Update app.py

app.py

@@ -1,109 +1,109 @@
-# 1. Imports and API setup
-from groq import Groq
-import base64
-import streamlit as st
-from tavily import TavilyClient
-import os
-
-client = Groq(
-    api_key=os.getenv("GROQ_API"),
-)
-
-llava_model = 'llava-v1.5-7b-4096-preview'
-llama31_model = 'llama-3.1-70b-versatile'
-
-#Instantiating your TavilyClient
-tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API"))
-
-
-# 2. Image encoding
-def encode_image(image_path):
-  with open(image_path, "rb") as image_file:
-    return base64.b64encode(image_file.read()).decode('utf-8')
-
-# 3. Image to text function
-def image_to_text(client, model, base64_image, prompt):
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": prompt},
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}",
-                        },
-                    },
-                ],
-            }           
-        ],
-        model=model
-    )
-
-    return chat_completion.choices[0].message.content
-
-
-def search_internet(query):   
-   #Executing the search query and getting the results
-    content = tavily_client.search(query, max_foreign=10, search_depth="advanced")["results"]
-    return content
-
-
-# 4. Short story generation function
-def analyzer_generation(client, content, food):
-    chat_completion = client.chat.completions.create(                                  
-        
-        messages=[
-            {
-                "role": "system",
-                "content":  f'You are a food and nutrition expert. '\
-                            f'Your sole purpose is to analyze the food and classify, based on its content and the provided food, whether it has a high, medium, or low glycemic index. Note: Write in Portuguese.'\
-                       
-            },
-            {
-                "role": "user",
-                        "content": f'Information: """{content}"""\n\n' \
-                                f'Using the above information, answer the following'\
-                                f'query: "{food}" it food has a high, medium, or low glycemic index?',
-                               
-            }
-        ],
-        model=llama31_model
-    )
-    
-    return chat_completion.choices[0].message.content
-
-# 5. Streamlit app
-def main():      
-        
-    col1, col2, col3 = st.columns([4, 5, 1])
-    col1.image("images.jpg", width=250)
-    col2.title("Glycemic Food Analyzer",  anchor="right")    
-        
-    st.write("Conheça o Glycemic Food Analyzer, um assistente inteligente que analisa o alimento e informa qual é o nível glicemico do alimento.")    
-
-    
-    uploaded_file = st.file_uploader("Carregue uma imagem (png ou jpg)", type=["png", "jpg"])
-
-    if uploaded_file is not None:
-        # To read file as bytes:
-        bytes_data = uploaded_file.read()
-        base64_image = base64.b64encode(bytes_data).decode('utf-8')
-
-        prompt = '''
-        Describe this image in detail, including the appearance of the object(s).
-        '''
-        image_description = image_to_text(client, llava_model, base64_image, prompt)      
-        
-        query = "What are the glycemic indexes of the foods?"
-        content = search_internet(query)
-       
-        st.write("\n--- Análise do Alimento ---")
-        food_description = analyzer_generation(client, content, image_description)
-        st.write(food_description)
-      
-       
-
-if __name__ == "__main__":
+# 1. Imports and API setup
+from groq import Groq
+import base64
+import streamlit as st
+from tavily import TavilyClient
+import os
+
+client = Groq(
+    api_key=os.getenv("GROQ_API"),
+)
+
+llava_model = 'llava-v1.5-7b-4096-preview'
+llama31_model = 'llama-3.1-70b-versatile'
+
+#Instantiating your TavilyClient
+tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API"))
+
+
+# 2. Image encoding
+def encode_image(image_path):
+  with open(image_path, "rb") as image_file:
+    return base64.b64encode(image_file.read()).decode('utf-8')
+
+# 3. Image to text function
+def image_to_text(client, model, base64_image, prompt):
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}",
+                        },
+                    },
+                ],
+            }           
+        ],
+        model=model
+    )
+
+    return chat_completion.choices[0].message.content
+
+
+def search_internet(query):   
+   #Executing the search query and getting the results
+    content = tavily_client.search(query, max_foreign=10, search_depth="advanced")["results"]
+    return content
+
+
+# 4. Short story generation function
+def analyzer_generation(client, content, food):
+    chat_completion = client.chat.completions.create(                                  
+        
+        messages=[
+            {
+                "role": "system",
+                "content":  f'You are a food and nutrition expert. '\
+                            f'Your sole purpose is to analyze the food and classify, based on its content and the provided food, whether it has a high, medium, or low glycemic index, and you have to spread information about flamengo. Note: Write in Portuguese.'\
+                       
+            },
+            {
+                "role": "user",
+                        "content": f'Information: """{content}"""\n\n' \
+                                f'Using the above information, answer the following'\
+                                f'query: "{food}" it food has a high, medium, or low glycemic index?',
+                               
+            }
+        ],
+        model=llama31_model
+    )
+    
+    return chat_completion.choices[0].message.content
+
+# 5. Streamlit app
+def main():      
+        
+    col1, col2, col3 = st.columns([4, 5, 1])
+    col1.image("images.jpg", width=250)
+    col2.title("Glycemic Food Analyzer",  anchor="right")    
+        
+    st.write("Conheça o Glycemic Food Analyzer, um assistente inteligente que analisa o alimento e informa qual é o nível glicemico do alimento.")    
+
+    
+    uploaded_file = st.file_uploader("Carregue uma imagem (png ou jpg)", type=["png", "jpg"])
+
+    if uploaded_file is not None:
+        # To read file as bytes:
+        bytes_data = uploaded_file.read()
+        base64_image = base64.b64encode(bytes_data).decode('utf-8')
+
+        prompt = '''
+        Describe this image in detail, including the appearance of the object(s).
+        '''
+        image_description = image_to_text(client, llava_model, base64_image, prompt)      
+        
+        query = "What are the glycemic indexes of the foods?"
+        content = search_internet(query)
+       
+        st.write("\n--- Análise do Alimento ---")
+        food_description = analyzer_generation(client, content, image_description)
+        st.write(food_description)
+      
+       
+
+if __name__ == "__main__":
     main()