Update app.py

New update.
Previous code was:"import gradio as gr
from huggingface_hub import InferenceClient

"""
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
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
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]})

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


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)


if __name__ == "__main__":
demo.launch()
"

app.py

@@ -1,64 +1,95 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+# SkyCode Version 1.0 - Complete Code  
+import gradio as gr  
+from fastapi import FastAPI, HTTPException  
+from pydantic import BaseModel  
+from transformers import AutoModelForCausalLM, AutoTokenizer  
+import subprocess  
+import requests  
+import uvicorn  
 
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Initialize FastAPI  
+app = FastAPI()  
 
+# AI Models  
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/codex-1")  
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/codex-1")  
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Pydantic Models  
+class Prompt(BaseModel):  
+    prompt: str  
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+class Question(BaseModel):  
+    code: str  
+    question: str  
 
-    messages.append({"role": "user", "content": message})
+class BuggyCode(BaseModel):  
+    code: str  
 
-    response = ""
+# API Endpoints  
+@app.post("/text-to-code")  
+def text_to_code(prompt: Prompt):  
+    inputs = tokenizer(prompt.prompt, return_tensors="pt")  
+    outputs = model.generate(inputs["input_ids"], max_length=100)  
+    return {"code": tokenizer.decode(outputs[0], skip_special_tokens=True)}  
 
-    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
+@app.post("/ask")  
+def ask_question(question: Question):  
+    # Example: Integrate with Q&A AI  
+    return {"answer": "This code calculates the factorial of a number."}  
 
-        response += token
-        yield response
+@app.post("/fix-bugs")  
+def fix_bugs(buggy_code: BuggyCode):  
+    # Example: Integrate with Bug Fixing AI  
+    return {"fixed_code": "def factorial(n):\n    return 1 if n == 0 else n * factorial(n-1)"}  
 
+@app.post("/analyze-code")  
+def analyze_code(code: str):  
+    # Analyze code for vulnerabilities  
+    with open("sandbox/tmp_code.py", "w") as f:  
+        f.write(code)  
+    result = subprocess.run(["bandit", "-r", "sandbox/tmp_code.py"], capture_output=True)  
+    return {"result": result.stdout.decode("utf-8")}  
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# Gradio Frontend  
+def skycode_interface(prompt):  
+    response = requests.post("http://localhost:8000/text-to-code", json={"prompt": prompt})  
+    return response.json().get("code")  
 
+def ask_skycode(code, question):  
+    response = requests.post("http://localhost:8000/ask", json={"code": code, "question": question})  
+    return response.json().get("answer")  
 
-if __name__ == "__main__":
-    demo.launch()
+def fix_skycode_bugs(code):  
+    response = requests.post("http://localhost:8000/fix-bugs", json={"code": code})  
+    return response.json().get("fixed_code")  
+
+def analyze_skycode(code):  
+    response = requests.post("http://localhost:8000/analyze-code", json={"code": code})  
+    return response.json().get("result")  
+
+with gr.Blocks() as demo:  
+    gr.Markdown("# SkyCode: AI-Powered Software Engineering Tool")  
+    with gr.Tab("Text to Code"):  
+        prompt = gr.Textbox(label="Enter your prompt", placeholder="Create a Python function to calculate factorial.")  
+        code_output = gr.Textbox(label="Generated Code", interactive=False)  
+        gr.Button("Generate Code").click(skycode_interface, inputs=prompt, outputs=code_output)  
+    with gr.Tab("Ask SkyCode"):  
+        code_input = gr.Textbox(label="Paste your code", placeholder="def factorial(n):...")  
+        question_input = gr.Textbox(label="Ask a question", placeholder="What does this code do?")  
+        answer_output = gr.Textbox(label="Answer", interactive=False)  
+        gr.Button("Ask").click(ask_skycode, inputs=[code_input, question_input], outputs=answer_output)  
+    with gr.Tab("Fix Bugs"):  
+        buggy_code = gr.Textbox(label="Paste your buggy code", placeholder="def factorial(n):...")  
+        fixed_code = gr.Textbox(label="Fixed Code", interactive=False)  
+        gr.Button("Fix Bugs").click(fix_skycode_bugs, inputs=buggy_code, outputs=fixed_code)  
+    with gr.Tab("Analyze Code"):  
+        code_to_analyze = gr.Textbox(label="Paste your code", placeholder="def factorial(n):...")  
+        analysis_output = gr.Textbox(label="Analysis Result", interactive=False)  
+        gr.Button("Analyze").click(analyze_skycode, inputs=code_to_analyze, outputs=analysis_output)  
+
+# Run the App  
+if __name__ == "__main__":  
+    # Start the backend server  
+    uvicorn.run(app, host="0.0.0.0", port=8000)  
+    # Start the frontend interface  
+    demo.launch()