Create app.py

app.py

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+"""
+LlamaIndex Agent for HuggingFace AI Agents Course - Unit 2
+Simple ReAct agent using LlamaIndex FunctionTool.
+"""
+
+import os
+import re
+import gradio as gr
+from llama_index.core.tools import FunctionTool
+
+# ============================================
+# TOOLS (using LlamaIndex FunctionTool)
+# ============================================
+
+def calculate(operation: str) -> str:
+    """Performs basic math calculations. Args: operation - A math expression like '2 + 2'"""
+    try:
+        allowed = set('0123456789+-*/(). ')
+        if all(c in allowed for c in operation):
+            return f"Result: {eval(operation)}"
+        return "Invalid operation"
+    except Exception as e:
+        return f"Error: {e}"
+
+def get_current_time() -> str:
+    """Gets the current date and time in UTC."""
+    from datetime import datetime
+    return f"Current time (UTC): {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')}"
+
+def get_weather(city: str) -> str:
+    """Gets weather for a city (mock data)."""
+    import random
+    conditions = ["sunny", "cloudy", "rainy", "windy"]
+    return f"Weather in {city}: {random.randint(15,30)}°C, {random.choice(conditions)}"
+
+def count_words(text: str) -> str:
+    """Counts words in text."""
+    return f"Word count: {len(text.split())}"
+
+# Create LlamaIndex FunctionTools
+calc_tool = FunctionTool.from_defaults(fn=calculate, name="calculate")
+time_tool = FunctionTool.from_defaults(fn=get_current_time, name="get_time")
+weather_tool = FunctionTool.from_defaults(fn=get_weather, name="get_weather")
+words_tool = FunctionTool.from_defaults(fn=count_words, name="count_words")
+
+TOOLS = {
+    "calculate": calculate,
+    "get_time": get_current_time,
+    "get_weather": get_weather,
+    "count_words": count_words,
+}
+
+# ============================================
+# AGENT (Simple ReAct with direct API call)
+# ============================================
+
+def call_hf_api(prompt: str) -> str:
+    """Call HuggingFace Inference API."""
+    import requests
+    
+    token = os.environ.get("HF_TOKEN")
+    
+    # Use the OpenAI-compatible endpoint
+    response = requests.post(
+        "https://router.huggingface.co/novita/v3/openai/chat/completions",
+        headers={
+            "Authorization": f"Bearer {token}",
+            "Content-Type": "application/json"
+        },
+        json={
+            "model": "meta-llama/llama-3.1-8b-instruct",
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": 500,
+            "temperature": 0.7
+        },
+        timeout=60
+    )
+    
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        raise Exception(f"API Error {response.status_code}: {response.text}")
+
+
+def run_agent(query: str) -> str:
+    """Run the ReAct agent loop."""
+    
+    tools_desc = """Available tools:
+- calculate: Do math (e.g., "2+2", "10*5")
+- get_time: Get current UTC time
+- get_weather: Get weather for a city
+- count_words: Count words in text"""
+
+    prompt = f"""{tools_desc}
+
+To use a tool: Action: <name> | Input: <value>
+For final answer: Answer: <response>
+
+Query: {query}
+Response:"""
+
+    for _ in range(3):
+        try:
+            response = call_hf_api(prompt)
+            
+            # Check for final answer
+            if "Answer:" in response:
+                return response.split("Answer:")[-1].strip()
+            
+            # Check for tool use
+            action = re.search(r"Action:\s*(\w+)", response)
+            inp = re.search(r"Input:\s*(.+?)(?:\n|$)", response)
+            
+            if action:
+                tool_name = action.group(1).lower()
+                tool_input = inp.group(1).strip() if inp else ""
+                
+                # Find and call tool
+                for name, func in TOOLS.items():
+                    if tool_name in name.lower():
+                        if name == "get_time":
+                            result = func()
+                        else:
+                            result = func(tool_input)
+                        
+                        prompt += f"\n{response}\nObservation: {result}\nResponse:"
+                        break
+                else:
+                    return response
+            else:
+                return response
+                
+        except Exception as e:
+            return f"Error: {str(e)}"
+    
+    return "Could not complete request."
+
+
+# ============================================
+# GRADIO UI
+# ============================================
+
+def chat(message, history):
+    if not message.strip():
+        return "Please ask something!"
+    return run_agent(message)
+
+with gr.Blocks(title="LlamaIndex Agent") as demo:
+    gr.Markdown("""
+# 🦙 LlamaIndex ReAct Agent - Unit 2
+
+Built with **LlamaIndex FunctionTool** for the HuggingFace AI Agents Course.
+
+**Tools:** Calculator | Time | Weather | Word Count
+---
+""")
+    
+    gr.ChatInterface(
+        fn=chat,
+        examples=[
+            "What is 25 * 4 + 10?",
+            "What time is it?",
+            "Weather in Tokyo?",
+            "Count words: hello world test",
+        ],
+    )
+    
+    gr.Markdown("---\n*Unit 2: LlamaIndex Framework*")
+
+if __name__ == "__main__":
+    demo.launch()