Update app.py

app.py

@@ -46,30 +46,55 @@
 # iface = gr.Interface(fn=func, inputs="text", outputs="text")
 # iface.launch()
 
-import gradio as gr
-from langchain.llms import LlamaCpp
-from langchain import PromptTemplate, LLMChain
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+# import gradio as gr
+# from langchain.llms import LlamaCpp
+# from langchain import PromptTemplate, LLMChain
+# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 
-print("DONE")
+# print("DONE")
 
-def func(user):
-    template = """
-    Your name is John and not a assistant but more like a chatbot. Respond precise not more words and act like a human. for example: user: How are you? You: I'm doing good how about you?. user: hello You: Hello how you doing?. Don't say How can I assist you today?.
-    Question: {question}
+# def func(user):
+#     template = """
+#     Your name is John and not a assistant but more like a chatbot. Respond precise not more words and act like a human. for example: user: How are you? You: I'm doing good how about you?. user: hello You: Hello how you doing?. Don't say How can I assist you today?.
+#     Question: {question}
     
-    Answer: """
+#     Answer: """
     
-    prompt = PromptTemplate(template=template, input_variables=["question"])
+#     prompt = PromptTemplate(template=template, input_variables=["question"])
     
-    local_path = "./nous-hermes-13b.ggmlv3.q4_0.bin"
+#     local_path = "./nous-hermes-13b.ggmlv3.q4_0.bin"
     
-    llm = LlamaCpp(model_path=local_path)
-    llm_chain = LLMChain(prompt=prompt, llm=llm, streaming=True)  # Enable streaming mode
-    question = user
-    llm_chain.run(question)
+#     llm = LlamaCpp(model_path=local_path)
+#     llm_chain = LLMChain(prompt=prompt, llm=llm, streaming=True)  # Enable streaming mode
+#     question = user
+#     llm_chain.run(question)
+
+#     return llm_chain.run(question)
+
+# iface = gr.Interface(fn=func, inputs="text", outputs="text")
+# iface.launch()
 
-    return llm_chain.run(question)
 
-iface = gr.Interface(fn=func, inputs="text", outputs="text")
+import gradio as gr
+from gpt4allj import Model
+
+# Load the local model
+model = Model('./ggml-gpt4all-j.bin')
+
+# Define a function that generates the model's response given a prompt
+def generate_response(prompt):
+    response = model.generate(prompt)
+    return response
+
+# Create a Gradio interface with a text input and an output text box
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="GPT-4 AllJ",
+    description="Generate responses using GPT-4 AllJ model."
+)
+
+# Run the Gradio interface
 iface.launch()
+