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README.md

@@ -1,12 +1,6 @@
 ---
-title: Votum Demo
-emoji: 🐠
-colorFrom: green
-colorTo: red
+title: votum-demo
+app_file: gradio_app.py
 sdk: gradio
 sdk_version: 3.44.4
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

gradio_app.py

@@ -0,0 +1,70 @@
+# %%
+
+from threading import Thread
+
+import gradio as gr
+# import torch
+from text_generation import Client, InferenceAPIClient
+from transformers import (AutoModelForCausalLM, AutoModelForSeq2SeqLM,
+                          AutoTokenizer, BitsAndBytesConfig,
+                          TextIteratorStreamer)
+
+client = Client("http://20.83.177.108:8080")
+
+
+# text = ""
+# for response in client.generate_stream("What is Deep Learning?", max_new_tokens=20):
+#     if not response.token.special:
+#         text += response.token.text
+# print(text)
+
+
+
+def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
+    # Get the model and tokenizer, and tokenize the user text.
+    user_text = f"""You are an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a consice and factually correct manner. Also mention the relevant sections of the law wherever applicable.
+    ### Input: {user_text}
+    ### Response: """
+
+    text = ""
+    for response in client.generate_stream(user_text, max_new_tokens=max_new_tokens,repetition_penalty=1.05):
+        if not response.token.special:
+            text += response.token.text
+            yield text
+
+    return text
+
+
+def reset_textbox():
+    return gr.update(value='')
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=4):
+            user_text = gr.Textbox(
+                placeholder="What is the punishment for taking dowry. explain in detail.",
+                label="Question"
+            )
+            model_output = gr.Textbox(label="AI Response", lines=10, interactive=False)
+            button_submit = gr.Button(value="Submit")
+
+        with gr.Column(scale=1):
+            max_new_tokens = gr.Slider(
+                minimum=1, maximum=1000, value=250, step=10, interactive=True, label="Max New Tokens",
+            )
+            top_p = gr.Slider(
+                minimum=0.05, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
+            )
+            top_k = gr.Slider(
+                minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k",
+            )
+            temperature = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.8, step=0.1, interactive=True, label="Temperature",
+            )
+
+    user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
+    button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
+
+    demo.queue(max_size=32).launch(enable_queue=True,share=True)
+