change the app to a chatbot

.gitignore

@@ -0,0 +1 @@
+.venv

main.py

@@ -1,30 +1,16 @@
+from langchain.schema import AIMessage, HumanMessage
 import gradio as gr
-import torch
-import requests
-from torchvision import transforms
+from langchain_community.llms import Ollama
 
-model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
+llm = Ollama(model="mistral:7b", timeout=1000)
 
+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.invoke(history_langchain_format)
+    return gpt_response
 
-def predict(inp):
-    inp = transforms.ToTensor()(inp).unsqueeze(0)
-    with torch.no_grad():
-        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-        confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-    return confidences
-
-
-def run():
-    demo = gr.Interface(
-        fn=predict,
-        inputs=gr.Image(type="pil"),
-        outputs=gr.Label(num_top_classes=3),
-    )
-
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
-
-if __name__ == "__main__":
-    run()
+gr.ChatInterface(predict).launch()

requirements.txt

@@ -1,4 +1,4 @@
 gradio
-torch
-torchvision
+langchain
+langchain-community
 requests