:Revert

.gitattributes

@@ -1,35 +1,35 @@
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 ---
 title: Livecoder
 emoji: 💬
@@ -13,4 +14,22 @@ hf_oauth_scopes:
 short_description: livecoder app section
 ---
 
-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).
+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).
+=======
+---
+title: Livecoder
+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
+short_description: livecoder app section
+---
+
+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).
+>>>>>>> 3d4926b (Revert)

app.py

@@ -1,84 +1,84 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-# Use lighter model for CPU
-#model_name = "microsoft/phi-2"  # 2.7B - TOO HEAVY
-model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # 1.1B - much lighter
-
-try:
-    print(f"Loading {model_name}...")
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    tokenizer.pad_token = tokenizer.eos_token
-    
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype=torch.float32,
-        device_map="cpu",
-        low_cpu_mem_usage=True  # Critical for CPU
-    )
-    print("Model loaded successfully")
-    
-except Exception as e:
-    print(f"Failed to load model: {e}")
-    # Fallback to dummy function
-    model, tokenizer = None, None
-
-def generate_response(message):
-    """Process user input and generate response"""
-    if not message.strip():
-        return "Please enter a question."
-    
-    if model is None or tokenizer is None:
-        return f"Model not loaded. Testing UI with: {message}"
-    
-    try:
-        # Format for chat model
-        prompt = f"<|user|>\n{message}\n<|assistant|>\n"
-        
-        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=384)
-        
-        # Generate with lower token count for CPU
-        with torch.no_grad():
-            outputs = model.generate(
-                inputs.input_ids,
-                attention_mask=inputs.attention_mask,  # FIX: Add attention mask
-                max_new_tokens=600,  # Reduced for CPU
-                temperature=0.8,
-                do_sample=True,
-                top_p=0.9,
-                pad_token_id=tokenizer.pad_token_id,
-                eos_token_id=tokenizer.eos_token_id
-            )
-        
-        response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
-        return response.strip()
-    
-    except Exception as e:
-        return f"Error: {str(e)[:100]}"
-
-# Create interface
-interface = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(label="Input", placeholder="Enter programming question...", lines=3),
-    outputs=gr.Textbox(label="Output", lines=10),
-    title="LiveCoder API",
-    description="LLM programming assistant",
-    allow_flagging="never"
-)
-
-# API endpoint info
-USERNAME = "sarekuwa"
-SPACE_NAME = "livecoder"
-print(f"API Endpoint: https://{USERNAME}-{SPACE_NAME}.hf.space/api/predict")
-
-# CRITICAL: Enable queue for request processing
-interface.queue(default_concurrency_limit=1)
-
-# Launch application
-interface.launch(
-    server_name="0.0.0.0", 
-    server_port=7860, 
-    share=False,
-    debug=True
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Use lighter model for CPU
+#model_name = "microsoft/phi-2"  # 2.7B - TOO HEAVY
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # 1.1B - much lighter
+
+try:
+    print(f"Loading {model_name}...")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float32,
+        device_map="cpu",
+        low_cpu_mem_usage=True  # Critical for CPU
+    )
+    print("Model loaded successfully")
+    
+except Exception as e:
+    print(f"Failed to load model: {e}")
+    # Fallback to dummy function
+    model, tokenizer = None, None
+
+def generate_response(message):
+    """Process user input and generate response"""
+    if not message.strip():
+        return "Please enter a question."
+    
+    if model is None or tokenizer is None:
+        return f"Model not loaded. Testing UI with: {message}"
+    
+    try:
+        # Format for chat model
+        prompt = f"<|user|>\n{message}\n<|assistant|>\n"
+        
+        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=384)
+        
+        # Generate with lower token count for CPU
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs.input_ids,
+                attention_mask=inputs.attention_mask,  # FIX: Add attention mask
+                max_new_tokens=600,
+                temperature=0.8,
+                do_sample=True,
+                top_p=0.9,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id
+            )
+        
+        response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+        return response.strip()
+    
+    except Exception as e:
+        return f"Error: {str(e)[:100]}"
+
+# Create interface
+interface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Input", placeholder="Enter programming question...", lines=3),
+    outputs=gr.Textbox(label="Output", lines=10),
+    title="LiveCoder API",
+    description="LLM programming assistant",
+    allow_flagging="never"
+)
+
+# API endpoint info
+USERNAME = "sarekuwa"
+SPACE_NAME = "livecoder"
+print(f"API Endpoint: https://{USERNAME}-{SPACE_NAME}.hf.space/api/predict")
+
+# CRITICAL: Enable queue for request processing
+interface.queue(default_concurrency_limit=1)
+
+# Launch application
+interface.launch(
+    server_name="0.0.0.0", 
+    server_port=7860, 
+    share=False,
+    debug=True
 )

requirements.txt

@@ -1,4 +1,4 @@
-torch>=2.0.0
-transformers>=4.35.0
-gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.35.0
+gradio>=4.0.0
 accelerate