Create app.py

app.py

@@ -0,0 +1,153 @@
+import os
+import subprocess
+repo_url = "https://huggingface.co/piyush2102020/veronica_model"
+if not os.path.exists("veronica_model"):
+    subprocess.run(["git", "clone", repo_url])
+
+import sys
+sys.path.append("veronica_model")  # Add the repo to the Python path
+
+import gradio as gr
+from transformers import GPT2Tokenizer
+from veronica import Veronica, VeronicaConfig
+import firebase_admin
+import json
+from firebase_admin import credentials, db
+import time
+from veronica import Veronica  # Import Veronica class
+
+
+
+# Load tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = Veronica(VeronicaConfig())
+model.eval()
+firebase_key_path = os.getenv("firebase_db_url")
+cred = json.load(os.getenv("firebase_credentials"))
+cred = credentials.Certificate(cred)
+firebase_app = firebase_admin.initialize_app(cred, {
+    'databaseURL': firebase_key_path
+})
+
+ref = db.reference('/rlhf')
+
+# Define the build function
+def build(question):
+    user_token = "###user ###"
+    bot_token = "###bot ###"
+    return f"{user_token}{question}{bot_token}".lower()
+
+# Chat function
+def veronica_chat(history=None, question=None,max_new_tokens=10):
+    global generated_answer1, generated_answer2
+    if history is None:
+        history = []
+
+    if len(history) == 0:
+        history.append({"role": "assistant", "content": "Hey, I am Veronica. How can I assist you today?"})
+    else:
+        history.append({"role": "user", "content": question})
+        processing_message = "<i>Processing your question...</i>"
+        history.append({"role": "assistant", "content": processing_message})
+        
+        # Generate responses
+        prompt = build(question)
+        responses, _ = model.generate(prompt, num_samples=2, max_new_tokens=max_new_tokens)
+        answers = []
+
+        for r in responses:
+            for item in [prompt, "###user ###", "###bot ###", "###", "###end", "end"]:
+                r = r.replace(item, "")
+            answers.append(r)
+
+        generated_answer1, generated_answer2 = answers
+
+        # Update history with generated responses
+        history[-1] = {
+            "role": "assistant",
+            "content": f"<div style='display: flex; justify-content: space-between; width: 100%;'>"
+                       f"<div style='flex: 1; padding: 10px; background-color: #f9f9f9;'>"
+                       f"<b>Response 1:</b><br>{generated_answer1}<br></div>"
+                       f"<div style='width: 4px; background-color: black;'></div>"
+                       f"<div style='flex: 1; padding: 10px; background-color: #f9f9f9;'>"
+                       f"<b>Response 2:</b><br>{generated_answer2}<br></div></div>"
+        }
+    return history
+
+# Clear textboxes
+def clear_textboxes(*args):
+    return "", ""
+
+# Feedback function
+def feedback(prompt="", flagged_answer="", decision="", human_response="", history=None):
+    global generated_answer1, generated_answer2
+    selected_answer = generated_answer1 if flagged_answer == "Response 1" else generated_answer2
+
+    # Prepare Firebase payload
+    payload = {
+        "prompt": prompt,
+        "selected_answer_content": selected_answer,
+        "decision": decision,
+        "human_response": human_response,
+        "time_stamp": time.time()
+    }
+    print(payload)
+    ref.push(payload)
+
+    # Update chat history with acknowledgment
+    if history is None:
+        history = []
+    history.append({"role": "assistant", "content": "Thanks for your feedback! How can I assist you further?"})
+    return history
+
+# Gradio Interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as interface:
+    interface.css = """
+    .gradio-container { margin: 0; padding: 0; }
+    .gradio-row, .gradio-column { margin: 0; padding: 0; }
+    .gradio-chatbot .message { font-size: 12px !important; }
+    .gradio-textbox textarea { font-size: 12px !important; }
+    """
+
+    gr.Markdown("# Veronica AI")
+    gr.Markdown("""
+    **Chat with Veronica**, an AI model that is currently in the **fine-tuning stage**. During this stage, Veronica is learning from user interactions and adapting to provide better, more context-aware answers.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## RLHF Instructions")
+            gr.Markdown("""
+            - Ask a question to see two responses.
+            - Select your preferred response and provide feedback if needed.
+            """)
+            with gr.Row():
+                slider=gr.Slider(minimum=10,maximum=500,label="Token Length")
+            drop_down = gr.Dropdown(
+                choices=VeronicaConfig().functions_list,
+                label="Select Function",
+                value=VeronicaConfig().functions_list[3]
+            )
+            answer_flag = gr.Dropdown(choices=["Response 1", "Response 2"], label="Flag Response", value="Response 1")
+            answer_input_human = gr.Textbox(placeholder="Write your answer here...", label="Human Answer")
+            submit_feedback = gr.Button("Submit Feedback")
+        with gr.Column(scale=8):
+            chatbot = gr.Chatbot(
+                label="Chat",
+                height=420,
+                type="messages",
+                value=[{"role": "assistant", "content": "Hey, I am Veronica. How can I assist you today?"}]
+            )
+            with gr.Row():
+                with gr.Column(scale=6):
+                    question_input = gr.Textbox(placeholder="Chat with Veronica...", label="")
+                with gr.Column():
+                    send_button = gr.Button("Send")
+
+    # Link components to functions
+    send_button.click(fn=veronica_chat, inputs=[chatbot, question_input,slider], outputs=[chatbot])
+    submit_feedback.click(fn=feedback, inputs=[question_input, answer_flag, drop_down, answer_input_human, chatbot], outputs=[chatbot])
+    submit_feedback.click(fn=clear_textboxes, inputs=[question_input, answer_input_human], outputs=[question_input, answer_input_human])
+
+# Launch interface
+interface.launch(share=True)