Update app.py

app.py

@@ -101,100 +101,6 @@ class SmartResearchAgent:
             logging.error(f"Image processing error: {e}")
             return "Unable to process the image."
 
-    def __call__(self, question: str, image: Image.Image = None) -> str:
-        logging.info(f"Received question: {question}")
-        q_lower = question.lower().strip()
-
-        try:
-            # 🔍 Handle image-based chess questions
-            if image is not None:
-                logging.info("Image input detected")
-                temp_path = "/tmp/input_image.jpg"
-                image.save(temp_path)
-                return self.caption_image(temp_path)
-
-            # 🔍 Handle text-based tasks
-            if q_lower.startswith("search:"):
-                query = question.split(":", 1)[1].strip()
-                return self.search_web(query)
-            elif q_lower.startswith("summarize:"):
-                target = question.split(":", 1)[1].strip()
-                return self.summarize(target)
-            elif q_lower.startswith("generate citation:") or q_lower.startswith("cite:"):
-                url = question.split(":", 1)[1].strip()
-                return self.generate_citation(url)
-            else:
-                # Default: search + summarize
-                search_result = self.search_web(question)
-                first_url = next((line.split(": ", 1)[-1] for line in search_result.splitlines() if "http" in line), None)
-                if first_url:
-                    summary = self.summarize(first_url)
-                    return f"{summary}\n\nSource: {first_url}"
-                else:
-                    return "Sorry, I couldn't find relevant information."
-        except Exception as e:
-            logging.exception("Unhandled error in agent call")
-            return f"Agent error: {e}"class SmartResearchAgent:
-    def __init__(self):
-        logging.info("Initializing SmartResearchAgent")
-        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-        self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-        self.blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-    def search_web(self, query: str) -> str:
-        try:
-            with DDGS() as ddgs:
-                results = ddgs.text(query, max_results=3)
-                top = [f"{i+1}. {r['title']}: {r['href']}" for i, r in enumerate(results)]
-                return "\n".join(top) if top else "No results found."
-        except Exception as e:
-            logging.error(f"Search error: {e}")
-            return "Error during web search."
-
-    def summarize(self, input_text_or_url: str) -> str:
-        try:
-            if input_text_or_url.startswith("http"):
-                article = Article(input_text_or_url)
-                article.download()
-                article.parse()
-                input_text_or_url = article.text
-            if not input_text_or_url.strip():
-                return "No content to summarize."
-            summary = self.summarizer(input_text_or_url, max_length=160, min_length=40, do_sample=False)
-            return summary[0]['summary_text'].strip()
-        except Exception as e:
-            logging.error(f"Summarization error: {e}")
-            return "Error during summarization."
-
-    def generate_citation(self, url: str) -> str:
-        try:
-            citation_id = hashlib.md5(url.encode()).hexdigest()[:6]
-            year = datetime.datetime.now().year
-            citation = (
-                f"@article{{cite{citation_id},\n"
-                f"  title={{Generated Reference}},\n"
-                f"  author={{Unknown}},\n"
-                f"  journal={{Online}},\n"
-                f"  year={{ {year} }},\n"
-                f"  url={{ {url} }}\n"
-                f"}}"
-            )
-            return citation
-        except Exception as e:
-            logging.error(f"Citation error: {e}")
-            return "Error during citation generation."
-
-    def caption_image(self, image_path: str) -> str:
-        try:
-            image = Image.open(image_path).convert("RGB")
-            inputs = self.blip_processor(image, return_tensors="pt")
-            out = self.blip_model.generate(**inputs)
-            caption = self.blip_processor.decode(out[0], skip_special_tokens=True)
-            return f"Image analysis: {caption}"
-        except Exception as e:
-            logging.error(f"Image processing error: {e}")
-            return "Unable to process the image."
-
     def __call__(self, question: str, image: Image.Image = None) -> str:
         logging.info(f"Received question: {question}")
         q_lower = question.lower().strip()
@@ -229,9 +135,9 @@ class SmartResearchAgent:
         except Exception as e:
             logging.exception("Unhandled error in agent call")
             return f"Agent error: {e}"
-
             
 
+
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,