Update app.py

app.py

@@ -6,75 +6,9 @@ from huggingface_hub import InferenceClient
 from transformers import pipeline, AutoModel, AutoTokenizer
 
 model_name = "bmas10/ForJerry"
-model = AutoModel.from_pretrained(model_name)
-tokenizer =  AutoTokenizer.from_pretrained(model_name)
 messages = [
     {"role": "user", "content": "Who are you?"},
 ]
-pipe = pipeline("text-generation", model=model)
+pipe = pipeline("text-generation", model=model_name)
 pipe(messages)
- 
- 
-'''
-def chat(input_text, history=[]):
-    history.append(input_text)
-    prompt = "\n".join(history) + "\nAI:"  # Simple conversational format
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    output = model.generate(**inputs, max_length=512, pad_token_id=tokenizer.eos_token_id)
-    response = tokenizer.decode(output[:, inputs.input_ids.shape[-1]:][0], skip_special_tokens=True)
-    history.append(f"AI: {response}")
-    return response, history
 
-"""
-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
-"""
-print("starting Praveen's smarter chatbot...")
-
-"""
-The transformer model used here is Microsoft-trained Phi-3.5-mini-instruct
-"""
-
-#model_name = "microsoft/Phi-3.5-mini-instruct"
-
-chat_model = pipeline("text-generation", model=model_name)
-
-print("defining the chat_response function")
-
-def chat_response(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens    
-):
-
-    print("Inside chat_response progressing...") 
-    
-    messages = [{"role": "system", "content": system_message}]
-
-    print ("System Messages", messages)
-    
-    messages.append({"role": "user", "content": message})
-    
-    print ("Messages after adding user messages", messages)
-    
-    response = chat_model(messages)  #Passing system and user messages to the transformer model Phi-3.5-mini-instruct to get smarter responses
-      
-    print("Response received from model",response)
-    
-    return response[-1]['generated_text'][-1]['content']
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-
-demo = gr.ChatInterface(
-    chat,
-    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")       
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
-'''