Update app.py

app.py

@@ -2,11 +2,15 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 import faiss
-
 import os
+import re
+import random
 from openai import OpenAI
+
+# Initialize OpenAI client securely (use Hugging Face secret)
 client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
 
+# Convert a CRM row into readable text for embeddings
 def row_to_text(row):
     return (
         f"Clinic: {row.get('Account_Name', 'N/A')}\n"
@@ -21,7 +25,7 @@ def row_to_text(row):
         f"Notes: {row.get('Account_Notes', 'N/A')}"
     )
 
-
+# Get embedding for a given text
 def get_embedding(text):
     response = client.embeddings.create(
         input=[text],
@@ -29,6 +33,7 @@ def get_embedding(text):
     )
     return response.data[0].embedding
 
+# Core function: generate contextual pitch
 def contextual_pitch_assistant(csv_file, query):
     df = pd.read_csv(csv_file.name)
     text_chunks = df.apply(row_to_text, axis=1).tolist()
@@ -47,10 +52,8 @@ def contextual_pitch_assistant(csv_file, query):
     Based on the following CRM insights and query, generate:
     1) A short HTML email pitch (subject + body) ready to send.
     2) A DALL·E 3 prompt for a matching header image.
-
     Query:
     {query}
-
     CRM data:
     {'---'.join(retrieved)}
     """
@@ -63,22 +66,11 @@ def contextual_pitch_assistant(csv_file, query):
 
     output_text = response.choices[0].message.content
 
-    import re
+    # Extract image prompt (for later)
     match = re.search(r"(Image prompt|DALL·E prompt)[:\-]\s*(.*)", output_text)
     image_prompt = match.group(2).strip() if match else "modern dental clinic interior with dentist and patient"
-    
-    #image = client.images.generate(
-    #    model="gpt-image-1",
-    #    prompt=image_prompt,
-    #    size="1024x1024"
-    #)
-    #image_url = image.data[0].url
-
-    # TEMP FIX — skip image generation until org verification is complete
-    import random
-    
-    # Generate or pick a random header image
-    import random
+
+    # TEMP FIX: use random pre-uploaded header image
     image_choices = [
         "https://huggingface.co/spaces/nmcamacho/RAGdemo/resolve/main/dental_header_1.png",
         "https://huggingface.co/spaces/nmcamacho/RAGdemo/resolve/main/dental_header_2.png",
@@ -86,11 +78,11 @@ def contextual_pitch_assistant(csv_file, query):
     ]
     image_url = random.choice(image_choices)
 
-    # Clean GPT output to remove leftover DALL·E prompt or Markdown artifacts
-        output_text = re.sub(r"(?is)dall[-·]e.*?```", "", output_text)  # remove DALL·E prompt lines
-        output_text = re.sub(r"###.*?HTML Email Pitch", "", output_text)  # remove markdown titles
-        output_text = re.sub(r"```html|```", "", output_text)  # remove code blocks
-        output_text = output_text.strip()
+    # Clean output text
+    output_text = re.sub(r"(?is)dall[-·]e.*?```", "", output_text)
+    output_text = re.sub(r"###.*?HTML Email Pitch", "", output_text)
+    output_text = re.sub(r"```html|```", "", output_text)
+    output_text = output_text.strip()
 
     html = f"""
     <div style='font-family:Arial,sans-serif;max-width:600px;margin:auto;padding:16px;background:#ffffff;
@@ -101,6 +93,7 @@ def contextual_pitch_assistant(csv_file, query):
     """
     return html
 
+# Build the Gradio app UI
 with gr.Blocks(title="Contextual Pitch Assistant for Dental Sales") as app:
     gr.Markdown(
         """
@@ -113,11 +106,7 @@ with gr.Blocks(title="Contextual Pitch Assistant for Dental Sales") as app:
     query = gr.Textbox(label="💬 Sales Query", placeholder="e.g. Which clinic is best for our imaging subscription?")
     run_btn = gr.Button("🚀 Generate Pitch")
     output = gr.HTML(label="✨ Email Pitch Preview", elem_id="output_html")
-    
-    run_btn.click(fn=contextual_pitch_assistant, inputs=[csv_file, query], outputs=output)
-
 
     run_btn.click(fn=contextual_pitch_assistant, inputs=[csv_file, query], outputs=output)
 
-
 app.launch()