Update app.py

app.py

@@ -1,36 +1,60 @@
+# 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)
+
+# 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
-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)
+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."
+)