Update app.py

app.py

@@ -9,174 +9,119 @@ import gradio as gr
 
 
 
-# Set up API keys
-litellm.api_key = os.getenv('GOOGLE_API_KEY')
-os.environ['SERPER_API_KEY'] = os.getenv('SERPER_API_KEY')
+
+# Error handling for API keys
+try: 
+    # Set up API keys
+    litellm.api_key = os.getenv('GOOGLE_API_KEY')
+    os.environ['SERPER_API_KEY'] = os.getenv('SERPER_API_KEY')
+
+    if not litellm.api_key or not os.environ['SERPER_API_KEY']:
+        raise ValueError("API keys are missing. Please ensure both Google API Key and SERPER API Key are set.")
+except Exception as e:
+    print(f"Error setting up API keys: {e}")
+    exit()
 
 # Define the LLM
 llm = "gemini/gemini-1.5-flash-exp-0827"  # Your LLM model
 
 # Initialize the tool for internet searching capabilities
-tool = SerperDevTool()
-
-# Create the CV Analysis Agent
-cv_analysis_agent = Agent(
-    role="CV Analyzer",
-    goal='Analyze the given CV and extract key skills and experiences and make improvements if needed for portfolio creation.',
+try:
+    tool = SerperDevTool(search_url="https://google.serper.dev/scholar", n_results=10)
+except Exception as e:
+    print(f"Error initializing search tool: {e}")
+    exit()
+
+# Research agent
+research_agent = Agent(
+    role="Research Assistant",
+    goal='Discover and retrieve the latest groundbreaking papers and publications on {topic}.',
     verbose=True,
     memory=True,
     backstory=(
-    "You are an expert CV Analyzer with a keen eye for detail. Your role is to meticulously examine the provided CV, "
-    "identifying and extracting key skills, experiences, accomplishments, and areas for improvement. "
-    "Your analysis should highlight strengths and suggest enhancements that would make the portfolio more competitive."
-),
-
-    tools=[tool],
+        "You are an expert researcher who specializes in locating the most recent and relevant research papers. "
+        "You focus on analyzing research from credible sources like Google Scholar, ensuring they are closely aligned with the {topic}. "
+        "Your insights help refine ongoing research by identifying gaps and suggesting areas for improvement."
+    ),
     llm=llm,
     allow_delegation=True
 )
 
-# Create the Portfolio Generation Agent
-portfolio_generation_agent = Agent(
-    role='Portfolio Generator',
-    goal='Generate a professional HTML/CSS/JS responsive landing portfolio webpage based on {cv} analysis.',
+# Writer agent
+writer_agent = Agent(
+    role="Research Key Points Writer",
+    goal="Extract and present the key points of relevant research papers, including publication links.",
     verbose=True,
     memory=True,
     backstory=(
-    "As a Responsive Portfolio Generator, your expertise lies in creating visually appealing and user-friendly web pages. "
-    "Based on the CV analysis, you will generate a professional HTML/CSS/JS portfolio. "
-    "Ensure the design reflects the individual's strengths and experiences while incorporating effective functionality. "
-    "Consider responsiveness, color schemes, and navigation for an optimal user experience."
+        "As a skilled research writer, your task is to extract key information such as objectives, methodologies, findings, and future improvements. "
+        "You will list the publication links in an organized manner."
     ),
     tools=[tool],
     llm=llm,
     allow_delegation=False
 )
 
-# Research task for CV analysis
-cv_analysis_task = Task(
+# Research task
+research_task = Task(
     description=(
-        "Analyze the provided {cv} in detail. Identify and summarize key skills, experiences, and notable accomplishments. "
-        "Highlight educational background and suggest potential enhancements to improve the overall presentation and competitiveness of the CV."
+        "Identify all relevant research papers on {topic}. "
+        "For each paper, extract key points such as the main objectives, methodology, findings, and any significant flaws in the study. "
+        "Highlight gaps in the research and suggest possible improvements."
     ),
-    expected_output='A detailed summary of skills, experiences, accomplishments, and improvement suggestions formatted for a portfolio.',
+    expected_output='A structured list of key points from relevant papers, including strengths, weaknesses, and improvement suggestions.',
     tools=[tool],
-    agent=cv_analysis_agent,
+    agent=research_agent,
 )
 
-
-# Writing task for portfolio generation with enhanced UI requirements
-
-portfolio_task = Task(
+# Writer task
+writer_task = Task(
     description=(
-        "Generate a responsive HTML/CSS portfolio webpage based on the given CV analysis. "
-        "Include a navbar with the individual's name, and sections for skills, projects, experiences, certifications, and contact information. "
-        "Ensure the layout is clean and visually appealing with a light/dark theme toggle option. "
-        "Embed CSS/JS directly into the HTML for easy deployment, and optimize for both desktop and mobile viewing."
+        "Compose a report highlighting the key points from {topic}-related publications. "
+        "The report should include the main objectives, methodologies, and findings of each paper, along with a link to the publication. "
+        "Ensure that the information is accurate, clear and well-organized."
     ),
-    expected_output='A complete and responsive HTML document ready for deployment, showcasing the individual’s strengths.',
+    expected_output='A markdown file (.md) containing key points and publication links for each paper.',
     tools=[tool],
-    agent=portfolio_generation_agent,
+    agent=writer_agent,
     async_execution=True,
+    output_file='key_points_report.md'
 )
 
-# Function to read CV from PDF or DOCX file
-def read_cv_file(file_path):
-    ext = os.path.splitext(file_path)[1].lower()
-    cv_content = ""
-
-    if ext == '.pdf':
-        with pdfplumber.open(file_path) as pdf:
-            for page in pdf.pages:
-                cv_content += page.extract_text()
-    elif ext == '.docx':
-        doc = Document(file_path)
-        for para in doc.paragraphs:
-            cv_content += para.text + "\n"
-    else:
-        raise ValueError("Unsupported file format. Please use .pdf or .docx.")
-
-    return cv_content.strip()
-
 # Create a Crew for processing
 crew = Crew(
-    agents=[cv_analysis_agent, portfolio_generation_agent],
-    tasks=[cv_analysis_task, portfolio_task],
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, writer_task],
     process=Process.sequential,
 )
 
-
-
-# Function to process CV and generate portfolio
-def process_cv(file):
+# Define a function that will take the research topic as input and return the markdown output
+def generate_report(topic):
     try:
-        cv_file_content = read_cv_file(file.name)
-        result = crew.kickoff(inputs={'cv': cv_file_content})
-
-        # Print the entire result object to explore its contents (for debugging)
-        print(result)
-
-        # Convert the result to string
-        html_output = str(result)
-
-        # Use replace to remove '''html''' and ''' from the output
-        clean_html_output = html_output.replace("```html", '').replace("```", '').strip()
-
-        return clean_html_output  # Return the cleaned HTML
+        # Kickoff the Crew process with the provided topic
+        result = crew.kickoff(inputs={'topic': topic})
+        # Read the generated markdown file (assuming report is saved as 'key_points_report.md')
+        with open('key_points_report.md', 'r') as file:
+            markdown_output = file.read()
+        return markdown_output
     except Exception as e:
-        return f"Error: {e}"
-
-
-
-def save_html_to_file(html_content):
-    output_file_path = "Portfolio_generated_by_FiftyBit.html"
-    with open(output_file_path, "w") as f:
-        f.write(html_content)
-    return output_file_path
-
-
-import html
-
-def upload_file(filepath):
-    name = Path(filepath).name
-    html_content = process_cv(filepath)  # Get HTML content from the CV
-
-    # Clean the HTML content and escape it for proper iframe embedding
-    clean_html_output = html_content.replace("```html", '').replace("```", '').strip()
-    escaped_html_content = html.escape(clean_html_output)  # Escape HTML content
-
-    # Debugging print to check the escaped HTML content
-    #print("Escaped HTML content:", escaped_html_content)
-
-    # Save the cleaned HTML content to a file (if you still want this feature)
-    file_path = save_html_to_file(clean_html_output)
-
-    # Return a full HTML string with embedded iframe for preview
-    iframe_html = f"""
-    <iframe srcdoc="{escaped_html_content}" style="width:100%; height:1000px; border:none; overflow:auto;"></iframe>
-    """
-    return iframe_html, gr.UploadButton(visible=False), gr.DownloadButton(label=f"Download Code", value=file_path, visible=True)
-    
-def download_file():
-    return [gr.UploadButton(label=f"Regenerate", visible=True), gr.DownloadButton(visible=False)]
-
-# Gradio App
-with gr.Blocks() as demo:
-    gr.Markdown("<center><h1> CV-2-Portfolio Site Generator</center></h1>")
-    gr.Markdown("<center><h2>Upload your CV in PDF or DOCX format for analysis and portfolio webpage generation.</center></h2>")
+        return f"Error during processing: {e}"
 
-    u = gr.UploadButton("Upload CV (.pdf or .docx)", file_count="single")
-    d = gr.DownloadButton("Download Portfolio", visible=False)
 
-    # Use gr.HTML with larger iframe size to display the full preview
-    output_preview = gr.HTML(
-        value="<div style='width:100%; height:1000px; border:1px solid #ccc; text-align:center;'>Upload a file to preview the generated portfolio</div>"
-    )
+# Gradio Interface
+def gradio_interface():
+    # Use Column to organize input and output in vertical layout
+    with gr.Blocks() as interface:
+        gr.Markdown("<center><h1>AI Research Assistant Agent-Key Points Extractor</h1></center>")
+        with gr.Column():
+            topic_input = gr.Textbox(lines=2, placeholder="Enter your research topic/keywords", label="Research Topic/Keywords")
+            result_output = gr.Markdown(label="Key Points Output")
+            submit_button = gr.Button("Generate Report")
 
-    # Connect the upload button to the upload_file function and update the output preview
-    u.upload(upload_file, u, [output_preview, u, d])
+            submit_button.click(generate_report, inputs=topic_input, outputs=result_output)
 
-    # Handle download button click
-    d.click(download_file, None, [u, d])
+    interface.launch(debug=True)
 
-demo.launch(debug=True)
+# Run the Gradio interface
+if __name__ == "__main__":
+    gradio_interface()