Update app.py

app.py

@@ -1,99 +1,53 @@
+import os
 import gradio as gr
-import requests, json
-
-public_ip = '71.202.66.108'
-
-model = 'llama3.1:latest'  # You can replace the model name if needed
-context = [] 
-
-ollama_serve = f"http://{public_ip}:11434/api/generate"
-
-# Call Ollama API
-def generate(prompt, context, top_k, top_p, temp):
-    r = requests.post(ollama_serve,
-                      json={
-                          'model': model,
-                          'prompt': prompt,
-                          'context': context,
-                          'options': {
-                              'top_k': top_k,
-                              'temperature': top_p,
-                              'top_p': temp
-                          }
-                      },
-                      stream=True)
-    r.raise_for_status()
-
-    response = ""  
-
-    for line in r.iter_lines():
-        body = json.loads(line)
-        response_part = body.get('response', '')
-        
-        if 'error' in body:
-            yield f"Error: {body['error']}"
-            return
-
-        # Append token to the growing response and yield the entire response so far
-        if response_part:
-            response += response_part
-            yield response  # Yield the growing response incrementally
-
-        if body.get('done', False):
-            context = body.get('context', [])
-            return  # End the generator once done
-
-def chat(input, chat_history, top_k, top_p, temp):
-    chat_history = chat_history or []
-    global context
-    
-    # Initialize the user input as part of the chat history
-    chat_history.append((input, ""))  # Add user input first
-    response = ""  # Initialize empty response
-
-    # Stream each part of the response as it's received
-    response_stream = generate(input, context, top_k, top_p, temp)
-
-    for response_part in response_stream:
-        response = response_part  # Keep updating with the new part of the response
-        # Update the latest assistant response (the second part of the tuple)
-        chat_history[-1] = (input, response)
-        yield chat_history, chat_history  # Yield the updated chat history
-
-
-######################### Gradio Code ##########################
-# background-image: url('https://cdn.shoplightspeed.com/shops/631940/files/45845092/800x800x3/apple-apple-macpro-trashcan-12-core-27ghz-64gb-1tb.jpg');
-block = gr.Blocks(css="""
-    .chatbox {
-        background-image: url('https://cdn.shoplightspeed.com/shops/631940/files/45845092/800x800x3/apple-apple-macpro-trashcan-12-core-27ghz-64gb-1tb.jpg');
-        background-size: contain;  /* Ensure the image fits the height */
-        background-repeat: no-repeat;
-        background-position: center;
-        height: 100%;  /* Make the chatbox fill the available height */
-    }
-""")
-
-
-
-with block:
-
-    gr.Markdown("""<h1><center> Trashcan AI </center></h1>""")
-    gr.Markdown("""<h3><center> LLama3.1 hosted on a 2013 "Trashcan" Mac Pro with ollama </center></h3>""")
-
-    # Add a custom class 'chatbox' to apply the background image
-    chatbot = gr.Chatbot(elem_classes="chatbox")
-    message = gr.Textbox(placeholder="Type here")
-
-    state = gr.State()
-    with gr.Row():
-        top_k = gr.Slider(0.0, 100.0, label="top_k", value=40, info="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)")
-        top_p = gr.Slider(0.0, 1.0, label="top_p", value=0.9, info="Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)")
-        temp = gr.Slider(0.0, 2.0, label="temperature", value=0.8, info="The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)")
-
-    submit = gr.Button("SEND")
-    
-    # Use .click() to trigger the response streaming
-    submit.click(chat, inputs=[message, state, top_k, top_p, temp], outputs=[chatbot, state])
-
-if __name__ == "__main__":
-    block.launch()
+import ollama
+public_ip = os.environ['PUBLIC_IP']
+port = os.environ['PORT']
+
+model = 'llama3.1'
+from ollama import Client
+client = Client(host=f'http://{public_ip}:{port}')
+
+def format_history(msg: str, history: list[list[str, str]], system_prompt: str):
+    chat_history = [{"role": "system", "content":system_prompt}]
+    for query, response in history:
+        chat_history.append({"role": "user", "content": query})
+        chat_history.append({"role": "assistant", "content": response})  
+    chat_history.append({"role": "user", "content": msg})
+    return chat_history
+
+def generate_response(msg: str, history: list[list[str, str]], system_prompt: str, top_k: int, top_p: float, temperature: float):
+    chat_history = format_history(msg, history, system_prompt)
+    response = client.chat(model=model, 
+                           stream=True, 
+                           messages=chat_history, 
+                           options={'top_k':top_k, 'top_p':top_p, 'temperature':temperature})
+    message = ""
+    for partial_resp in response:
+        token = partial_resp["message"]["content"]
+        message += token
+        yield message
+
+
+chatbot = gr.ChatInterface(
+                generate_response,
+                chatbot=gr.Chatbot(
+                        avatar_images=["user.png", "chatbot.png"],
+                        height="64vh"
+                    ),
+                additional_inputs=[
+                    gr.Textbox("You are a helpful assistant and always try to answer user queries to the best of your ability.", label="System Prompt"),
+                    gr.Slider(0.0,100.0, label="top_k", value=40, info="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)"),
+                    gr.Slider(0.0,1.0, label="top_p", value=0.9, info=" Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)"),
+                    gr.Slider(0.0,2.0, label="temperature", value=0.4, info="The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)"),
+                ],
+                title="Trashcan AI",
+                description="LLama3.1 hosted on a 2013 \"Trashcan\" Mac Pro with ollama",
+                theme="finlaymacklon/smooth_slate",
+                submit_btn="Send",
+                retry_btn="🔄 Regenerate Response",
+                undo_btn="↩ Delete Previous",
+                clear_btn="🗑️ Clear Chat"
+)
+
+chatbot.queue().launch()