Update app.py

app.py

@@ -1,43 +1,40 @@
 import os
-import logging
-import gradio as gr
-import requests
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-debug = True
-logging_level = logging.DEBUG if debug else logging.INFO
-logging.basicConfig(level=logging_level)
-
-# Initialize the CodeBERT model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("microsoft/CodeBERT-base")
-model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/CodeBERT-base")
-
-def fetch_repo_contents(repo_url):
-    username, repo_name = repo_url.split("github.com/")[-1].split("/")
-    api_url = f"https://api.github.com/repos/{username}/{repo_name}/contents"
-    response = requests.get(api_url)
-    response.raise_for_status()
-    return response.json()
-
-def generate_chatbot_response(repo_url, question):
-    repo_contents = fetch_repo_contents(repo_url)
-    prompt = f"Answer the question about the repository {repo_url}: {question}\n\n"
-    for item in repo_contents:
-        prompt += f"{item['name']}:\n{item['download_url']}\n\n"
-    inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=1024, truncation=True)
-    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
-
-# Gradio UI
-repo_url_input = gr.inputs.Text(label="GitHub Repository URL")
-question_input = gr.inputs.Text(label="Question")
-output_text = gr.outputs.Text(label="Answer")
-
-gr.Interface(
-    generate_chatbot_response,
-    inputs=[repo_url_input, question_input],
-    outputs=output_text,
-    title="Create a Conversational AI Chatbot for Your Public GitHub Repository Codebase",
-    theme="huggingface_dark",
-).launch()
+import streamlit as st
+from langchain import HuggingFaceHub, LLMChain
+from git import Repo
+
+# Run pip freeze and pip install -r requirements.txt
+os.system("pip freeze > requirements.txt")
+os.system("pip install -r requirements.txt")
+
+st.set_page_config(layout="wide", initial_sidebar_state="auto", theme="dark")
+st.title("Hugging Face Space Demo")
+
+repository_url = st.text_input("Enter GitHub repository URL:", "")
+access_token = st.text_input("Enter GitHub access token (optional):", "")
+debug_logging = st.checkbox("Enable debug logging")
+
+if st.button("Run"):
+    if debug_logging:
+        import logging
+        logging.basicConfig(filename='log.txt', level=logging.DEBUG, format='%(asctime)s %(message)s')
+        logging.debug('Starting the process')
+
+    # Clone the repository
+    local_path = "/tmp/repository"
+    Repo.clone_from(repository_url, local_path, branch="main", env={"GIT_TERMINAL_PROMPT": "0", "GIT_SSL_NO_VERIFY": "true"})
+
+    # Initialize Hugging Face model
+    os.environ['HUGGINGFACEHUB_API_TOKEN'] = access_token
+    hub_llm = HuggingFaceHub(repo_id='google/flan-t5-xl', model_kwargs={'temperature': 1e-10})
+
+    # Create a prompt template and LLM chain
+    prompt = "What is the main purpose of the repository at {}?".format(repository_url)
+    llm_chain = LLMChain(prompt=prompt, llm=hub_llm)
+
+    # Get the result
+    answer = llm_chain.run()
+    st.write("Answer:", answer)
+
+    if debug_logging:
+        logging.debug('Finished the process')