Update app.py

app.py

@@ -1,12 +1,36 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
+import requests
 
-"""
-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
-"""
+# Hugging Face Inference Client
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# GitHub API details
+GITHUB_USERNAME = "YOUR_USERNAME"
+GITHUB_REPOSITORY = "YOUR_REPOSITORY"
+GITHUB_API_TOKEN = os.getenv("GITHUB_API_TOKEN")
 
+# Function to fetch GitHub issues
+def fetch_github_issues():
+  url = f"https://api.github.com/repos/{GITHUB_USERNAME}/{GITHUB_REPOSITORY}/issues"
+  headers = {
+    "Authorization": f"Bearer {GITHUB_API_TOKEN}",
+    "Accept": "application/vnd.github.v3+json"
+  }
+  response = requests.get(url, headers=headers)
+  if response.status_code == 200:
+    return response.json()
+  else:
+    raise Exception(f"Error fetching issues: {response.status_code}")
+
+# Function to analyze issues and provide solutions
+def analyze_issues(issue_text, model_name):
+  nlp = pipeline("text-generation", model=model_name)
+  result = nlp(issue_text)
+  return result[0]['generated_text']
+
+# Function to handle chat responses
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -23,41 +47,54 @@ def respond(
         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 message.startswith("/github"):
+        try:
+            issues = fetch_github_issues()
+            issue_list = "\n".join([f"{i+1}. {issue['title']}" for i, issue in enumerate(issues)])
+            yield f"Available GitHub Issues:\n{issue_list}\n\nEnter the issue number to analyze:"
+        except Exception as e:
+            yield f"Error fetching GitHub issues: {e}"
+    elif message.isdigit():
+        try:
+            issue_number = int(message) - 1
+            issues = fetch_github_issues()
+            issue = issues[issue_number]
+            issue_text = issue['title'] + "\n\n" + issue['body']
+            resolution = analyze_issues(issue_text, "gpt2") # Default to gpt2 for now
+            yield f"Resolution for Issue '{issue['title']}':\n{resolution}"
+        except Exception as e:
+            yield f"Error analyzing issue: {e}"
+    else:
+        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
 
+with gr.Blocks() as demo:
+    chatbot = 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()
+    demo.queue().launch(share=True, server_name="0.0.0.0", server_port=7860)