Update app.py

app.py

@@ -1,63 +1,43 @@
-# import gradio as gr
-# import random
-# from datasets import load_dataset
-# from transformers import pipeline
-
-# # Load Hindi Poetry Dataset
-# dataset = load_dataset("rahul7star/hindi-poetry")
-# poems = dataset["train"]
-
-# def get_random_poem():
-#     poem = random.choice(poems)
-#     return f"**{poem['poet']} ({poem['category']})**\n\n{poem['poem']}"
-
-# # Load Text Generation Model
-# # Load Text Generation Model
-# text_generator = pipeline("text-generation", model="rahul7star/hindi_poetry_language_model")
-
-
-# def generate_poem(prompt):
-#     result = text_generator(prompt, max_length=100, do_sample=True, temperature=0.7)
-#     return result[0]["generated_text"]
-
-# # Gradio UI
-# demo = gr.Interface(
-#     title="📜 हिंदी कविता जनरेटर",
-#     description="देखें हिंदी कविताएँ और अपनी खुद की कविता बनाएं!",
-#     theme="huggingface",
-#     fn=generate_poem,
-#     inputs=gr.Textbox(placeholder="अपनी कविता की शुरुआत लिखें...", label="कविता की शुरुआत"),
-#     outputs=gr.Textbox(label="उत्पन्न कविता"),
-#     examples=[["प्रकृति की सुंदरता"], ["प्रेम की निशानी"]],
-#     live=True
-# )
-
-# # Add button to display a random poem
-# demo.launch(share=True)
 import gradio as gr
-from fastai.text.all import load_learner
-from huggingface_hub import hf_hub_download
-# Download model and tokenizer from HF
-model_path = hf_hub_download("rahul7star/hindi_poetry_language_model", filename="model.pkl")
-#tokenizer_path = hf_hub_download("rahul7star/Rahul-FineTunedLLM-v03", filename="tokenizer/tokenizer.pkl")
-
-# Load the learner from Hugging Face
-learn = load_learner(model_path)
-
-# Step 9: Define Gradio interface to generate poems based on theme
-def generate_poem_gradio(theme):
-    # Generate a poem based on the theme input
-    text = learn.predict(theme)[0]
-    return text
-
-# Define the Gradio interface
-import gradio as gr
-
-iface = gr.Interface(
-    fn=generate_poem_gradio,  # Function to call for generating poems
-    inputs="text",  # User input will be a text field (theme)
-    outputs="text",  # Output will be the generated poem
-    title="Hindi Poem Generator",
-    description="Enter a theme (in Hindi), and get a poem generated based on that theme."
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# 1️⃣ Load Model & Tokenizer from Hugging Face Hub
+model_name = "rahul7star/hindi_poetry_language_model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Ensure model uses the correct pad token
+model.config.pad_token_id = tokenizer.pad_token_id
+
+# 2️⃣ Function to Generate Hindi Poetry
+def generate_poetry(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    with torch.no_grad():
+        output = model.generate(
+            input_ids, 
+            max_length=max_length, 
+            temperature=temperature, 
+            top_k=top_k, 
+            top_p=top_p,
+            pad_token_id=tokenizer.pad_token_id
+        )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# 3️⃣ Gradio Interface
+interface = gr.Interface(
+    fn=generate_poetry,
+    inputs=[
+        gr.Textbox(label="Enter Prompt", placeholder="Start your Hindi poem..."),
+        gr.Slider(50, 500, step=10, value=100, label="Max Length"),
+        gr.Slider(0.1, 1.5, step=0.1, value=0.7, label="Temperature"),
+        gr.Slider(1, 100, step=1, value=50, label="Top-k Sampling"),
+        gr.Slider(0.1, 1.0, step=0.05, value=0.95, label="Top-p Sampling"),
+    ],
+    outputs=gr.Textbox(label="Generated Hindi Poem"),
+    title="Hindi Poetry Generator ✨",
+    description="Generate beautiful Hindi poetry using a fine-tuned GPT-2 model. Just enter a prompt and adjust parameters for creative variations!",
 )
-iface.launch(share=True)
+
+# 4️⃣ Run the Gradio App
+interface.launch(share=True)