new update

app.py

@@ -1,11 +1,12 @@
 import streamlit as st
-import pickle
 import numpy as np
-from huggingface_hub import hf_hub_download
-import os
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
 
-def load_model_test_steps():
+# Load model from Hugging Face
+def load_model():
     repo_id = 'Preethamreddy799/NLP_MODEL'
     filename = 'model_test_steps.h5'  # Assuming the model is in HDF5 format
 
@@ -18,13 +19,29 @@ def load_model_test_steps():
     print(f"Model loaded successfully from {cached_model_path}")
     return model
 
-model = load_model_test_steps()
+model = load_model()
 
-# Function to generate predictions
-def generate_test_steps(acceptance_criteria, test_data):
-    # Example input preprocessing (tokenization/embedding logic should match training)
-    input_features = np.array([len(acceptance_criteria), len(test_data)]).reshape(1, -1)
+# Initialize Tokenizer (Should match training tokenizer)
+tokenizer = Tokenizer(num_words=1000)
+
+# Function to preprocess text data
+def preprocess_text(input_text):
+    # Fit tokenizer (you should ideally fit the tokenizer during training and save it)
+    tokenizer.fit_on_texts([input_text])
 
+    # Convert the input text to a sequence of integers
+    sequence = tokenizer.texts_to_sequences([input_text])
+
+    # Pad the sequence to ensure uniform input size
+    input_features = pad_sequences(sequence, maxlen=100)  # Ensure length matches the expected input (100)
+    
+    return input_features
+
+# Function to generate test steps
+def generate_test_steps(acceptance_criteria, test_data):
+    # Preprocess the input text
+    input_features = preprocess_text(f"{acceptance_criteria} {test_data}")
+    
     # Generate prediction
     predicted_steps = model.predict(input_features)
     return predicted_steps
@@ -50,4 +67,4 @@ if st.button("Generate Test Steps"):
         else:
             st.error("Model not loaded. Please check the model repository and file.")
     else:
-        st.warning("Please fill in both Acceptance Criteria and Test Data.")
+        st.warning("Please fill in both Acceptance Criteria and Test Data.")