Update app.py

app.py

@@ -1,107 +1,51 @@
 import gradio as gr
-import requests
-from bs4 import BeautifulSoup
 from huggingface_hub import InferenceClient
 
-# Initialize Hugging Face client
+# Initialize Hugging Face client with your model
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Bot 1: Web Search
-def bot1_task(search_query):
-    try:
-        # Simulate a web search using a request to a search engine (basic example)
-        headers = {"User-Agent": "Mozilla/5.0"}
-        search_url = f"https://www.google.com/search?q={search_query}"
-        response = requests.get(search_url, headers=headers)
-        soup = BeautifulSoup(response.text, "html.parser")
-        # Extract the first few search results (as plain text)
-        results = [a.text for a in soup.find_all('h3')[:3]]
-        return results if results else ["No results found."]
-    except Exception as e:
-        return [f"Error in Web Search: {str(e)}"]
+# Define the task for each bot
+def search_web(query):
+    # Simulating web search (replace with actual search logic)
+    return f"Searching for: {query}..."
 
-# Bot 2: Summarization
-def bot2_task(content_list):
-    summaries = []
-    for content in content_list:
-        try:
-            response = ""
-            for message in client.chat_completion(
-                [{"role": "user", "content": f"Summarize this: {content}"}],
-                max_tokens=100,
-                stream=True,
-                temperature=0.7,
-                top_p=0.95,
-            ):
-                response += message.choices[0].delta.content
-            summaries.append(response)
-        except Exception as e:
-            summaries.append(f"Error summarizing content: {str(e)}")
-    return summaries
+def summarize_content(content):
+    # Simulating summarization (replace with actual summarization logic)
+    return f"Summary: {content[:50]}..."  # Simple truncation for demo
 
-# Bot 3: Analysis & Final Overview
-def bot3_task(summaries):
-    try:
-        final_analysis = " ".join(summaries)
-        response = ""
-        for message in client.chat_completion(
-            [{"role": "user", "content": f"Provide a detailed overview: {final_analysis}"}],
-            max_tokens=200,
-            stream=True,
-            temperature=0.7,
-            top_p=0.95,
-        ):
-            response += message.choices[0].delta.content
-        return response
-    except Exception as e:
-        return f"Error in final analysis: {str(e)}"
+def final_review(summary):
+    # Simulating final review of the summary (replace with actual review logic)
+    return f"Final Overview: {summary}"
 
-# Gradio Interface
-def process_workflow(search_query):
-    # Step 1: Web Search
-    search_results = bot1_task(search_query)
-    # Step 2: Summarization
-    summaries = bot2_task(search_results)
-    # Step 3: Analysis
-    final_result = bot3_task(summaries)
-    return {
-        "search_results": search_results,
-        "summaries": summaries,
-        "final_analysis": final_result,
-    }
+# Define the process button callback function
+def process_task(query, history):
+    # 1. First bot searches the web
+    search_result = search_web(query)
+    
+    # 2. Second bot summarizes the content
+    summary = summarize_content(search_result)
+    
+    # 3. Third bot gives the final review
+    final_overview = final_review(summary)
+    
+    # Return results for each step (this will be reflected in the outputs)
+    return search_result, summary, final_overview
 
+# Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Multi-Bot Problem Solving Workflow")
+    # Create input component for user to type in a query
+    query_input = gr.Textbox(label="Enter your search query")
     
-    # Input Section
-    with gr.Row():
-        search_query = gr.Textbox(label="Enter a Search Query", placeholder="What do you want to find?")
-        process_button = gr.Button("Start Workflow")
+    # Create output components for search result, summary, and final review
+    search_output = gr.Textbox(label="Search Result")
+    summary_output = gr.Textbox(label="Summary")
+    final_output = gr.Textbox(label="Final Review")
     
-    # Output Section
-    with gr.Tab(label="Bot Outputs"):
-        with gr.Row():
-            gr.Markdown("### Web Search Results")
-            search_results_output = gr.Textbox(label="Bot 1: Search Results", lines=6)
-        
-        with gr.Row():
-            gr.Markdown("### Summarized Content")
-            summaries_output = gr.Textbox(label="Bot 2: Summaries", lines=6)
-        
-        with gr.Row():
-            gr.Markdown("### Final Analysis")
-            final_output = gr.Textbox(label="Bot 3: Final Overview", lines=6)
+    # Create process button to start the task
+    process_button = gr.Button("Process Task")
     
-    # Workflow connection
-    process_button.click(
-        process_workflow,
-        inputs=[search_query],
-        outputs={
-            "search_results": search_results_output,
-            "summaries": summaries_output,
-            "final_analysis": final_output,
-        },
-    )
+    # Define the button click action
+    process_button.click(process_task, inputs=[query_input, None], outputs=[search_output, summary_output, final_output])
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+demo.launch()