Update app.py

app.py

@@ -11,8 +11,6 @@ import pandas as pd
 import pdfplumber
 import gradio as gr
 import torch
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfgen import canvas
 
 # === Configuration ===
 persistent_dir = "/data/hf_cache"
@@ -61,21 +59,6 @@ def remove_duplicate_paragraphs(text: str) -> str:
             seen.add(clean_p)
     return "\n\n".join(unique_paragraphs)
 
-def save_report_as_pdf(text: str, path: str):
-    c = canvas.Canvas(path, pagesize=letter)
-    width, height = letter
-    y = height - 50
-
-    lines = text.split('\n')
-    for line in lines:
-        if y < 50:
-            c.showPage()
-            y = height - 50
-        c.drawString(50, y, line.strip())
-        y -= 15
-
-    c.save()
-
 def extract_text_from_excel(path: str) -> str:
     all_text = []
     xls = pd.ExcelFile(path)
@@ -205,17 +188,14 @@ def generate_final_summary(agent, combined: str) -> str:
     combined = remove_duplicate_paragraphs(combined)
     final_prompt = f"""
 You are an expert clinical summarizer. Analyze the following summaries carefully and generate a **single final concise structured medical report**, avoiding any repetition or redundancy.
-
 Summaries:
 {combined}
-
 Respond with:
 - Diagnostic Patterns
 - Medication Issues
 - Missed Opportunities
 - Inconsistencies
 - Follow-up Recommendations
-
 Avoid repeating the same points multiple times.
 """.strip()
 
@@ -266,11 +246,12 @@ def process_report(agent, file, messages: List[Dict[str, str]]) -> Tuple[List[Di
             return messages, None
 
         summary = generate_final_summary(agent, "\n\n".join(valid))
+        report_path = os.path.join(report_dir, f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md")
+        with open(report_path, 'w', encoding='utf-8') as f:
+            f.write(f"# 🧠 Final Medical Report\n\n{summary}")
 
-        report_path = os.path.join(report_dir, f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf")
-        save_report_as_pdf(summary, report_path)
-
-        messages.append({"role": "assistant", "content": "✅ Report generated successfully. Click below to download it."})
+        messages.append({"role": "assistant", "content": f"📊 Final Report:\n\n{summary}"})
+        messages.append({"role": "assistant", "content": f"✅ Report saved: {os.path.basename(report_path)}"})
         return messages, report_path
 
     except Exception as e:
@@ -293,7 +274,7 @@ def create_ui(agent):
             chatbot = gr.Chatbot(label="🧠 CPS Assistant", height=480, type="messages")
             upload = gr.File(label="📂 Upload Medical File", file_types=[".xlsx", ".csv", ".pdf"])
             analyze = gr.Button("🧠 Analyze")
-            download = gr.File(label="📥 Download Report", visible=False, interactive=True)
+            download = gr.File(label="📥 Download Report", visible=False, interactive=False)
 
         state = gr.State(value=[])
 
@@ -309,4 +290,4 @@ def create_ui(agent):
 if __name__ == "__main__":
     agent = init_agent()
     ui = create_ui(agent)
-    ui.launch(server_name="0.0.0.0", server_port=7860, allowed_paths=["/data/hf_cache/reports"], share=False)
+    ui.launch(server_name="0.0.0.0", server_port=7860, allowed_paths=["/data/hf_cache/reports"], share=False)