Update app.py

app.py

@@ -1,59 +1,51 @@
 import gradio as gr
 from fastai.vision.all import *
-import skimage
 import openai
+import os
+
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-# Load the model
+# Load your trained model (you should replace 'model.pkl' with the path to your model file)
 learn = load_learner('model.pkl')
 
-# Define the labels
+# Define the labels for the output
 labels = learn.dls.vocab
 
-# Define a function for generating text
-def generate_text(prompt):
-  response = openai.Completion.create(
-      engine="davinci",
-      prompt=prompt,
-      max_tokens=1024,
-      n=1,
-      stop=None,
-      temperature=0.7,
-  )
-  return response.choices[0].text.strip()
-
-# Define a function to handle user queries
-def handle_query(query, chat_history):
-  response = openai.ChatCompletion.create(
-      model="gpt-3.5-turbo",
-      messages=[{"role": "system", "content": "You are a helpful assistant."},
-               {"role": "user", "content": query}] + chat_history
-  )
-  return response.choices[0].message['content']
-
 # Define the prediction function
 def predict(img):
-  img = PILImage.create(img)
-  pred,pred_idx,probs = learn.predict(img)
-  prediction = {labels[i]: float(probs[i]) for i in range(len(labels))}
-  chat_prompt = f"The model predicted {prediction}."
-  chat_response = generate_text(chat_prompt)
-  return {**prediction, 'chat_response': chat_response}
-
-# Define the chat function
-def chat(query, chat_history):
-  chat_response = handle_query(query, chat_history)
-  return chat_response
-
-# Define the examples
-examples = ['image.jpg']
-
-# Define the interpretation
-interpretation='default'
-
-# Define the enable_queue
-enable_queue=True
-
-# Launch the interface
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    prediction = {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+    # Now generate a chat/text response based on the model's prediction.
+    chat_prompt = f"The image likely depicts the following: {pred}. What can I help you with next?"
+
+    # Ensure that you have set the OPENAI_API_KEY environment variable,
+    # as we will use it to interact with OpenAI's GPT-3 model.
+    response = openai.Completion.create(
+        engine="text-davinci-003",  # Adjust the engine as needed for your use-case
+        prompt=chat_prompt,
+        max_tokens=1024,
+        n=1,
+        stop=None,
+        temperature=0.7,
+    )
+    text_response = response.choices[0].text.strip()
+
+    return prediction, text_response
+
+# Create examples list by specifying the paths to the example images
+examples = ["path/to/example1.jpg", "path/to/example2.jpg"]  # replace with actual image paths
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(shape=(512, 512)),
+    outputs=[gr.Label(num_top_classes=3), gr.Textbox(label="GPT-3 Response")],
+    examples=examples,
+    enable_queue=True  # This is optional and only necessary if you're hosting under heavy traffic
+)
+
+# Launch the Gradio app
+iface.launch()