Update app.py

app.py

@@ -74,7 +74,6 @@ class BasicAgent:
             inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
             outputs = self.model.generate(**inputs, max_new_tokens=100)
             answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-            # Extract concise answer
             answer = answer.strip().split("\n")[0].strip()
             print(f"Reasoning: Qwen returned answer: {answer}")
             return answer
@@ -89,7 +88,7 @@ class BasicAgent:
             response.raise_for_status()
             soup = self.BeautifulSoup(response.text, 'html.parser')
             context = soup.find("div", id="content").text if soup.find("div", id="content") else response.text
-            context = context[:4000]  # Limit context size
+            context = context[:4000]
             print(f"Reasoning: Web context fetched (first 100 chars): {context[:100]}...")
             prompt = f"Answer the following question based on the context:\nQuestion: {question}\nContext: {context}\nProvide only the final answer in the exact format required (e.g., number, comma-separated list, or name)."
             return self.query_qwen(prompt, question)
@@ -124,7 +123,7 @@ class BasicAgent:
             print("Reasoning: Transcriber unavailable.")
             return "Transcriber unavailable"
         except Exception as e:
-            print(f"Reasoning: Error processing audio: {e")
+            print(f"Reasoning: Error processing audio: {e}")
             return "Unable to process audio"
 
     def process_chess_image(self, image_path, question):
@@ -133,7 +132,6 @@ class BasicAgent:
             print(f"Reasoning: Chess image not found: {image_path}")
             return "Image not found"
         try:
-            # Placeholder FEN (requires vision model for actual conversion)
             fen = "rnbqkbnr/pppp1ppp/5n2/4p3/4P3/5N2/PPPP1PPP/RNBQKBNR w KQkq - 0 1"
             print(f"Reasoning: Using placeholder FEN: {fen}")
             if self.stockfish:
@@ -164,13 +162,12 @@ class BasicAgent:
     def process_table(self, table_text, question):
         print(f"Reasoning: Processing table data (first 100 chars): {table_text[:100]}...")
         try:
-            lines = table_text.split("\n")[1:]  # Skip header
+            lines = table_text.split("\n")[1:]
             table_data = []
             for line in lines:
                 if line.strip():
                     row = line.strip("|").split("|")[1:]
                     table_data.append(row)
-            # Assume 5x5 table (common in GAIA)
             df = self.pd.DataFrame(table_data, index=['a', 'b', 'c', 'd', 'e'], columns=['a', 'b', 'c', 'd', 'e'])
             print(f"Reasoning: Table parsed:\n{df.to_string()}")
             prompt = f"Analyze the following table to answer the question:\nQuestion: {question}\nTable:\n{df.to_string()}\nProvide only the final answer in the exact format required (e.g., comma-separated list)."
@@ -199,7 +196,6 @@ class BasicAgent:
         print(f"Full Question: {question}")
         question_lower = question.lower()
 
-        # Classify question and select tool
         if ".mp3" in question_lower:
             print("Reasoning: Detected audio question.")
             file_name = self.re.search(r'[\w\s]+\.mp3', question, self.re.IGNORECASE)