Update app.py

app.py

@@ -16,14 +16,22 @@ API_KEY = os.getenv("Weather_Token")
 # -------------------- TOOL 1: Get Weather --------------------
 @tool
 def get_current_weather(place: str) -> str:
-    try:
-        url = "https://api.openweathermap.org/data/2.5/weather"
-        params = {
-            "q": place,
-            "appid": API_KEY,
-            "units": "metric"
-        }
+    """
+    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"
+    }
 
+    try:
         response = requests.get(url, params=params)
         data = response.json()
 
@@ -49,6 +57,13 @@ def get_current_weather(place: str) -> str:
 # -------------------- 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")
@@ -63,7 +78,19 @@ qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
 
 @tool
 def document_qna_tool(pdf_path: str, question: str) -> str:
-    import traceback
+    """
+    A tool that answers natural language questions about a given PDF document.
+    
+    Args:
+        pdf_path (str): Path to the local PDF file.
+        question (str): Question about the content of the PDF.
+    
+    Returns:
+        str: Answer to the question based on the content.
+    """
+    import os, fitz, traceback
+    from sentence_transformers import SentenceTransformer, util
+    from transformers import pipeline
 
     try:
         print(f"[DEBUG] PDF Path: {pdf_path}")
@@ -72,24 +99,43 @@ def document_qna_tool(pdf_path: str, question: str) -> str:
         if not os.path.exists(pdf_path):
             return f"[ERROR] File not found: {pdf_path}"
 
-        doc = fitz.open(pdf_path)
-        text_chunks = [page.get_text() for page in doc if page.get_text().strip()]
+        print("[DEBUG] Opening PDF...")
+        try:
+            doc = fitz.open(pdf_path)
+        except RuntimeError as e:
+            return f"[ERROR] Could not open PDF. It may be corrupted or encrypted. Details: {str(e)}"
+
+        text_chunks = []
+        for page in doc:
+            text = page.get_text()
+            if text.strip():
+                text_chunks.append(text)
         doc.close()
 
         if not text_chunks:
             return "[ERROR] No readable text in the PDF."
 
+        print(f"[DEBUG] Extracted {len(text_chunks)} text chunks.")
+        print(f"[DEBUG] First text chunk preview:\n{text_chunks[0][:300]}...")
+
         embeddings = embedding_model.encode(text_chunks, convert_to_tensor=True)
         question_embedding = embedding_model.encode(question, convert_to_tensor=True)
 
+        print("[DEBUG] Performing semantic search...")
         scores = util.pytorch_cos_sim(question_embedding, embeddings)[0]
+
+        print(f"[DEBUG] Similarity scores: {scores}")
+
         if scores.shape[0] == 0:
             return "[ERROR] No semantic matches found in PDF text."
 
         best_match_idx = scores.argmax().item()
         best_context = text_chunks[best_match_idx]
 
+        print(f"[DEBUG] Best context preview:\n{best_context[:300]}...")
+
         prompt = f"Context: {best_context}\nQuestion: {question}"
+        print("[DEBUG] Calling QA model...")
         answer = qa_pipeline(prompt, max_new_tokens=100)[0]['generated_text']
 
         return f"Answer: {answer.strip()}"
@@ -120,7 +166,7 @@ agent = CodeAgent(
         get_current_weather,
         image_generation_tool,
         search_tool,
-        document_qna_tool,
+        document_qna_tool,  # ← New Tool Added
         final_answer
     ],
     max_steps=6,
@@ -131,9 +177,8 @@ agent = CodeAgent(
     description=None,
     prompt_templates=prompt_templates
 )
-
 print("[DEBUG] Registered Tools:")
 for t in agent.tools:
     print(f" - {getattr(t, 'name', str(t))}")
 
-GradioUI(agent).launch()
+GradioUI(agent).launch()