Create app.py

app.py

@@ -0,0 +1,96 @@
+import os
+import json
+import gradio as gr
+from datetime import datetime
+from huggingface_hub import InferenceClient
+
+# Constants
+DEFAULT_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.1"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+LOG_FILE = "chat_log.json"
+
+def chat_with_model(system_prompt, user_message, max_tokens=500, model_id=""):
+    """
+    Generate a chat completion using the specified or default Mistral model.
+    Args:
+        system_prompt (str): The system prompt to set the context.
+        user_message (str): The user's input message.
+        max_tokens (int): Maximum number of tokens to generate.
+        model_id (str): The model ID to use. If empty, uses the default model.
+    Returns:
+        str: The model's response.
+    """
+    model_id = model_id.strip() if model_id else DEFAULT_MODEL_ID
+    client = InferenceClient(model_id, token=HF_TOKEN)
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_message}
+    ]
+    response = ""
+    try:
+        for message in client.chat_completion(
+            messages=messages,
+            max_tokens=max_tokens,
+            stream=True
+        ):
+            response += message.choices[0].delta.content or ""
+    except Exception as e:
+        response = f"Error: {str(e)}"
+    
+    # Log the input and output
+    log_chat(system_prompt, user_message, response, max_tokens, model_id)
+    
+    return response
+
+def log_chat(system_prompt, user_message, response, max_tokens, model_id):
+    """
+    Log the chat details to a JSON file.
+    """
+    log_entry = {
+        "timestamp": datetime.now().isoformat(),
+        "system_prompt": system_prompt,
+        "user_message": user_message,
+        "response": response,
+        "max_tokens": max_tokens,
+        "model_id": model_id
+    }
+    
+    try:
+        # Read existing log file if it exists
+        if os.path.exists(LOG_FILE):
+            with open(LOG_FILE, 'r') as f:
+                log_data = json.load(f)
+        else:
+            log_data = []
+        
+        # Append new entry
+        log_data.append(log_entry)
+        
+        # Write updated log back to file
+        with open(LOG_FILE, 'w') as f:
+            json.dump(log_data, f, indent=2)
+    except Exception as e:
+        print(f"Error logging chat: {str(e)}")
+
+def create_gradio_interface():
+    """Create and configure the Gradio interface."""
+    return gr.Interface(
+        fn=chat_with_model,
+        inputs=[
+            gr.Textbox(label="System Prompt", placeholder="Enter the system prompt here..."),
+            gr.Textbox(label="User Message", placeholder="Ask a question..."),
+            gr.Slider(minimum=50, maximum=1000, value=500, step=50, label="Max Tokens"),
+            gr.Textbox(label="Model ID", placeholder=f"Enter model ID (default: {DEFAULT_MODEL_ID})")
+        ],
+        outputs=gr.Textbox(label="Response"),
+        title="Mistral Chatbot",
+        description="Chat with Mistral model using your own system prompts and choose your model.",
+        examples=[
+            ["You are a helpful AI assistant.", "What is the capital of France?", 500, ""],
+            ["You are an expert in Python programming.", "Explain list comprehensions.", 500, ""]
+        ]
+    )
+
+if __name__ == "__main__":
+    iface = create_gradio_interface()
+    iface.launch(show_api=True, share=False, show_error=True)