app.py

import gradio as gr
import torch

# Global variable for model
model = None

def load_model():
    global model
    try:
        # APNI .pth FILE KA NAAM YAHAN LIKHEIN (jaise "my_model.pth")
        model_path = "model.pth" 
        
        # Try loading the complete model
        model = torch.load(model_path, map_location=torch.device('cpu'))
        model.eval()
        print("Model kamyabi se load ho gaya!")
    except Exception as e:
        print(f"Model load hone mein masla aaya: {e}")

def predict(input_text):
    if model is None:
        return "Error: Model theek se load nahi hua. Spaces ke 'Logs' check karein."
    
    try:
        # ====================================================================
        # IMPORTANT: Text Preprocessing (Text ko numbers mein convert karna)
        # ====================================================================
        # PyTorch model ko text ki jagah Tensors chahiye hotay hain.
        # Aapne text ko jis tareeqe se train karte waqt numbers mein badla tha 
        # (jaise Tokenizer, ya TF-IDF), woh logic yahan aayegi.
        
        # Misaal ke tor par (Dummy Tensor) - Isko apne model ke hisaab se change karein:
        # tokenized_input = torch.zeros(1, 10, dtype=torch.long) 
        
        # output = model(tokenized_input)
        # result = output.argmax(dim=1).item()
        
        # Abhi ke liye hum sirf ek confirmation message return kar rahay hain:
        return f"Aapka input '{input_text}' receive ho gaya hai.\n(Model se asal jawab lene ke liye line 26 par tokenization/preprocessing add karein)"
        
    except Exception as e:
        return f"Prediction Error: {e}"

# Start karte hi model load karo
load_model()

# Gradio Interface (UI)
iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=2, placeholder="Apni .txt file ka koi sentence yahan likhein..."),
    outputs="text",
    title="Mera PyTorch Model",
    description="Apna text input karein aur PyTorch model se output dekhein."
)

iface.launch()