Upload gradio_chat_app.py

gradio_chat_app.py

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+
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+
+# Load CSV file
+def load_csv(file_path):
+    # Load the CSV file into a DataFrame
+    return pd.read_csv(file_path)
+
+# Placeholder for the LLM pipeline
+def llm_pipeline(prompt):
+    # This function should be filled with the logic to process the prompt using an LLM.
+    # For example, using OpenAI's GPT-3 or GPT-4 model.
+    return "Response from LLM based on the prompt: " + prompt
+
+# Placeholder for querying against CSV
+def query_csv(query, df):
+    # This function should contain the logic for querying the DataFrame.
+    # For example, using natural language processing techniques.
+    return "Queried response based on CSV data."
+
+# Gradio interface function
+def chat_with_llm(prompt, file_path):
+    df = load_csv(file_path)
+    response = llm_pipeline(prompt)
+    query_response = query_csv(prompt, df)
+    return response + "\n\n" + query_response
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=chat_with_llm,
+    inputs=[gr.inputs.Textbox(label="Your Prompt"), gr.inputs.File(label="CSV File")],
+    outputs=gr.outputs.Textbox(label="Response"),
+    title="Chat with LLM and Query CSV",
+    description="A chat interface to interact with a language model and query a CSV file."
+)
+
+# Run the interface
+if __name__ == "__main__":
+    iface.launch()