Update app.py

app.py

@@ -4,7 +4,6 @@ import gradio as gr
 from dotenv import load_dotenv
 import io
 from PIL import Image
-import pytesseract  # Optional: Using Tesseract OCR to extract text from the image
 
 # Load environment variables (where your OpenAI key will be stored)
 load_dotenv()
@@ -12,31 +11,28 @@ load_dotenv()
 # Load the OpenAI API key from environment variables
 openai.api_key = os.getenv("OPENAI_API_KEY").strip()
 
-# Function to analyze the ad image by first extracting the text with pytesseract
+# Function to analyze the ad image (text analysis with GPT-4)
 def analyze_ad(image):
-    # Extract text from the image using Tesseract OCR
-    ad_copy = pytesseract.image_to_string(image)
+    # Convert the image to bytes (you can still keep this for future multimodal capabilities)
+    image_bytes = io.BytesIO()
+    image.save(image_bytes, format='PNG')
+    image_bytes = image_bytes.getvalue()
 
-    if not ad_copy.strip():  # If OCR doesn't extract text, return an error message
-        return "No text was detected in the image. Please upload a clearer ad image."
-
-    # Prompt for the marketing persona and scoring rubric
-    prompt = f"""
-    Analyze the following ad copy and generate a marketing persona. Then, provide a score (out of 10) for each of the following:
+    # Placeholder: Using a simple prompt for text-based analysis (future multimodal support)
+    prompt = """
+    This is an advertisement. Please generate a marketing persona based on this ad and provide scores (out of 10) on the following:
     
-    1. Relevance to Target Audience: Is the ad appealing to the intended demographic?
-    2. Emotional Engagement: Does the ad evoke the right emotional response?
-    3. Brand Consistency: Does the ad align with the brand’s voice and values?
-    4. Creativity: How unique or innovative is the ad's design and text approach?
-    5. Persuasiveness: Does the ad motivate action, such as clicking or purchasing?
-
-    Ad Copy: {ad_copy}
+    1. Relevance to Target Audience
+    2. Emotional Engagement
+    3. Brand Consistency
+    4. Creativity
+    5. Persuasiveness
     
-    Provide the persona description and the scores in table form with a final score.
+    Explain your reasoning for each score and provide a final persona description.
     """
 
     # Send the prompt to GPT-4-turbo for analysis
-    response = openai.chat_completions.create(
+    response = openai.ChatCompletion.create(
         model="gpt-4-turbo",
         messages=[
             {"role": "system", "content": "You are a marketing expert analyzing an advertisement."},
@@ -54,18 +50,17 @@ def analyze_ad(image):
 
 # Function to process the image and run the analysis
 def upload_and_analyze(image):
-    # Pass the uploaded image to the analyze_ad function
+    # Call the analyze_ad function to generate marketing insights
     result = analyze_ad(image)
-    
     return result
 
 # Gradio interface for Hugging Face deployment
 iface = gr.Interface(
     fn=upload_and_analyze,
-    inputs=gr.Image(type="pil", label="Upload Advertisement Image"),  # Use type="pil" for the image input
+    inputs=gr.Image(type="pil", label="Upload Advertisement Image"),  # Upload the ad image
     outputs=gr.Textbox(label="Marketing Persona and Ad Analysis"),
     title="Advertisement Persona and Scoring Analyzer",
-    description="Upload an advertisement image, and the app will generate marketing personas and evaluate the ad based on Relevance, Emotional Engagement, Brand Consistency, Creativity, and Persuasiveness."
+    description="Upload an advertisement image and get a marketing persona along with scores for Relevance, Emotional Engagement, Brand Consistency, Creativity, and Persuasiveness."
 )
 
 # Launch the app