Update app.py

app.py

@@ -1,98 +1,35 @@
-# import gradio as gr
-# from transformers import GPT2LMHeadModel, GPT2Tokenizer
-# import torch
-# import os
-
-# # Load token from environment variable
-# token = os.getenv('ACCESS_SECRET')
-
-# # Specify the repository ID
-# model_repo_id = "Ajay12345678980/QA_Chatbot"
-
-# # Load model and tokenizer
-# model = GPT2LMHeadModel.from_pretrained(model_repo_id, use_auth_token=token)
-# tokenizer = GPT2Tokenizer.from_pretrained(model_repo_id, use_auth_token=token)
-
-# # Define prediction function
-# def predict(text):
-#     inputs = tokenizer.encode(text, return_tensors="pt")
-#     with torch.no_grad():
-#         outputs = model.generate(inputs, max_length=50, do_sample=True)
-#     prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     return prediction
-
-# # Set up Gradio interface
-# interface = gr.Interface(
-#     fn=predict, 
-#     inputs="text", 
-#     outputs="text", 
-#     title="GPT-2 Text Generation",
-#     description="Enter some text and see what the model generates!"
-# )
-
-# # Launch the Gradio app
-# if __name__ == "__main__":
-#     interface.launch()
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("Ajay12345678980/QA_Chatbot")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import os
+
+# Load token from environment variable
+token = os.getenv('ACCESS_SECRET')
+
+# Specify the repository ID
+model_repo_id = "Ajay12345678980/QA_Chatbot"
+
+# Load model and tokenizer
+model = GPT2LMHeadModel.from_pretrained(model_repo_id, use_auth_token=token)
+tokenizer = GPT2Tokenizer.from_pretrained(model_repo_id, use_auth_token=token)
+
+# Define prediction function
+def predict(text):
+    inputs = tokenizer.encode(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(inputs, max_length=50, do_sample=True)
+    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return prediction
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs="text", 
+    outputs="text", 
+    title="GPT-2 Text Generation",
+    description="Enter some text and see what the model generates!"
 )
 
-
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()