Update app.py

app.py

@@ -64,13 +64,10 @@ class CodeLlamaTool(Tool):
 
     def forward(self, question: str) -> str:
         prompt = f"""You are an AI that uses Python code to answer questions.
-
 Question: {question}
-
 Instructions:
 - If solving requires code, use a block like <tool>code</tool>.
 - Always end with <final>FINAL ANSWER</final> containing the final number or string.
-
 Example:
 Question: What is 5 * sqrt(36)?
 Answer:
@@ -79,7 +76,6 @@ import math
 print(5 * math.sqrt(36))
 </tool>
 <final>30.0</final>
-
 Answer:"""
 
         response = self.pipeline(prompt)[0]["generated_text"]
@@ -112,152 +108,6 @@ Answer:"""
         except Exception:
             return f"Error executing code:\n{traceback.format_exc()}"
 
-import requests
-from smolagents import Tool
-
-class ArxivSearchTool(Tool):
-    name = "arxiv_search"
-    description = "Search Arxiv for papers matching a query and return titles and links."
-    inputs = {
-        "query": {"type": "string", "description": "Search query for Arxiv papers"}
-    }
-    output_type = "string"
-
-    def forward(self, query: str) -> str:
-        url = "http://export.arxiv.org/api/query"
-        params = {
-            "search_query": query,
-            "start": 0,
-            "max_results": 3,
-            "sortBy": "relevance",
-            "sortOrder": "descending"
-        }
-        try:
-            response = requests.get(url, params=params, timeout=10)
-            response.raise_for_status()
-            # Simple parse titles and links (basic, for demo)
-            import xml.etree.ElementTree as ET
-            root = ET.fromstring(response.content)
-            ns = {"atom": "http://www.w3.org/2005/Atom"}
-
-            entries = root.findall("atom:entry", ns)
-            results = []
-            for entry in entries:
-                title = entry.find("atom:title", ns).text.strip().replace('\n', ' ')
-                link = entry.find("atom:id", ns).text.strip()
-                results.append(f"{title}\n{link}")
-            return "\n\n".join(results) if results else "No results found."
-        except Exception as e:
-            return f"Error during Arxiv search: {e}"
-
-
-
-from transformers import pipeline
-from smolagents import Tool
-
-class TranslationTool(Tool):
-    name = "translate_text"
-    description = "Translate text from one language to another."
-
-    inputs = {
-        "text": {"type": "string", "description": "Text to translate"},
-        "src_lang": {"type": "string", "description": "Source language code"},
-        "tgt_lang": {"type": "string", "description": "Target language code"},
-    }
-    output_type = "string"
-
-    def __init__(self):
-        self.translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
-
-    def forward(self, text: str, src_lang: str, tgt_lang: str) -> str:
-        return self.translator(text)[0]["translation_text"]
-
-
-
-from transformers import pipeline
-from smolagents import Tool
-from PIL import Image
-
-class HuggingFaceDocumentQATool(Tool):
-    name = "document_qa"
-    description = "Answer questions from document images (e.g., scanned invoices)."
-    inputs = {
-        "image_path": {"type": "string", "description": "Path to the image file"},
-        "question": {"type": "string", "description": "Question to ask about the document"}
-    }
-    output_type = "string"
-
-    def __init__(self):
-        self.pipeline = pipeline("document-question-answering", model="impira/layoutlm-document-qa")
-
-    def forward(self, image_path: str, question: str) -> str:
-        image = Image.open(image_path)
-        result = self.pipeline(image, question=question)
-        return result[0]['answer']
-
-
-from transformers import BlipProcessor, BlipForQuestionAnswering
-
-class HuggingFaceImageQATool(Tool):
-    name = "image_qa"
-    description = "Answer questions about an image."
-    inputs = {
-        "image_path": {"type": "string", "description": "Path to image"},
-        "question": {"type": "string", "description": "Question about the image"}
-    }
-    output_type = "string"
-
-    def __init__(self):
-        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
-        self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
-
-    def forward(self, image_path: str, question: str) -> str:
-        image = Image.open(image_path)
-        inputs = self.processor(image, question, return_tensors="pt")
-        out = self.model.generate(**inputs)
-        return self.processor.decode(out[0], skip_special_tokens=True)
-
-
-
-from transformers import pipeline
-
-class HuggingFaceTranslationTool(Tool):
-    name = "translate"
-    description = "Translate text from English to another language."
-    inputs = {
-        "text": {"type": "string", "description": "Text to translate"}
-    }
-    output_type = "string"
-
-    def __init__(self):
-        self.translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
-
-    def forward(self, text: str) -> str:
-        return self.translator(text)[0]["translation_text"]
-
-
-
-import io
-import contextlib
-
-class PythonCodeExecutionTool(Tool):
-    name = "run_python"
-    description = "Execute Python code and return result."
-    inputs = {
-        "code": {"type": "string", "description": "Python code to execute"}
-    }
-    output_type = "string"
-
-    def forward(self, code: str) -> str:
-        output = io.StringIO()
-        try:
-            with contextlib.redirect_stdout(output):
-                exec(code, {})
-            return output.getvalue().strip()
-        except Exception as e:
-            return f"Error: {str(e)}"
-
-
 
 
 #from smolagents import Tool
@@ -417,13 +267,12 @@ class VideoTranscriptionTool(Tool):
 class BasicAgent:
     def __init__(self):
         token = os.environ.get("HF_API_TOKEN")
-
         model = HfApiModel(
             temperature=0.1,
             token=token
         )
 
-        # Your other tools
+        # Existing tools
         search_tool = DuckDuckGoSearchTool()
         wiki_search_tool = WikiSearchTool()
         str_reverse_tool = StringReverseTool()
@@ -432,21 +281,20 @@ class BasicAgent:
         visit_webpage_tool = VisitWebpageTool()
         final_answer_tool = FinalAnswerTool()
         video_transcription_tool = VideoTranscriptionTool()
-        code_llama_tool = CodeLlamaTool()
-        arxiv_search_tool = ArxivSearchTool()
 
-        # ✅ Add Hugging Face default tools
-        doc_qa_tool = HuggingFaceDocumentQATool()
-        image_qa_tool = HuggingFaceImageQATool()
-        translation_tool = HuggingFaceTranslationTool()
-        python_tool = PythonCodeExecutionTool()
+        # ✅ New Llama Tool
+        code_llama_tool = CodeLlamaTool()
 
-        system_prompt = """You are my general AI assistant...
-Always return your final result in the format:
-"FINAL ANSWER: [your short answer here]"
+        system_prompt = f"""
+You are my general AI assistant. Your task is to answer the question I asked.
+First, provide an explanation of your reasoning, step by step, to arrive at the answer.
+Then, return your final answer in a single line, formatted as follows: "FINAL ANSWER: [YOUR FINAL ANSWER]".
+[YOUR FINAL ANSWER] should be a number, a string, or a comma-separated list of numbers and/or strings, depending on the question.
+If the answer is a number, do not use commas or units (e.g., $, %) unless specified.
+If the answer is a string, do not use articles or abbreviations (e.g., for cities), and write digits in plain text unless specified.
+If the answer is a comma-separated list, apply the above rules for each element based on whether it is a number or a string.
 """
 
-
         self.agent = CodeAgent(
             model=model,
             tools=[
@@ -454,9 +302,6 @@ Always return your final result in the format:
                 keywords_extract_tool, speech_to_text_tool,
                 visit_webpage_tool, final_answer_tool,
                 parse_excel_to_json, video_transcription_tool,
-                arxiv_search_tool,
-                doc_qa_tool, image_qa_tool,
-                translation_tool, python_tool,
                 code_llama_tool  # 🔧 Add here
             ],
             add_base_tools=True