app.py

import json
import os
import gradio as gr
from huggingface_hub import InferenceClient

# Function to call the Llama 3.1 8B model through Hugging Face API
def call_llama_model(user_query):
    # Initialize the inference client with access token
    client = InferenceClient(
        "meta-llama/Meta-Llama-3.1-8B-Instruct",
        token=os.environ.get("HF_TOKEN")
    )
    
    # Define the addition function schema
    function_schema = {
        "name": "add_numbers",
        "description": "Add two numbers together",
        "parameters": {
            "type": "object",
            "properties": {
                "num1": {
                    "type": "number",
                    "description": "First number to add"
                },
                "num2": {
                    "type": "number",
                    "description": "Second number to add"
                }
            },
            "required": ["num1", "num2"]
        }
    }
    
    # Create the system prompt with function definition
    system_prompt = f"""You have access to the following function:
{json.dumps(function_schema, indent=2)}

When given a query about adding numbers, you must:
1. Extract the two numbers from the query
2. Call the add_numbers function with these numbers
3. Respond ONLY with a valid JSON object in this exact format:
{{"function_call": {{"name": "add_numbers", "parameters": {{"num1": [first number], "num2": [second number]}}}}}}

DO NOT include any explanation, just the JSON.
"""
    
    # Call the model with the appropriate format for Llama 3.1
    response = client.text_generation(
        prompt=f"<|system|>\n{system_prompt}\n<|user|>\n{user_query}\n<|assistant|>",
        max_new_tokens=256,
        temperature=0.1,
        return_full_text=False
    )
    
    return response

# Function to parse the model response and calculate the result
def process_addition(query):
    try:
        # Get model response
        model_response = call_llama_model(query)
        
        # Create a debug output with the raw response
        debug_info = f"Raw model response:\n{model_response}\n\n"
        
        # Try to parse the JSON response
        try:
            # Find the JSON part in the response (it might have additional text)
            json_start = model_response.find('{')
            json_end = model_response.rfind('}') + 1
            
            if json_start >= 0 and json_end > json_start:
                json_str = model_response[json_start:json_end]
                response_data = json.loads(json_str)
                debug_info += f"Parsed JSON:\n{json.dumps(response_data, indent=2)}\n\n"
            else:
                return f"Error: No valid JSON found in response.\n\n{debug_info}"
            
            # Check if it has a function call
            if "function_call" in response_data:
                function_name = response_data["function_call"]["name"]
                params = response_data["function_call"]["parameters"]
                
                if function_name == "add_numbers":
                    # Extract the numbers
                    num1 = float(params["num1"])
                    num2 = float(params["num2"])
                    result = num1 + num2
                    
                    # Return a formatted response
                    return f"""
### Input Processed Successfully

**Numbers extracted:** {num1} and {num2}

**Function called:** `{function_name}`

**Result:** {result}

**JSON Function Call:**
```json
{json.dumps(response_data, indent=2)}