api error 4

.gitignore

@@ -0,0 +1,42 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Gradio
+flagged/
+
+# Model cache (optional - remove if you want to cache models)
+# .cache/

README.md

@@ -1,12 +1,51 @@
 ---
-title: Chatbot
-emoji: 💬
-colorFrom: yellow
+title: DialoGPT Chatbot
+emoji: 🤖
+colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.0.1
+sdk_version: 3.50.2
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-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).
+# DialoGPT Chatbot
+
+A conversational AI chatbot powered by Microsoft's DialoGPT-medium model, hosted on Hugging Face Spaces.
+
+## About
+
+This chatbot uses the `microsoft/DialoGPT-medium` model, a pre-trained conversational AI model that can engage in natural dialogue. The interface is built with Gradio for easy interaction.
+
+## Features
+
+- Natural conversation flow
+- Context-aware responses based on chat history
+- Clean and user-friendly interface
+- Example prompts to get started
+- Clear chat functionality
+
+## Usage
+
+Simply type your message in the text box and press Enter to chat with the bot. The conversation history is maintained throughout the session.
+
+## Technical Details
+
+- **Model**: microsoft/DialoGPT-medium
+- **Framework**: PyTorch + Transformers
+- **Interface**: Gradio 3.50.2
+- **Hosting**: Hugging Face Spaces (CPU)
+
+## Installation
+
+If you want to run this locally:
+
+```bash
+pip install torch==2.1.0 transformers==4.35.2 gradio==3.50.2
+python app.py
+```
+
+## License
+
+MIT License

app.py

@@ -1,59 +1,92 @@
-# app.py
-
 import gradio as gr
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-print("Loading DialoGPT-medium model...")
-
-# Load the tokenizer and model
+# Load model and tokenizer
+print("Loading DialoGPT-medium...")
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-print("Model loaded successfully!")
+# Add pad token if it doesn't exist
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
 
-# Define the prediction function
-def predict(message, history):
-    # Format the history for DialoGPT.
-    # It expects a flat string of alternating user/bot messages separated by an end-of-string token.
-    history_transformer_format = ""
-    for user_msg, bot_msg in history:
-        history_transformer_format += user_msg + tokenizer.eos_token
-        history_transformer_format += bot_msg + tokenizer.eos_token
-    
-    # Append the new user message
-    history_transformer_format += message + tokenizer.eos_token
+print("Model loaded successfully!")
 
-    # Tokenize the input
-    new_user_input_ids = tokenizer.encode(history_transformer_format, return_tensors='pt')
+def respond(message, history):
+    """Generate response for the chatbot"""
+    try:
+        # Build conversation history
+        conversation = ""
+        for user_msg, bot_msg in history:
+            conversation += f"{user_msg}{tokenizer.eos_token}{bot_msg}{tokenizer.eos_token}"
+        
+        # Add current message
+        conversation += f"{message}{tokenizer.eos_token}"
+        
+        # Tokenize
+        input_ids = tokenizer.encode(conversation, return_tensors="pt")
+        
+        # Limit input length to prevent memory issues
+        if input_ids.shape[1] > 800:
+            input_ids = input_ids[:, -800:]
+        
+        # Generate response
+        with torch.no_grad():
+            output = model.generate(
+                input_ids,
+                max_new_tokens=100,
+                do_sample=True,
+                top_p=0.9,
+                temperature=0.8,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                no_repeat_ngram_size=2
+            )
+        
+        # Decode response
+        response = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)
+        return response.strip() or "I'm not sure how to respond to that."
+        
+    except Exception as e:
+        print(f"Error: {e}")
+        return "Sorry, I encountered an error. Please try again."
 
-    # Generate a response
-    bot_output_ids = model.generate(
-        new_user_input_ids,
-        max_length=1250,
-        pad_token_id=tokenizer.eos_token_id,
-        no_repeat_ngram_size=3,
-        do_sample=True,
-        top_k=100,
-        top_p=0.7,
-        temperature=0.8
+# Create Gradio interface
+with gr.Blocks(title="DialoGPT Chatbot") as demo:
+    gr.Markdown("# 🤖 DialoGPT-medium Chatbot")
+    gr.Markdown("Chat with Microsoft's DialoGPT-medium model!")
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Type your message here...", container=False, scale=7)
+    clear = gr.Button("Clear Chat")
+    
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+    
+    def bot(history):
+        if history and history[-1][1] is None:
+            user_message = history[-1][0]
+            bot_response = respond(user_message, history[:-1])
+            history[-1][1] = bot_response
+        return history
+    
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
     )
-
-    # Decode the response, skipping the input part to avoid repetition
-    response = tokenizer.decode(bot_output_ids[:, new_user_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    clear.click(lambda: None, None, chatbot, queue=False)
     
-    return response
-
-# Build the Gradio interface using gr.ChatInterface
-# This component is simpler and automatically includes undo and clear buttons.
-demo = gr.ChatInterface(
-    fn=predict,
-    title="DialoGPT Chatbot 🤖",
-    description="Ask me anything! This chatbot is running the microsoft/DialoGPT-medium model.",
-    theme="soft",
-    examples=["Hello!", "How does a computer work?", "Tell me a joke about AI."]
-)
+    # Add example prompts
+    gr.Examples(
+        examples=[
+            "Hello, how are you?",
+            "What's your favorite movie?", 
+            "Tell me a joke",
+            "What do you think about AI?"
+        ],
+        inputs=msg
+    )
 
-# Launch the app. .queue() is recommended for handling multiple users.
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue()
+    demo.launch()

requirements.txt

@@ -1,3 +1,3 @@
-torch
-transformers
-gradio
+torch>=2.0.0,<2.2.0
+transformers>=4.30.0,<4.40.0
+gradio>=3.50.0,<4.0.0