Update app.py

app.py

@@ -1,10 +1,101 @@
-
 import gradio as gr
 from huggingface_hub import InferenceClient
+import pandas as pd
+import json
+
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    uploaded_file,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    if uploaded_file is not None:
+        with open(uploaded_file.name, "r") as f:
+            file_content = f.read()
+        messages.append({"role": "user", "content": file_content})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+    # Process
+    if uploaded_file is not None:
+        print(f"Uploaded file: {uploaded_file.name}")
+
+        # CSV file
+        if uploaded_file.name.endswith(".csv"):
+            try:
+                df = pd.read_csv(uploaded_file.name)
+                print(f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.")
+                json_data = df.to_json(orient="records")
+                with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file:
+                    json_file.write(json_data)
+                print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json")
+            except Exception as e:
+                print(f"Error loading CSV file: {e}")
+
+        # text file
+        elif uploaded_file.name.endswith(".txt"):
+            try:
+                with open(uploaded_file.name, "r") as f:
+                    text = f.read()
+                print(f"Text file loaded with {len(text)} characters.")
+                json_data = json.dumps({"text": text})
+                with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file:
+                    json_file.write(json_data)
+                print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json")
+            except Exception as e:
+                print(f"Error loading text file: {e}")
+
+demo = gr.ChatInterface(
+    respond,
+    title="Nixie Steamcore, a hotbot!",
+    additional_inputs=[
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=1.2, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+        gr.File(label="Upload a document"),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()
 
 """
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+import gradio as gr
+from huggingface_hub import InferenceClient
+
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
@@ -40,9 +131,6 @@ def respond(
         response += token
         yield response
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -61,4 +149,5 @@ demo = gr.ChatInterface(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+    """