init

app.py

@@ -1,20 +1,40 @@
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import AIMessage, HumanMessage
-import openai
-import os
 import gradio as gr
+from transformers import pipeline
 
-os.environ["OPENAI_API_KEY"] = "sk-proj-KyHyIzEDLKe94DhrSq6LT3BlbkFJynj2NRPnnRwdDSV3XaZY"  # Replace with your key
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions}
 
-def predict(message, history):
-    history_langchain_format = []
-    for human, ai in history:
-        history_langchain_format.append(HumanMessage(content=human))
-        history_langchain_format.append(AIMessage(content=ai))
-    history_langchain_format.append(HumanMessage(content=message))
-    gpt_response = llm(history_langchain_format)
-    return gpt_response.content
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
 
-gr.ChatInterface(predict).launch()
+if __name__ == "__main__":
+    gradio_app.launch()
+
+
+# from langchain.chat_models import ChatOpenAI
+# from langchain.schema import AIMessage, HumanMessage
+# import openai
+# import os
+# import gradio as gr
+#
+# os.environ["OPENAI_API_KEY"] = "sk-proj-KyHyIzEDLKe94DhrSq6LT3BlbkFJynj2NRPnnRwdDSV3XaZY"  # Replace with your key
+#
+# llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
+#
+# def predict(message, history):
+#     history_langchain_format = []
+#     for human, ai in history:
+#         history_langchain_format.append(HumanMessage(content=human))
+#         history_langchain_format.append(AIMessage(content=ai))
+#     history_langchain_format.append(HumanMessage(content=message))
+#     gpt_response = llm(history_langchain_format)
+#     return gpt_response.content
+#
+# gr.ChatInterface(predict).launch()

requirements.txt

@@ -1,4 +1,4 @@
 transformers
 torch
 openai
-langchain-community
+langchain