Update MODEL.PY

MODEL.PY

@@ -1,7 +1,11 @@
 import pandas as pd
 from datasets import Dataset
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
+import torch
+import gradio as gr
+from huggingface_hub import HfApi, Repository
 
-# Sample Data
+# Step 1: Create a sample dataset
 data = {
     "Name": ["John Doe", "Jane Smith", "Mike Johnson"],
     "Email": ["johndoe@example.com", "janesmith@example.com", "mikej@example.com"],
@@ -14,11 +18,56 @@ data = {
     ]
 }
 
-# Create a Pandas DataFrame
+# Create a DataFrame
 df = pd.DataFrame(data)
 
 # Convert the DataFrame to a Hugging Face Dataset
 dataset = Dataset.from_pandas(df)
 
-# Display the dataset
-print(dataset)
+# Step 2: Upload the dataset to Hugging Face
+# Authenticate using your Hugging Face credentials
+api = HfApi()
+repo_id = "SailajaS/case-feedback-dataset"  # Replace with your actual repo name
+
+# Create a repo to store the dataset
+api.create_repo(repo_id=repo_id, repo_type="dataset")
+
+# Initialize the repository
+repo = Repository(local_dir="./dataset_repo", clone_from=repo_id)
+
+# Save the dataset to a CSV file
+dataset.to_csv("./dataset_repo/dataset.csv")
+
+# Push the dataset to Hugging Face
+repo.push_to_hub()
+
+# Step 3: Load pre-trained DistilBERT model and tokenizer
+model_name = "distilbert-base-uncased"
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+
+# Step 4: Define the prediction function
+def predict_case_feedback(details):
+    inputs = tokenizer(details, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax(dim=-1).item()
+    
+    # Map predicted class to label
+    feedback_labels = ["Negative", "Positive", "Neutral"]
+    feedback = feedback_labels[predicted_class]
+    
+    return f"Predicted Feedback: {feedback}"
+
+# Step 5: Create Gradio interface for the prediction function
+interface = gr.Interface(
+    fn=predict_case_feedback,  # Prediction function
+    inputs="text",              # Input: Text
+    outputs="text",             # Output: Text
+    title="Case Feedback Prediction",
+    description="Enter the case details to predict feedback (Positive, Negative, Neutral)"
+)
+
+# Launch Gradio app
+interface.launch()