Update app.py

app.py

@@ -18,6 +18,15 @@ API_KEY = os.getenv("Weather_Token")
 # -------------------- TOOL 1: Get Weather --------------------
 @tool
 def get_current_weather(place: str) -> str:
+    """
+    Get the current weather for a given location.
+
+    Args:
+        place (str): Name of the city or location (e.g., "London" or "New York").
+
+    Returns:
+        str: Weather condition, temperature, humidity, and wind speed.
+    """
     url = "https://api.openweathermap.org/data/2.5/weather"
     params = {
         "q": place,
@@ -43,6 +52,16 @@ def get_current_weather(place: str) -> str:
 # -------------------- TOOL 2: Get Time --------------------
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
+    """
+    Get the current local time in a given timezone.
+
+    Args:
+        timezone (str): Timezone string in the format 'Region/City', 
+                        e.g., "America/New_York".
+
+    Returns:
+        str: Formatted local time string.
+    """
     try:
         tz = pytz.timezone(timezone)
         local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
@@ -56,6 +75,16 @@ qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
 
 @tool
 def document_qna_tool(pdf_path: str, question: str) -> str:
+    """
+    Answer a natural language question based on the content of a PDF document.
+
+    Args:
+        pdf_path (str): Path to the local PDF file.
+        question (str): Natural language question to ask about the PDF.
+
+    Returns:
+        str: Answer generated from the most relevant section of the document.
+    """
     try:
         if not os.path.exists(pdf_path):
             return f"[ERROR] File not found: {pdf_path}"
@@ -79,6 +108,15 @@ def document_qna_tool(pdf_path: str, question: str) -> str:
 # -------------------- TOOL 4: Local Image Generation --------------------
 @tool
 def image_generator(prompt: str) -> str:
+    """
+    Generate an image from a given text prompt using Stable Diffusion.
+
+    Args:
+        prompt (str): Description of the image to generate.
+
+    Returns:
+        str: Path to the saved generated image.
+    """
     try:
         device = "cuda" if torch.cuda.is_available() else "cpu"
         pipe = StableDiffusionPipeline.from_pretrained(
@@ -96,6 +134,9 @@ def image_generator(prompt: str) -> str:
 from smolagents import LocalModel
 
 class TransformersModel(LocalModel):
+    """
+    Local text generation model wrapper using Hugging Face Transformers.
+    """
     def __init__(self):
         self.pipeline = pipeline(
             "text-generation",
@@ -105,6 +146,16 @@ class TransformersModel(LocalModel):
         )
 
     def generate(self, prompt, **kwargs):
+        """
+        Generate text from a given prompt.
+
+        Args:
+            prompt (str): Input prompt for generation.
+            **kwargs: Additional parameters for the pipeline.
+
+        Returns:
+            str: Generated text output.
+        """
         result = self.pipeline(prompt, max_new_tokens=500, do_sample=True)
         return result[0]['generated_text']