Update space

app.py

@@ -1,66 +1,67 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-# Load model directly
+# from transformers import pipeline # Or whatever library your model needs (e.g., torch, tensorflow)
 from transformers import AutoModel
-model = AutoModel.from_pretrained("mradermacher/DeepSeek-R1-Distill-Qwen-7B-Multilingual-i1-GGUF")
+import os # Useful for environment variables if needed
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 1. Load your Hugging Face model
+# Replace "your-model-id" with the actual ID of the model on Hugging Face Hub
+# Using pipeline is often the easiest way to start for common tasks
+try:
+    # Example: Sentiment Analysis model
+    # model = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+    model = AutoModel.from_pretrained("mradermacher/DeepSeek-R1-Distill-Qwen-7B-Multilingual-i1-GGUF")
+    # Or load specific model/tokenizer if pipeline isn't suitable:
+    # from transformers import AutoModel, AutoTokenizer
+    # tokenizer = AutoTokenizer.from_pretrained("your-model-id")
+    # model = AutoModel.from_pretrained("your-model-id")
+except Exception as e:
+     # Handle potential errors during model loading (e.g., network issues, model not found)
+     print(f"Error loading model: {e}")
+     model = None # Set model to None if loading fails
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# 2. Define the function that uses the model
+# This function takes the input from the Gradio interface
+# and returns the output that Gradio will display.
+def process_input_with_model(input_text):
+    if model is None:
+        return "Model could not be loaded. Please check logs."
 
-    messages.append({"role": "user", "content": message})
+    # Example using a pipeline:
+    result = model(input_text)
+    return result[0]['label'] # Adjust based on your model's output format
 
-    response = ""
+    # Example if you loaded model/tokenizer manually:
+    # inputs = tokenizer(input_text, return_tensors="pt")
+    # outputs = model(**inputs)
+    # Process outputs to get your desired result...
+    # return processed_result
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# 3. Define the Gradio Interface
+# Set up the input and output components and link the processing function
+if model is not None: # Only create the interface if the model loaded successfully
+    interface = gr.Interface(
+        fn=process_input_with_model,  # Your function
+        inputs=gr.Textbox(label="Enter text for analysis"), # Input component (adjust type as needed)
+        outputs=gr.Label(), # Output component (adjust type as needed)
+        title="My Hugging Face Model Test App",
+        description="Test out the sentiment analysis model."
+    )
+else:
+    # Create a simple interface indicating an error if the model failed to load
+     interface = gr.Interface(
+        fn=lambda x: "Application failed to load model.", # Simple function
+        inputs=gr.Textbox(label="Status"),
+        outputs=gr.Textbox(),
+        title="Application Error",
+        description="Failed to load the model. Check the logs for details."
+    )
 
 
+# 4. Launch the Gradio App
+# This is crucial for App Spaces to run your application.
 if __name__ == "__main__":
-    demo.launch()
+    # The listen='0.0.0.0' and share=False are often handled by the App Space environment
+    # but including them is harmless. App Spaces expose on port 7860 by default.
+    interface.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))

requirements.txt

@@ -1,2 +1,4 @@
 huggingface_hub==0.25.2
-transformers==4.12.5
+gradio
+transformers==4.12.5
+torch