Initial commit

README.md

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+---
+title: Gpt2 Chat Base
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.42.0
+app_file: app.py
+pinned: false
+hf_oauth: true
+hf_oauth_scopes:
+- inference-api
+license: agpl-3.0
+short_description: GPT-2 Trained on other AI models and WhatsApp chats
+---
+
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load your custom model
+model_path = "alexdev404/gpt2-finetuned-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+def generate_response(prompt, system_message, conversation_history=None, max_tokens=75, temperature=0.78, top_p=0.85):
+    """Generate using your custom training format"""
+    
+    # Build context using your NEW format
+    context = ""
+    if conversation_history:
+        # Last 2-3 exchanges
+        # Use more conversation history to fill GPT-2's context window (1024 tokens)
+        # Estimate ~20-30 tokens per exchange, so we can fit ~30-40 exchanges
+        recent = conversation_history[-30:] if len(conversation_history) > 30 else conversation_history
+        is_first_message = False
+        for i, message in enumerate(recent):
+            if i == 0:
+                is_first_message = True
+                context += f"<|start|>User:<|message|>{system_message}<|end|>\n<|start|>Assistant:<|message|>Hey, what's up nice to meet you. I'm glad to be here!<|end|>\n"
+            if message['role'] == 'user':
+                context += f"<|start|>User:<|message|>{message['content']}<|end|>\n"
+            else:
+                context += f"<|start|>Assistant:<|message|>{message['content']}<|end|>\n"
+
+    # Format input to match training
+    # formatted_input = None
+    # if is_first_message:
+    #     formatted_input = f"{context}<|start|>User:<|message|>{prompt}<|end|>\n<|start|>Assistant:<|message|>"
+    # else:
+    formatted_input = f"{context}<|start|>User:<|message|>{prompt}<|end|>\n<|start|>Assistant:<|message|>"
+
+    # Debug: Print the formatted input
+    print(f"Formatted input: {repr(formatted_input)}")
+
+    inputs = tokenizer(
+        formatted_input, 
+        return_tensors="pt", 
+        padding=True, 
+        truncation=True,
+        max_length=512
+    )
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids,
+            attention_mask=inputs.attention_mask,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+            repetition_penalty=1,
+            eos_token_id=tokenizer.encode("<|end|>", add_special_tokens=False)[0]
+        )
+
+    # Decode only new tokens
+    new_tokens = outputs[0][inputs.input_ids.shape[-1]:]
+    response = tokenizer.decode(new_tokens, skip_special_tokens=False)
+    
+    return response.strip()
+
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    """
+    Modified to use your custom GPT-2 model instead of Hugging Face Inference API
+    """
+    # Convert gradio history format to your format
+    # Gradio history is already in the correct format: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
+    conversation_history = history  # Use history directly
+    
+    # Debug: Print the formatted input to see what's being sent to the model
+    print(f"User message: {message}")
+    print(f"History length: {len(conversation_history)}")
+    
+    # Generate response using your model
+    response = generate_response(
+        message, 
+        system_message,
+        conversation_history, 
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p
+    )
+    
+    # print(f"Raw response: {repr(response)}")
+    
+    # Clean up the response
+    if "<|end|>" in response:
+        response = response.split("<|end|>")[0]
+    
+    # Remove any remaining special tokens
+    # response = response.replace("<|start|>", "")
+    # response = response.replace("<|message|>", "")
+    # response = response.replace("User:", "")
+    # response = response.replace("Assistant:", "")
+    
+    # print(f"Cleaned response: {repr(response)}")
+    
+    return response.strip()
+
+
+"""
+Gradio ChatInterface for your custom GPT-2 model
+"""
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    title="Chat with the model",
+    description="Chat with the GPT-2-based model trained on WhatsApp data",
+    additional_inputs=[
+        gr.Textbox(value="Hey I\'m Alice and you\'re Grace. You are having a casual peer-to-peer conversation with someone. Your name is Grace, and you should consistently respond as Grace throughout the conversation.\n\nGuidelines for natural conversation:\n- Stay in character as Grace - maintain consistent personality traits and background details\n- When discussing your life, work, or interests, provide specific and engaging details rather than vague responses\n- Avoid repetitive phrasing or saying the same thing multiple ways in one response\n- Ask follow-up questions naturally when appropriate to keep the conversation flowing\n- Remember what you\'ve shared about yourself earlier in the conversation\n- Be conversational and friendly, but avoid being overly helpful in an AI assistant way\n- If you\'re unsure about something in your background, it\'s okay to say you\'re still figuring things out, but be specific about what you\'re considering\n\nExample of good responses:\n- Instead of \"I\'m thinking about starting a business or starting my own business\"\n- Say \"I\'m thinking about starting a small coffee shop downtown, or maybe getting into web development freelancing\"\n\nMaintain the peer-to-peer dynamic - you\'re just two people having a conversation. The user has entered the chat. Introduce yourself.", label="System message"),
+        gr.Slider(minimum=10, maximum=150, value=75, step=5, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=1.2, value=0.8, step=0.01, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.84,
+            step=0.01,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    chatbot.render()
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",  # Makes it accessible from other devices on your network
+        server_port=7860,       # Default gradio port
+        share=False,            # Set to True to get a public shareable link
+        debug=True
+    )

requirements.txt

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+transformers
+torch