Update app.py

app.py

@@ -1,70 +1,113 @@
+# app.py
+import os
+import tempfile
 import gradio as gr
-from huggingface_hub import InferenceClient
 
+from embedding_manager import EmbeddingManager
+from summarizer import PatientChartSummarizer
+from hedis_engine import HedisComplianceEngine
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+APP_TITLE = "ChartWise AI"
+DEFAULT_MEASURE_YEAR = 2024
 
-    messages = [{"role": "system", "content": system_message}]
+# --- Handlers ---
+def generate_patient_summary(pdf_file):
+    try:
+        if pdf_file is None:
+            return "⚠️ Please upload a PDF file first."
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+            tmp.write(pdf_file)
+            pdf_path = tmp.name
 
-    messages.extend(history)
+        manager = EmbeddingManager(pdf_path)
+        vectordb = manager.get_or_create_embeddings()
 
-    messages.append({"role": "user", "content": message})
+        summarizer = PatientChartSummarizer(vectordb)
+        result = summarizer.summarize_chart()
+        os.unlink(pdf_path)
+        return result
+    except Exception as e:
+        return f"❌ Error processing chart: {e}"
 
-    response = ""
+def generate_hedis_analysis(pdf_file, measure_name, measurement_year):
+    try:
+        if pdf_file is None:
+            return "⚠️ Please upload a PDF file first."
+        if not measure_name:
+            return "⚠️ Please enter a HEDIS measure code (e.g., COL, BCS, CCS, AAB)."
+        if not measurement_year:
+            return "⚠️ Please enter a measurement year."
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+            tmp.write(pdf_file)
+            pdf_path = tmp.name
 
-        response += token
-        yield response
+        manager = EmbeddingManager(pdf_path)
+        vectordb = manager.get_or_create_embeddings()
 
+        hedis = HedisComplianceEngine(vectordb, measure_name, int(measurement_year))
+        result = hedis.run()
+        os.unlink(pdf_path)
+        return result
+    except Exception as e:
+        return f"❌ Error processing HEDIS analysis: {e}"
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# --- Gradio Theme ---
+simple_theme = gr.themes.Soft(
+    primary_hue=gr.themes.colors.blue,
+    secondary_hue=gr.themes.colors.slate,
+    neutral_hue=gr.themes.colors.slate,
+).set(
+    button_primary_background_fill="#1e40af",
+    button_primary_background_fill_hover="#1d4ed8",
+    button_primary_text_color="white",
+    background_fill_primary="white",
+    background_fill_secondary="#f8fafc",
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# --- Interface ---
+with gr.Blocks(theme=simple_theme, title="ChartWise AI") as interface:
+    gr.HTML("<h1 style='text-align:center;color:#1e40af;'>🏥 ChartWise AI</h1>")
+    gr.HTML("<p style='text-align:center;color:#64748b;'>Patient Chart Analysis &amp; HEDIS Compliance</p>")
 
+    with gr.Row(equal_height=True):
+        # Left: Patient Summary
+        with gr.Column():
+            gr.HTML("<h3 style='color:#1e40af;'>📋 Patient Chart Analysis</h3>")
+            pdf_upload = gr.File(label="Upload Patient Chart (PDF)", file_types=[".pdf"], type="binary")
+            summary_btn = gr.Button("📊 Generate Patient Summary", variant="primary")
+            summary_output = gr.Markdown(
+                label="Patient Summary Results",
+                value="Upload a patient chart and click 'Generate Patient Summary' to see results here.",
+                height=400
+            )
 
+        # Right: HEDIS
+        with gr.Column():
+            gr.HTML("<h3 style='color:#1e40af;'>🎯 HEDIS Measure Analysis</h3>")
+            hedis_measure = gr.Textbox(
+                label="HEDIS Measure Code",
+                placeholder="e.g., COL, BCS, CCS, AAB",
+                info="Measure code per NCQA HEDIS"
+            )
+            measurement_year = gr.Number(
+                label="Measurement Year",
+                value=DEFAULT_MEASURE_YEAR,
+                precision=0,
+                info="e.g., 2024"
+            )
+            hedis_btn = gr.Button("🎯 Run HEDIS Analysis", variant="secondary")
+            hedis_output = gr.Markdown(
+                label="HEDIS Analysis Results",
+                value="Enter measure and year, then click 'Run HEDIS Analysis'.",
+                height=400
+            )
+
+    # Wire events
+    summary_btn.click(fn=generate_patient_summary, inputs=[pdf_upload], outputs=[summary_output])
+    hedis_btn.click(fn=generate_hedis_analysis, inputs=[pdf_upload, hedis_measure, measurement_year], outputs=[hedis_output])
+
+# Spaces auto-runs the script; these hints are fine, too:
 if __name__ == "__main__":
-    demo.launch()
+    # On Spaces, port/host are managed, but these envs are also supported by Gradio.  [oai_citation:6‡Gradio](https://www.gradio.app/guides/environment-variables?utm_source=chatgpt.com)
+    interface.queue().launch(server_name="0.0.0.0", server_port=int(os.environ.get("GRADIO_SERVER_PORT", "7860")))