Update app.py

app.py

@@ -93,7 +93,9 @@ class DetailedExplainer:
             "text2text-generation",
             model=explanation_model,
             tokenizer=explanation_model,
-            device=device
+            device=device,
+            max_length=500,
+            max_new_tokens=800
         )
 
     def extract_concepts(self, text: str) -> list:
@@ -109,14 +111,12 @@ class DetailedExplainer:
 
     def explain_concept(self, concept: str, context: str, min_accuracy: float = 0.50) -> str:
         prompt = (
-            f"Explain the concept '{concept}' in depth using the following context. "
-            f"Aim for at least {int(min_accuracy * 100)}% accuracy."
-            f"\nContext:\n{context}\n"
+            f"The following sentence from a PDF is given:
+\"{context}\"\n\n"
+            f"Now explain the concept '{concept}' mentioned above with at least {int(min_accuracy * 100)}% accuracy."
         )
         result = self.explainer(
             prompt,
-            max_new_tokens=800,
-            min_length=80,
             do_sample=False
         )
         return result[0]["generated_text"].strip()
@@ -125,15 +125,8 @@ class DetailedExplainer:
         concepts = self.extract_concepts(text)
         explanations = {}
         for concept in concepts:
-            try:
-                explanations[concept] = self.explain_concept(concept, context)
-            except Exception as e:
-                explanations[concept] = f"[Error explaining concept: {e}]"
-        return {
-            "answer_sentence": text.strip(),
-            "concepts": concepts,
-            "explanations": explanations
-        }
+            explanations[concept] = self.explain_concept(concept, context)
+        return {"concepts": concepts, "explanations": explanations}
 
 class AdvancedPDFAnalyzer:
     def __init__(self):
@@ -193,7 +186,7 @@ class AdvancedPDFAnalyzer:
                 add_special_tokens=True,
                 return_tensors="pt",
                 max_length=512,
-                truncation=True #iniatially only_second
+                truncation=True
             )
             inputs = {k: v.to(self.device) for k, v in inputs.items()}
             with torch.no_grad():
@@ -227,7 +220,6 @@ class AdvancedPDFAnalyzer:
             best_answer['answer'] = f"[Low Confidence] {best_answer['answer']}"
         return best_answer
 
-# Instantiate analyzer once
 analyzer = AdvancedPDFAnalyzer()
 documents = analyzer.extract_text_with_metadata("example.pdf")
 
@@ -238,25 +230,13 @@ def ask_question_gradio(question: str):
         result = analyzer.answer_question(question, documents)
         answer = result['answer']
         confidence = result['confidence']
-
-        explanation_data = result.get("explanations", {})
-        answer_sentence = explanation_data.get("answer_sentence", answer)
-        explanations = explanation_data.get("explanations", {})
-
-        markdown_output = f"\U0001F4CC **Answer:**\n{answer_sentence}\n\n"
-        markdown_output += f"\U0001F512 **Confidence:** {confidence:.2f}\n\n"
-        markdown_output += f"\U0001F4D8 **Concept Explanations:**\n"
-
-        if not explanations:
-            markdown_output += "\nNo key concepts found to explain."
-        else:
-            for concept, desc in explanations.items():
-                markdown_output += f"\n\U0001F539 **{concept}**\n{desc}\n"
-
-        return markdown_output
-
+        explanation = "\n\n".join(
+            f"🔹 {concept}: {desc}"
+            for concept, desc in result.get("explanations", {}).get("explanations", {}).items()
+        )
+        return f"📌 **Answer**: {answer}\n\n🔒 **Confidence**: {confidence:.2f}\n\n📘 **Explanations**:\n{explanation}"
     except Exception as e:
-        return f"\u274C Error: {str(e)}"
+        return f"❌ Error: {str(e)}"
 
 demo = gr.Interface(
     fn=ask_question_gradio,