Update app.py

app.py

@@ -1,26 +1,189 @@
 import gradio as gr
-from transformers import pipeline
+import os
+import sys
+import json 
+import requests
+import random
 
-# Load your model from Hugging Face (for text generation)
-model = pipeline('LLM', model='gpt2')  # Replace with the model you want
 
-# Function to generate code (or text) based on user input
-def generate_code(input_text):
+MODEL = "gpt-4o-mini"
+API_URL = os.getenv("API_URL")
+DISABLED = os.getenv("DISABLED") == 'True'
+OPENAI_API_KEYS = os.getenv("OPENAI_API_KEYS").split(',')
+print (API_URL)
+print (OPENAI_API_KEYS)
+NUM_THREADS = int(os.getenv("NUM_THREADS"))
+
+print (NUM_THREADS)
+
+def exception_handler(exception_type, exception, traceback):
+    print("%s: %s" % (exception_type.__name__, exception))
+sys.excepthook = exception_handler
+sys.tracebacklimit = 0
+    
+def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request):
+    payload = {
+        "model": MODEL,
+        "messages": [{"role": "user", "content": f"{inputs}"}],
+        "temperature": temperature,
+        "top_p": top_p,
+        "n" : 1,
+        "stream": True,
+        "presence_penalty":0,
+        "frequency_penalty":0,
+    }
+    OPENAI_API_KEY = random.choice(OPENAI_API_KEYS)
+    print (OPENAI_API_KEY)
+    headers_dict = {key.decode('utf-8'): value.decode('utf-8') for key, value in request.headers.raw}
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {OPENAI_API_KEY}",
+        "Headers": f"{headers_dict}"
+    }
+
+    # print(f"chat_counter - {chat_counter}")
+    if chat_counter != 0 :
+        messages = []
+        for i, data in enumerate(history):
+            if i % 2 == 0:
+                role = 'user'
+            else:
+                role = 'assistant'
+            message = {}
+            message["role"] = role
+            message["content"] = data
+            messages.append(message)
+        
+        message = {}
+        message["role"] = "user" 
+        message["content"] = inputs
+        messages.append(message)
+        payload = {
+            "model": MODEL,
+            "messages": messages,
+            "temperature" : temperature,
+            "top_p": top_p,
+            "n" : 1,
+            "stream": True,
+            "presence_penalty":0,
+            "frequency_penalty":0,
+        }
+
+    chat_counter += 1
+
+    history.append(inputs)
+    token_counter = 0 
+    partial_words = "" 
+    counter = 0
+
     try:
-        # Generate the output text based on input
-        output = model(input_text, max_length=150, num_return_sequences=1)[0]['generated_text']
-        return output
+        # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+        response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+        response_code = f"{response}"
+        #if response_code.strip() != "<Response [200]>":
+        #    #print(f"response code - {response}")
+        #    raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}")
+        
+        for chunk in response.iter_lines():
+            #print (chunk)
+            #sys.stdout.flush()
+            #Skipping first chunk
+            if counter == 0:
+                counter += 1
+                continue
+                #counter+=1
+            # check whether each line is non-empty
+            if chunk.decode() :
+                chunk = chunk.decode()
+                # decode each line as response data is in bytes
+                if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
+                    partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
+                    if token_counter == 0:
+                        history.append(" " + partial_words)
+                    else:
+                        history[-1] = partial_words
+                    token_counter += 1
+                    yield [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False)  # resembles {chatbot: chat, state: history}  
     except Exception as e:
-        return f"Error: {str(e)}"
-
-# Gradio interface
-iface = gr.Interface(
-    fn=generate_code,  # Function to call
-    inputs="text",  # Text input field for user to enter requests
-    outputs="text",  # Text output for generated code or text
-    title="HandyAI - Your All Purpose AI",
-    description="HandyAI can generate code or text based on your input request."
-)
-
-# Launch the interface
-iface.launch()
+        print (f'error found: {e}')
+    yield [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True)
+    print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
+                   
+
+def reset_textbox():
+    return gr.update(value='', interactive=False), gr.update(interactive=False)
+
+title = """<h1 align="center">GPT-4o Mini: Research Preview (Short-Term Availability)</h1>"""
+if DISABLED:
+    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. Please check back tomorrow.</h1>"""
+description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
+```
+User: <utterance>
+Assistant: <utterance>
+User: <utterance>
+Assistant: <utterance>
+...
+```
+In this app, you can explore the outputs of a gpt-4 turbo LLM.
+"""
+
+theme = gr.themes.Default(primary_hue="green")                
+
+with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}""",
+              theme=theme) as demo:
+    gr.HTML(title)
+    gr.HTML("""<h3 align="center">This app provides you full access to GPT-4o mini (128K token limit). You don't need any OPENAI API key.</h3>""")
+
+    #gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
+    with gr.Column(elem_id = "col_container", visible=False) as main_block:
+        #GPT4 API Key is provided by Huggingface 
+        #openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 OpenAI API key here")
+        chatbot = gr.Chatbot(elem_id='chatbot') #c
+        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
+        state = gr.State([]) #s
+        with gr.Row():
+            with gr.Column(scale=7):
+                b1 = gr.Button(visible=not DISABLED) #.style(full_width=True)
+            with gr.Column(scale=3):
+                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
+    
+        #inputs, top_p, temperature, top_k, repetition_penalty
+        with gr.Accordion("Parameters", open=False):
+            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
+            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
+            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
+            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
+            chat_counter = gr.Number(value=0, visible=False, precision=0)
+    
+    with gr.Column(elem_id = "user_consent_container") as user_consent_block:
+        # Get user consent
+        accept_checkbox = gr.Checkbox(visible=False)
+        js = "(x) => confirm('By clicking \"OK\", I agree that my data may be published or shared.')"
+        with gr.Accordion("User Consent for Data Collection, Use, and Sharing", open=True):
+            gr.HTML("""
+            <div>
+                <p>By using our app, which is powered by OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:</p>
+                <ol>
+                    <li><strong>Collection:</strong> We may collect information, including the inputs you type into our app, the outputs generated by OpenAI's API, and certain technical details about your device and connection (such as browser type, operating system, and IP address) provided by your device's request headers.</li>
+                    <li><strong>Use:</strong> We may use the collected data for research purposes, to improve our services, and to develop new products or services, including commercial applications, and for security purposes, such as protecting against unauthorized access and attacks.</li>
+                    <li><strong>Sharing and Publication:</strong> Your data, including the technical details collected from your device's request headers, may be published, shared with third parties, or used for analysis and reporting purposes.</li>
+                    <li><strong>Data Retention:</strong> We may retain your data, including the technical details collected from your device's request headers, for as long as necessary.</li>
+                </ol>
+                <p>By continuing to use our app, you provide your explicit consent to the collection, use, and potential sharing of your data as described above. If you do not agree with our data collection, use, and sharing practices, please do not use our app.</p>
+            </div>
+            """)
+            accept_button = gr.Button("I Agree")
+
+        def enable_inputs():
+            return gr.update(visible=False), gr.update(visible=True)
+
+    accept_button.click(None, None, accept_checkbox, js=js, queue=False)
+    accept_checkbox.change(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block], queue=False)
+
+    inputs.submit(reset_textbox, [], [inputs, b1], queue=False)
+    inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
+    b1.click(reset_textbox, [], [inputs, b1], queue=False)
+    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
+             
+    demo.queue(max_size=10, default_concurrency_limit=NUM_THREADS, api_open=False).launch(share=False)