Update app.py

app.py

@@ -1,33 +1,48 @@
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
 import datetime
-import os
-import pytz
 import requests
+import pytz
 import yaml
+import os
+from tools.final_answer import FinalAnswerTool
+from Gradio_UI import GradioUI
 import fitz  # PyMuPDF
 from sentence_transformers import SentenceTransformer, util
 from transformers import pipeline
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
-from tools.final_answer import FinalAnswerTool
-from Gradio_UI import GradioUI  # Import your UI class
 
 # API Key for weather
 API_KEY = os.getenv("Weather_Token")
 
-# -------------------- TOOLS --------------------
-
+# -------------------- TOOL 1: Get Weather --------------------
 @tool
 def get_current_weather(place: str) -> str:
+    """
+    A tool that fetches the current weather of a particular place.
+
+    Args:
+        place (str): A string representing a valid place (e.g., 'London/Paris').
+
+    Returns:
+        str: Weather description including condition, temperature, humidity, and wind speed.
+    """
     api_key = API_KEY
     url = "https://api.openweathermap.org/data/2.5/weather"
-    params = {"q": place, "appid": api_key, "units": "metric"}
+    params = {
+        "q": place,
+        "appid": api_key,
+        "units": "metric"
+    }
+
     try:
         response = requests.get(url, params=params)
         data = response.json()
+
         if response.status_code == 200:
             weather_desc = data["weather"][0]["description"]
             temperature = data["main"]["temp"]
             humidity = data["main"]["humidity"]
             wind_speed = data["wind"]["speed"]
+
             return (
                 f"Weather in {place}:\n"
                 f"- Condition: {weather_desc}\n"
@@ -41,8 +56,18 @@ def get_current_weather(place: str) -> str:
         return f"Error fetching weather data for '{place}': {str(e)}"
 
 
+# -------------------- TOOL 2: Get Time --------------------
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
+    """
+    A tool that fetches the current local time in a specified timezone.
+
+    Args:
+        timezone (str): A string representing a valid timezone (e.g., 'America/New_York').
+
+    Returns:
+        str: The current local time formatted as a string.
+    """
     try:
         tz = pytz.timezone(timezone)
         local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
@@ -51,34 +76,52 @@ def get_current_time_in_timezone(timezone: str) -> str:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
 
+# -------------------- TOOL 3: Document QnA --------------------
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
 
 @tool
 def document_qna_tool(pdf_path: str, question: str) -> str:
+    """
+    A tool for answering questions based on the content of a PDF document.
+
+    Args:
+        pdf_path (str): Path to the local PDF file.
+        question (str): A natural language question to ask about the PDF content.
+
+    Returns:
+        str: Answer to the question based on the PDF's content.
+    """
     try:
+        # Step 1: Extract text from PDF
         doc = fitz.open(pdf_path)
-        text_chunks = [page.get_text() for page in doc if page.get_text().strip()]
+        text_chunks = []
+        for page in doc:
+            text = page.get_text()
+            if text.strip():
+                text_chunks.append(text)
         doc.close()
 
         if not text_chunks:
             return "No text found in the PDF."
 
+        # Step 2: Semantic search
         embeddings = embedding_model.encode(text_chunks, convert_to_tensor=True)
         question_embedding = embedding_model.encode(question, convert_to_tensor=True)
         scores = util.pytorch_cos_sim(question_embedding, embeddings)[0]
         best_match_idx = scores.argmax()
         best_context = text_chunks[best_match_idx]
 
+        # Step 3: Answer question
         prompt = f"Context: {best_context}\nQuestion: {question}"
         answer = qa_pipeline(prompt, max_new_tokens=100)[0]['generated_text']
         return f"Answer: {answer.strip()}"
+
     except Exception as e:
         return f"Error processing document QnA: {str(e)}"
 
 
-# -------------------- SETUP AGENT --------------------
-
+# -------------------- Other Components --------------------
 final_answer = FinalAnswerTool()
 search_tool = DuckDuckGoSearchTool()
 
@@ -101,20 +144,16 @@ agent = CodeAgent(
         get_current_weather,
         image_generation_tool,
         search_tool,
-        document_qna_tool,
+        document_qna_tool,  # ← New Tool Added
         final_answer
     ],
     max_steps=6,
     verbosity_level=1,
+    grammar=None,
+    planning_interval=None,
+    name=None,
+    description=None,
     prompt_templates=prompt_templates
 )
 
-# -------------------- LAUNCH UI --------------------
-
-# Optional: define an upload folder if you want file uploads saved
-upload_folder = "uploaded_files"
-if not os.path.exists(upload_folder):
-    os.makedirs(upload_folder)
-
-ui = GradioUI(agent=agent, file_upload_folder=upload_folder)
-ui.launch(share=True)
+GradioUI(agent).launch()