chat

app.py

@@ -115,10 +115,16 @@ def show_combined_repo_and_llm():
     return combined_content, summary, df
 
 def go_to_analysis():
-    return gr.update(visible=False), gr.update(visible=True)
+    return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
 
 def go_to_input():
-    return gr.update(visible=True), gr.update(visible=False)
+    return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
+
+def go_to_chatbot():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+
+def go_to_start():
+    return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
 
 repo_id_input = gr.Textbox(label="Enter repo IDs (comma or newline separated)", lines=5, placeholder="repo1, repo2\nrepo3")
 df_output = gr.Dataframe(headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"],
@@ -128,8 +134,14 @@ df_output = gr.Dataframe(headers=["repo id", "strength", "weaknesses", "speciali
 with gr.Blocks() as demo:
     page_state = gr.State(0)
 
+    # --- Start Page: Option Selection ---
+    with gr.Column(visible=True) as start_page:
+        gr.Markdown("## Welcome! How would you like to proceed?")
+        option_a_btn = gr.Button("A) I know which repos I want to search and research about")
+        option_b_btn = gr.Button("B) I don't know exactly what I want (Chatbot)")
+
     # --- Page 1: Input ---
-    with gr.Column(visible=True) as input_page:
+    with gr.Column(visible=False) as input_page:
         gr.Markdown("## Enter Keyword or Repo IDs")
         keyword_input = gr.Textbox(label="Enter keyword to search repos", lines=1, placeholder="e.g. audio")
         keyword_btn = gr.Button("Search and Update Repo List")
@@ -137,6 +149,7 @@ with gr.Blocks() as demo:
         df_box = df_output.render()
         submit_btn = gr.Button("Submit Repo IDs")
         next_btn = gr.Button("Next: Go to Analysis")
+        back_to_start_btn = gr.Button("Back to Start")
 
     # --- Page 2: Analysis ---
     with gr.Column(visible=False) as analysis_page:
@@ -149,10 +162,22 @@ with gr.Blocks() as demo:
             datatype=["str", "str", "str", "str", "str", "str"]
         )
         back_btn = gr.Button("Back to Input")
-
-    # Button logic to switch pages
-    next_btn.click(go_to_analysis, inputs=None, outputs=[input_page, analysis_page])
-    back_btn.click(go_to_input, inputs=None, outputs=[input_page, analysis_page])
+        back_to_start_btn2 = gr.Button("Back to Start")
+
+    # --- Page 3: Chatbot Placeholder ---
+    with gr.Column(visible=False) as chatbot_page:
+        gr.Markdown("## Chatbot (Coming Soon)")
+        gr.Markdown("This is where you will be able to chat and get repo recommendations!")
+        back_to_start_btn3 = gr.Button("Back to Start")
+
+    # Navigation logic
+    option_a_btn.click(go_to_analysis, inputs=None, outputs=[start_page, analysis_page, chatbot_page])
+    option_b_btn.click(go_to_chatbot, inputs=None, outputs=[start_page, analysis_page, chatbot_page])
+    next_btn.click(go_to_analysis, inputs=None, outputs=[input_page, analysis_page, chatbot_page])
+    back_btn.click(go_to_input, inputs=None, outputs=[input_page, analysis_page, chatbot_page])
+    back_to_start_btn.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page])
+    back_to_start_btn2.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page])
+    back_to_start_btn3.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page])
 
     # Keyword and repo input logic
     keyword_btn.click(keyword_search_and_update, inputs=keyword_input, outputs=df_box)

chatbot_page.py

@@ -0,0 +1,71 @@
+import gradio as gr
+from analyzer import analyze_code
+
+# System prompt for the chatbot
+CHATBOT_SYSTEM_PROMPT = (
+    "You are a helpful assistant. Your goal is to help the user describe their ideal open-source repo. "
+    "Ask questions to clarify what they want, their use case, preferred language, features, etc. "
+    "When the user clicks 'End Chat', analyze the conversation and return about 5 keywords for repo search. "
+    "Return only the keywords as a comma-separated list."
+)
+
+# Store the conversation
+conversation_history = []
+
+# Function to handle chat
+def chat_with_user(user_message, history):
+    from openai import OpenAI
+    client = OpenAI()
+    # Build the message list for the LLM
+    messages = [
+        {"role": "system", "content": CHATBOT_SYSTEM_PROMPT}
+    ]
+    for msg in history:
+        messages.append({"role": "user", "content": msg[0]})
+        if msg[1]:
+            messages.append({"role": "assistant", "content": msg[1]})
+    messages.append({"role": "user", "content": user_message})
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages,
+        max_tokens=256,
+        temperature=0.7
+    )
+    assistant_reply = response.choices[0].message.content
+    return assistant_reply
+
+# Function to end chat and extract keywords
+def extract_keywords_from_conversation(history):
+    # Combine all user and assistant messages into a single string
+    conversation = "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[1]])
+    prompt = (
+        "Given the following conversation between a user and an assistant about finding an ideal open-source repo, "
+        "extract about 5 keywords that best represent what the user is looking for. "
+        "Return only the keywords as a comma-separated list.\n\nConversation:\n" + conversation
+    )
+    keywords = analyze_code(prompt)
+    return keywords
+
+with gr.Blocks() as chatbot_demo:
+    gr.Markdown("## Repo Recommendation Chatbot")
+    chatbot = gr.Chatbot()
+    state = gr.State([])  # conversation history
+    user_input = gr.Textbox(label="Your message", placeholder="Describe your ideal repo or answer the assistant's questions...")
+    send_btn = gr.Button("Send")
+    end_btn = gr.Button("End Chat and Extract Keywords")
+    keywords_output = gr.Textbox(label="Extracted Keywords for Repo Search", interactive=False)
+
+    def user_send(user_message, history):
+        assistant_reply = chat_with_user(user_message, history)
+        history = history + [[user_message, assistant_reply]]
+        return history, history, ""
+
+    def end_chat(history):
+        keywords = extract_keywords_from_conversation(history)
+        return keywords
+
+    send_btn.click(user_send, inputs=[user_input, state], outputs=[chatbot, state, user_input])
+    end_btn.click(end_chat, inputs=state, outputs=keywords_output)
+
+if __name__ == "__main__":
+    chatbot_demo.launch()