Create app.py

app.py

@@ -0,0 +1,81 @@
+# app.py
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import json
+import os
+
+# Load the model and tokenizer from Hugging Face
+model_name = "bigcode/starcoder"  # Use StarCoder for code-related tasks
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Ensure the model runs on CPU for Hugging Face Spaces free tier
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Cache to store recent prompts and responses with file-based persistence
+CACHE_FILE = "cache.json"
+cache = {}
+
+# Load cache from file if it exists
+if os.path.exists(CACHE_FILE):
+    with open(CACHE_FILE, "r") as f:
+        cache = json.load(f)
+
+def code_assistant(prompt, language):
+    # Input validation
+    if not prompt.strip():
+        return "Error: The input prompt cannot be empty. Please provide a coding question or code snippet."
+    if len(prompt) > 256:
+        return "Error: The input prompt is too long. Please limit it to 256 characters."
+
+    # Check if the prompt is in cache
+    cache_key = (prompt, language)
+    if str(cache_key) in cache:
+        return cache[str(cache_key)]
+
+    # Customize the prompt based on language
+    if language:
+        prompt = f"[{language}] {prompt}"  # Indicate the language for context
+    
+    # Tokenize the input
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    # Generate response with adjusted parameters for faster responses
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=128,        # Shortened max length for quicker response
+        temperature=0.1,       # Lower temperature for more focused output
+        top_p=0.8,             # Slightly reduced top_p for quicker sampling
+        do_sample=True
+    )
+    
+    # Decode the generated output
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Store the response in cache (limit cache size to 10 items)
+    if len(cache) >= 10:
+        cache.pop(next(iter(cache)))  # Remove the oldest item
+    cache[str(cache_key)] = generated_text
+
+    # Write the updated cache to file
+    with open(CACHE_FILE, "w") as f:
+        json.dump(cache, f)
+
+    return generated_text
+
+# Set up Gradio interface with a dropdown for programming language selection
+iface = gr.Interface(
+    fn=code_assistant,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Ask a coding question or paste your code here..."),
+        gr.Dropdown(choices=["Python", "JavaScript", "Java", "C++", "HTML", "CSS", "SQL", "Other"], label="Programming Language")
+    ],
+    outputs="text",
+    title="Code Assistant with StarCoder",
+    description="An AI code assistant to help you with coding queries, debugging, and code generation. Specify the programming language for more accurate responses."
+)
+
+# Launch the Gradio app
+iface.launch()