Create app.py

app.py

@@ -0,0 +1,57 @@
+# app.py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import requests
+import gradio as gr
+
+# Load the Hugging Face model and tokenizer
+model_name = "SakanaAI/Llama-3-8B-Instruct-Coding-Expert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Groq API configuration
+GROQ_API_KEY = "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"
+GROQ_API_URL = "https://api.groq.com/v1/completions"
+
+# Function to query Groq API
+def query_groq(prompt):
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "prompt": prompt,
+        "max_tokens": 150
+    }
+    response = requests.post(GROQ_API_URL, headers=headers, json=data)
+    return response.json()["choices"][0]["text"]
+
+# Function to generate smart contract code
+def generate_smart_contract(language, requirements):
+    # Create a prompt for the model
+    prompt = f"Generate a {language} smart contract with the following requirements: {requirements}"
+    
+    # Use the Hugging Face model to generate code
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=500)
+    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Enhance the code using Groq API
+    enhanced_code = query_groq(generated_code)
+    
+    return enhanced_code
+
+# Gradio interface for the app
+def generate_contract(language, requirements):
+    return generate_smart_contract(language, requirements)
+
+interface = gr.Interface(
+    fn=generate_contract,
+    inputs=["text", "text"],
+    outputs="text",
+    title="Smart Contract Generator",
+    description="Generate smart contracts using AI."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()