Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the model
+model_name = "knowledgator/comprehend_it-base"
+classifier = pipeline("zero-shot-classification", model=model_name, device="cpu")
+
+# Keywords associated with the "Value" label
+value_keywords = [
+    "cheap", "expensive", "worth", "waste", "value for money", "overpriced", "bargain",
+    "affordable", "pricey", "costly", "economical", "deal", "rip-off", "budget-friendly",
+    "high-priced", "low-priced", "discounted", "premium", "luxurious", "inexpensive",
+    "priced right", "steal", "splurge", "bang for the buck", "investment", "saver",
+    "money's worth", "exorbitant", "reasonable", "unreasonable", "priced well",
+    "cost-effective", "overvalued", "undervalued", "fair price", "high cost", "low cost",
+    "good deal", "bad deal", "profitable", "loss", "savings", "spendy", "wallet-friendly",
+    "financially smart", "economic", "lavish", "modestly priced", "upscale", "downscale"
+                ]
+
+
+# Function to check for value-related keywords in feedback
+def contains_value_keywords(feedback_text):
+    for keyword in value_keywords:
+        if keyword in feedback_text.lower():
+            return True
+    return False
+
+# Function to classify feedback
+def classify_feedback(feedback_text):
+    # Classify feedback using the loaded model
+    labels = ["Value", "Facilities", "Experience", "Functionality", "Quality"]
+    result = classifier(feedback_text, labels, multi_label=True)
+    
+    # Check for value-related keywords and adjust scores if necessary
+    if contains_value_keywords(feedback_text):
+        # Find the index of the "Value" label
+        try:
+            value_index = result["labels"].index("Value")
+            # Promote the score of the "Value" label
+            result["scores"][value_index] += 0.2  # Adjust the promotion strength as needed
+            # Ensure the score does not exceed 1
+            result["scores"][value_index] = min(result["scores"][value_index], 1.0)
+        except ValueError:
+            pass  # "Value" label not in the top results
+
+    # Get the top two labels associated with the feedback, after possible adjustment
+    top_labels_scores = sorted(zip(result["labels"], result["scores"]), key=lambda x: x[1], reverse=True)[:2]
+    top_labels, scores = zip(*top_labels_scores)
+    
+    # Generate HTML content for displaying the scores as meters/progress bars
+    html_content = ""
+    for i in range(len(top_labels)):
+        score_percentage = scores[i] * 100  # Convert score to percentage
+        html_content += f"<div><b>{top_labels[i]}:</b> {scores[i]:.2f} <div style='background-color: #e0e0e0; border-radius: 10px;'><div style='height: 24px; width: {score_percentage}%; background-color: #76b900; border-radius: 10px;'></div></div></div>"
+    
+    return html_content
+
+# Create Gradio interface
+feedback_textbox = gr.Textbox(label="Enter your feedback:")
+feedback_output = gr.HTML(label="Top 2 Labels with Scores:")
+
+gr.Interface(
+    fn=classify_feedback,
+    inputs=feedback_textbox,
+    outputs=feedback_output,
+    title="Feedback Classifier",
+    description="Enter your feedback and get the top 2 associated labels with scores."
+).launch()