Update app.py

app.py

@@ -18,9 +18,14 @@ def analyze_ad(image):
     image.save(image_bytes, format='PNG')
     image_bytes = image_bytes.getvalue()
 
+    # Placeholder for ad text extracted from the image (since GPT-4-turbo doesn't support image-to-text natively)
+    # In a real scenario, you could integrate OCR to extract text or pass the image to a vision model.
+    ad_text = "This is a placeholder for text extracted from the image."
+
     # Prompt for the marketing persona and scoring rubric
-    prompt = """
-    Analyze this advertisement image. Extract any text present in the image and generate a marketing persona.
+    prompt = f"""
+    Analyze this advertisement and generate a marketing persona. The text in the ad says:
+    '{ad_text}'
     Then, provide a score (out of 10) for each of the following:
 
     1. Relevance to Target Audience: Is the ad appealing to the intended demographic?
@@ -32,36 +37,19 @@ def analyze_ad(image):
     Provide the persona description and the scores in table form with a final score.
     """
 
-    # Send the image and prompt to GPT-4-turbo for analysis
-    response = client.chat.completions.create(
-        model="gpt-4-turbo-vision",  # Use the multimodal GPT-4-turbo model
+    # Send the prompt to GPT-4-turbo for analysis
+    response = openai.ChatCompletion.create(
+        model="gpt-4-turbo",  # Use GPT-4 turbo model
         messages=[
             {"role": "system", "content": "You are a marketing expert analyzing an advertisement."},
             {"role": "user", "content": prompt}
         ],
-        functions=[
-            {
-                "name": "analyze_image",
-                "description": "Analyze an image and generate marketing insights",
-                "parameters": {
-                    "type": "image",
-                    "properties": {
-                        "image": {
-                            "type": "string",
-                            "description": "The input advertisement image for analysis"
-                        }
-                    },
-                    "required": ["image"]
-                }
-            }
-        ],
-        function_call={"name": "analyze_image", "arguments": {"image": image_bytes}},  # Sending the image as input
         temperature=0.7,
         max_tokens=500
     )
 
     # Extract the response text from the API output
-    result = response.choices[0].message.content.strip()
+    result = response['choices'][0]['message']['content'].strip()
 
     # Return the result for display
     return result