first commit

.gitignore

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+.env
+env
+/venv

app.py

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+# import gradio as gr
+from dotenv import load_dotenv
+import gradio as gr
+# Load environment variables from the .env file de forma local
+load_dotenv()
+import base64
+import requests
+
+with open("Iso_Logotipo_Ceibal.png", "rb") as image_file:
+    encoded_image = base64.b64encode(image_file.read()).decode()
+
+import os
+from openai import OpenAI
+
+client=OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+api_key=os.environ["OPENAI_API_KEY"]
+
+def respond2(image,text):
+    # with open('some_file.txt', 'w') as f:
+    #     f.write(processed_string)
+    # Function to encode the image
+    def encode_image(image_path):
+      with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+    # Path to your image
+   
+
+    # Getting the base64 string
+    base64_image = encode_image(image)
+
+    headers = {
+      "Content-Type": "application/json",
+      "Authorization": f"Bearer {api_key}"
+    }
+
+    payload = {
+    "model": "gpt-4-vision-preview",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {
+            "type": "text",
+            "text": text
+          },
+          {
+            "type": "image_url",
+            "image_url": {
+              "url":  f"data:image/jpeg;base64,{base64_image}",
+              "detail": "low"
+
+            }
+          }
+        ]
+      }
+    ],
+    "max_tokens": 300
+  }
+
+
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+
+    print(response.json())
+    return response.json()['choices'][0]['message']['content']
+
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    <center>
+    <h1>
+    Uso de AI para la generación de imagenes a partir de texto.
+    </h1>
+    <img src='data:image/jpg;base64,{}' width=200px>
+    <h2>
+    Con este espacio podrás hacer que una AI describa lo que ve en una imagen al responder una pregunta sobre la misma.
+    </h2>
+    <h2>
+     Obtendrás mejores resultados si la pregunta se mantiene simple, por ejemplo, ¿Que se ve en la imagen?
+    </h2>
+    
+    </center>
+    """.format(
+            encoded_image
+        )
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                gr.Markdown("Primero debes ingresar la pregunta para la imagen imagen:")
+
+            with gr.Row():
+                prompt = gr.Textbox(
+                    label="Pregunta, debe ser simple."
+                ) 
+            with gr.Row():
+                image= gr.Image(type="filepath")
+            with gr.Row():
+                btn= gr.Button()
+
+        with gr.Column():
+            output = gr.TextArea(
+                label="Resultado"
+            )  # Move the output up too
+            # examples = gr.Examples(
+            #     inputs=[prompt]
+            #     examples=[["Un perro en el parque", "low quality"]],
+            # )
+    btn.click(respond2,inputs=[image,prompt], outputs=output)
+
+demo.queue()
+demo.launch()

requirements.txt

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+openai
+gradio
+python-dotenv