Update app.py

app.py

@@ -1,61 +1,57 @@
+# Week - 3 Assignment - Integrate Traditional Chatbot with AI Service Project (Transformers) Praveen Kumar Parimi
+
+#importing the required libraries including transformers
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import pipeline
 
 """
 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("HuggingFaceH4/zephyr-7b-beta")
+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"
+model_name="meta-llama/Llama-2-7b-chat-hf"
 
-def respond(
+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,
-    temperature,
-    top_p,
+    max_tokens    
 ):
-    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]})
+    print("Inside chat_response progressing...") 
+    
+    messages = [{"role": "system", "content": system_message}]
 
+    print ("System Messages", messages)
+    
     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
-
-
+    
+    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(
-    respond,
+    chat_response,
     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)",
-        ),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")       
     ],
 )