Update app.py

app.py

@@ -8,40 +8,37 @@ import pandas as pd
 
 openai.api_key = st.secrets["open_ai_key"]
 
-def get_response(context, prompt):
-    
-
-    return gpt_response
-
 uploaded_file = st.file_uploader("Choose a file")
 if uploaded_file is not None:
-
     
-
     image = Image.open(uploaded_file)
-    with st.expander('Preview uploaded image', expanded=False):
+    
+    with st.expander('Uploaded image', expanded=False):
         st.image(image)
-    extractedInformation = pytesseract.image_to_string(image)
-    st.write(extractedInformation)
-
-    context = '''parse the unstructured input of a cooking recipe into a valid JSON array of objects in the following format:
-    [{
-    "recipe_name": "the name of the recipe expressed as a string",
-    "recipe_ingredients": "list of ingredients specified in the recipe expressed as string",
-    "recipe_steps": "list of steps to cook the recipe expressed as a string"
-    }]'''
-
-    gpt_response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": context},
-            {"role": "user", "content": extractedInformation}
-        ],
-        temperature=0.2,
-        max_tokens=1000
-    )
-
-    st.json((json.loads(gpt_response['choices'][0]['message']['content'])))
-    cost = gpt_response['usage']["prompt_tokens"]*(0.0015/1000) + gpt_response['usage']["completion_tokens"]*(0.002/1000)
-    st.write(f'Cost for query was approx ${cost}')
+    
+    with st.expander('Raw OCR', expanded=False):
+        extractedInformation = pytesseract.image_to_string(image)
+        st.write(extractedInformation)
+    
+    with st.expander('GPT cleaned output', expanded=True):
+        context = '''parse the unstructured input of a cooking recipe into a valid JSON array of objects in the following format:
+        [{
+        "recipe_name": "the name of the recipe expressed as a string",
+        "recipe_ingredients": "list of ingredients specified in the recipe expressed as string",
+        "recipe_steps": "list of steps to cook the recipe expressed as a string"
+        }]'''
+    
+        gpt_response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": context},
+                {"role": "user", "content": extractedInformation}
+            ],
+            temperature=0.2,
+            max_tokens=1000
+        )
+    
+        st.json((json.loads(gpt_response['choices'][0]['message']['content'])))
+        cost = gpt_response['usage']["prompt_tokens"]*(0.0015/1000) + gpt_response['usage']["completion_tokens"]*(0.002/1000)
+        st.write(f'Cost for query was approx ${cost}')