Add application file

app.py

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+from dotenv import load_dotenv
+import os
+import google.generativeai as genai
+from groq import Groq
+from PIL import Image
+import gradio as gr
+
+# Load environment variables from .env
+load_dotenv()
+
+from groq import Groq
+
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
+
+
+
+
+# Fetch variables
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+
+#login(token=HF_TOKEN)
+
+
+def product_identification_response(image_path=r"C:\Users\JoeJo\Downloads\XyAaqBEtYtb8YffjKZ68Gb.jpg"):
+    # Authenticate
+    genai.configure(api_key=os.environ.get("GENAI_API_KEY"))
+    
+    # Load Gemini Pro Vision
+    model = genai.GenerativeModel('gemini-1.5-flash')
+    
+    # Load your image
+    clean_path = image_path.strip('"')
+    image = Image.open(clean_path)
+    
+    # Ask Gemini
+    response = model.generate_content(
+        ["What product is in this image, and what is the condition of the product?", image]
+    )
+    
+    print(f"gemini-1.5-flash answer is: {response.text}")
+    
+    prompt = f"""Your task is to returned structured JSON of product and condition in the following format: {{ "product": "the identity of the product", "condition": "the condition of the product"}}.
+    The condition of the product must be one of the following: (*) New, (*) Like New, (*) Good or (*) Poor.
+    Use the data from {response} as the source for your response
+    """
+    
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": prompt
+            },
+            {
+                "role": "user",
+                "content": response.text,
+            }
+            ],
+            model="llama-3.3-70b-versatile",
+            response_format={"type": "json_object"},#and include word 'json' in messages/prompt
+        )
+        
+    print(chat_completion.choices[0].message.content)
+    return chat_completion.choices[0].message.content
+
+
+
+#product_identification_response()
+
+demo = gr.Interface(
+    fn=product_identification_response,
+    inputs="text",
+    outputs="text",
+    title="identify product and condition",
+    description="finds info about a product"
+)
+
+demo.launch(share=True)