Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model_name = "Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0"
+model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=False, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def generate_prompt(instruction, user_input):
+    """
+    Generates a prompt for the model to ensure it responds with the intent in the same language as the input.
+    """
+    return f"""
+### Instruction:
+{instruction}
+
+### Input:
+{user_input}
+
+### Response:
+"""
+
+def get_model_response(user_input, instruction="Identify and summarize the core intent in the same language:"):
+    """
+    Gets the model's response, ensuring it matches the input language and focuses on extracting a concise intent.
+    """
+    input_text = generate_prompt(instruction, user_input)
+    inputs = tokenizer([input_text], return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=300, use_cache=True)
+    response = tokenizer.batch_decode(outputs)[0]
+    return response.split("### Response:")[-1].strip()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=get_model_response,
+    inputs=[
+        gr.inputs.Textbox(label="Input Text"),
+        gr.inputs.Textbox(label="Instruction", default="Identify and summarize the core intent in the same language:"),
+    ],
+    outputs=gr.outputs.Textbox(label="Response"),
+    title="Intent Summarization",
+    description="Summarize the core intent of the input text in the same language.",
+)
+
+iface.launch()