells
+# Function to handle feedback submission -Piitu
+def handle_feedback(feedback, name):
+    return f"Thank you for your feedback, {name}!"
 
-def evaluate(file):
-    reviews = parse_xl(file)
-    ratings = []
-    text = ""
+# Function to update placeholder text -Piitu
+def update_placeholder(name):
+    if name:
+        return f"Enter your feedback here, {name}..."
+    else:
+        return "Enter your feedback here..."
 
-    for review in reviews:
-        ratings.append(int(classifier(review)[0]['label'].split('_')[1]))
-        text += review
-        text += " "
-            
-    inputs = tokenizer([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=50, max_length=1000)
-    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+# Random data for testing, actual data added later
+df = pd.DataFrame({
+    'Year': np.random.randint(2000, 2024, 25),
+    'Reviews': np.random.randint(120, 320, 25),
+})
 
-    inputs_keywords = tokenizer_keywords([text], max_length=1024, truncation=True, return_tensors="pt")
-    summary_ids_keywords = model_keywords.generate(inputs_keywords["input_ids"], num_beams=2, min_length=0, max_length=100)
-    keywords = tokenizer_keywords.batch_decode(summary_ids_keywords, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]   
+# Theme
+theme = gr.themes.Soft(
+    primary_hue="yellow",
+    secondary_hue="amber",
+    spacing_size="sm",
+    radius_size="lg",
+)
 
-    return round(mean(ratings), 2), summary, keywords
+with gr.Blocks(theme=theme) as demo:
 
-iface = gr.Interface(
-    fn=evaluate,
-    inputs=gr.File(label="Reviews", file_types=[".xlsx", ".xlsm", ".xltx", ".xltm"]),
-    outputs=[gr.Textbox(label="Rating"), gr.Textbox(label="Summary"), gr.Textbox(label="Keywords")],
-    title='Summarize Reviews',
-    description="Evaluate and summarize collection of reviews. Reviews are submitted as an Excel file, where each reviews is in its own cell."
-)
+    # Basic user interface for company's view -Janika
+    with gr.Tab("User Interface"):
+        with gr.Row():
+            with gr.Column(scale=2, min_width=300):
+                # Overall summary from all feedback
+                com_summary_output = gr.Textbox(label="Summary")
+
+            with gr.Column(scale=1, min_width=300):
+                # Average star rating across all feedback
+                com_star_rating = gr.HTML(value=show_stars())
+
+                # The most common keywords across all feedback
+                com_keywords_output = gr.Textbox(label="Keywords")
+
+
+     # Testing Area -Piitu
+    with gr.Tab("Testing Area"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=300):
+                name_input = gr.Textbox(label="Enter your name", placeholder="Enter your name here...")
+
+                text_input = gr.Textbox(label="Please give us feedback!", 
+                                        placeholder="Enter your feedback here...", 
+                                        max_length=5000)
+
+                # Update placeholder based on name input -Piitu
+                name_input.change(fn=update_placeholder, inputs=name_input, outputs=text_input)
+
+                # Send button -Piitu
+                send_button = gr.Button("Send")
+
+                # Output for feedback submission confirmation -Piitu
+                feedback_output = gr.Textbox(label="Submission Result", interactive=False)
+
+                # Link button to function that handles feedback submission -Piitu
+                send_button.click(handle_feedback, inputs=[text_input, name_input], outputs=feedback_output)
+
+            with gr.Column(scale=2, min_width=300):
+                # Estimated star rating from customer's feedback -Janika
+                cus_star_rating = gr.HTML(value=show_stars())
+
+                # Estimated sentiment from customer's feedback -Janika
+                cus_sentiment = gr.Textbox(label="Sentiment")
+                
+                # Summary from customer's feedback -Janika
+                cus_summary_output = gr.Textbox(label="Summary")
+
+                # The most common keywords from customer's feedback -Janika
+                cus_keywords_output = gr.Textbox(label="Keywords")  
 
-iface.launch(share=True)
+demo.launch()