Update app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+import openai
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load the model
+learn = load_learner('model.pkl')
+
+# Define the labels
+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()