Update app.py

app.py

@@ -7,19 +7,19 @@ from dotenv import load_dotenv
 import google.generativeai as genai
 import os
 
-from openai import OpenAI
-client = OpenAI(api_key="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")
-import base64
+# from openai import OpenAI
+# client = OpenAI(api_key="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")
+# import base64
 
-# Open the image file and encode it as a base64 string
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode("utf-8")
+# # Open the image file and encode it as a base64 string
+# def encode_image(image_path):
+#     with open(image_path, "rb") as image_file:
+#         return base64.b64encode(image_file.read()).decode("utf-8")
 
-# load_dotenv()
-# GOOGLE_API_KEY = os.getenv("GOOGLE_API")
-# genai.configure(api_key=GOOGLE_API_KEY)
-# model_vision = genai.GenerativeModel('gemini-pro-vision')
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("AIzaSyByqW3ByYPxC4xLS_NhgwAOAMMEgB7DvoY")
+genai.configure(api_key=GOOGLE_API_KEY)
+model_vision = genai.GenerativeModel('gemini-pro-vision')
 
 def gemini_response_vision(input_texts, image):
     try:
@@ -68,26 +68,26 @@ def shot(image, labels_text, model_name, hypothesis_template_prefix, hypothesis_
         domains = [domain.strip(" ") for domain in domains_text.strip(" ").split(",")]
     else:
         #img = Image.open(image)
-        #input_text = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information.
-        #domains = gemini_response_vision(input_texts=input_text, image=image)
+        input_text = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information.
+        domains = gemini_response_vision(input_texts=input_text, image=image)
         #IMAGE_PATH = './reasoning_xy.jpg'
-        base64_image = encode_image('car.png')
-        prompt = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information."
+        # base64_image = encode_image('car.png')
+        # prompt = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information."
         
-        response = client.chat.completions.create(
-            model="gpt-4o",
-            messages=[
-                # {"role": "system", "content": "You are a helpful assistant that responds in Markdown. Help me with my math homework!"},
-                {"role": "user", "content": [
-                    {"type": "text", "text": prompt},
-                    {"type": "image_url", "image_url": {
-                        "url": f"data:image/png;base64,{base64_image}"}
-                    }
-                ]}
-            ],
-            temperature=0.0,
-        )
-        domains = response.choices[0].message.content
+        # response = client.chat.completions.create(
+        #     model="gpt-4o",
+        #     messages=[
+        #         # {"role": "system", "content": "You are a helpful assistant that responds in Markdown. Help me with my math homework!"},
+        #         {"role": "user", "content": [
+        #             {"type": "text", "text": prompt},
+        #             {"type": "image_url", "image_url": {
+        #                 "url": f"data:image/png;base64,{base64_image}"}
+        #             }
+        #         ]}
+        #     ],
+        #     temperature=0.0,
+        # )
+        # domains = response.choices[0].message.content
         print(domains)
         
     hypothesis_template = hypothesis_template_prefix + ' ' + hypothesis_template_suffix.format(*domains)