Create app.py

app.py

@@ -0,0 +1,96 @@
+import torch
+import os
+import json
+from huggingface_hub import hf_hub_download
+import gradio as gr
+
+repo_id = "iimran/AnalyserV2"
+def download_model_files(repo_id):
+    model_path = hf_hub_download(repo_id=repo_id, filename="model_weights.pth")
+    vocab_path = hf_hub_download(repo_id=repo_id, filename="vocab.json")
+    label_encoder_path = hf_hub_download(repo_id=repo_id, filename="label_encoder.json")
+    config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+    return model_path, vocab_path, label_encoder_path, config_path
+def get_transformer_model_class():
+    model_code = os.getenv("MODEL_VAR")
+
+    if model_code is None:
+        raise ValueError("Environment variable 'MODEL_VAR' is not set.")
+    exec(model_code, globals())
+    if "TransformerModel" not in globals():
+        raise NameError("The TransformerModel class was not defined after executing MODEL_VAR.")
+    TransformerModel = globals()["TransformerModel"]
+    #print("TransformerModel Class:", TransformerModel)
+
+    return TransformerModel
+def get_preprocess_function():
+    # Retrieve the preprocess_text code from the environment variable
+    preprocess_code = os.getenv("MODEL_PROCESS")
+
+    if preprocess_code is None:
+        raise ValueError("Environment variable 'MODEL_PROCESS' is not set.")
+    exec(preprocess_code, globals())
+    if "preprocess_text" not in globals():
+        raise NameError("The preprocess_text function was not defined after executing MODEL_PROCESS.")
+    #print("Preprocess Function Loaded:", globals()["preprocess_text"])
+
+    return globals()["preprocess_text"]
+def load_model_and_resources(repo_id):
+    model_path, vocab_path, label_encoder_path, config_path = download_model_files(repo_id)
+    try:
+        with open(vocab_path, "r") as f:
+            vocab = json.load(f)
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Vocabulary file not found at {vocab_path}. Please check the repository.")
+    except json.JSONDecodeError:
+        raise ValueError(f"Invalid JSON format in vocabulary file at {vocab_path}.")
+    try:
+        with open(label_encoder_path, "r") as f:
+            label_encoder_classes = json.load(f)
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Label encoder file not found at {label_encoder_path}. Please check the repository.")
+    except json.JSONDecodeError:
+        raise ValueError(f"Invalid JSON format in label encoder file at {label_encoder_path}.")
+    TransformerModel = get_transformer_model_class()
+    model = TransformerModel(vocab_size=len(vocab), num_classes=len(label_encoder_classes))
+    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))  # Use "cuda" if GPU is available
+    model.eval()
+    #print("Model Architecture:")
+    #print(model)
+
+    return model, vocab, label_encoder_classes
+preprocess_text = get_preprocess_function()
+def predict(text, model, vocab, label_encoder_classes):
+    input_ids, attention_mask = preprocess_text(text, vocab)
+    print("Input IDs:", input_ids)
+    print("Attention Mask:", attention_mask)
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask)
+        print("Model Outputs:", outputs)  # Debug: Inspect model outputs
+    
+    if outputs is None:
+        raise ValueError("Model returned None. Check the forward method and input data.")
+    predicted_class_idx = outputs.argmax(1).item()
+    predicted_label = label_encoder_classes[predicted_class_idx]
+    return predicted_label
+def create_gradio_interface():
+    model, vocab, label_encoder_classes = load_model_and_resources(repo_id)
+    def predict_wrapper(text):
+        return predict(text, model, vocab, label_encoder_classes)
+    interface = gr.Interface(
+        fn=predict_wrapper,  # Use the wrapper function
+        inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+        outputs=gr.Textbox(label="Predicted Label"),
+        title="Text Classification Model",
+        description="Enter text to classify it using the model.",
+        examples=[
+            ["There is a pothole on Main Street."],
+            ["The drainage system is clogged."],
+            ["The streetlights are not working."]
+        ],
+        cache_examples=False  # Disable caching
+    )
+    return interface
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch()