Upload app.py

app.py

@@ -8,9 +8,6 @@ import os
 from dotenv import load_dotenv
 load_dotenv()
 
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-model_vision = genai.GenerativeModel('gemini-pro-vision')
-model_text = genai.GenerativeModel("gemini-pro")
 
 INTERMEDIATE_JSON_PATH = "intermediate_data.json"
 INTERMEDIATE_JOB_DESC_PATH = "intermediate_job_desc.txt"
@@ -33,43 +30,41 @@ def load_prompt(filename):
     except Exception as e:
         return f"Error loading prompt: {e}"
 
-def process_pdf_and_save_job_desc(pdf_file, job_description):
-    try:
-        
-        if not pdf_file:
-            return None, "No file provided"
-
-        doc = fitz.open(stream=pdf_file, filetype="pdf")
-
-        # Store results in a list and process all pages
-        json_data = []
-        images = []  # List to hold images of each page
-        for page_num in range(len(doc)):
-            page = doc.load_page(page_num)
-            pix = page.get_pixmap()
-            img_bytes = pix.tobytes("png")
-            image = Image.open(io.BytesIO(img_bytes))
-            images.append(image)
-
-            # ... Your image processing with the genai model
-            prompt = load_prompt("prompts/resume_parsing_prompt.txt")
-            response = model_vision.generate_content([prompt, image])
-            json_data.append(response.text) 
-
-        doc.close()
-
-        # Store data appropriately (consider a list of JSON objects, or a structured dict)
-        with open(INTERMEDIATE_JSON_PATH, "w") as json_file:
-            json.dump(json_data, json_file)
-        with open(INTERMEDIATE_JOB_DESC_PATH, "w") as file:
-            file.write(job_description)
-
-        return images, json_data  # Return the list of images
-    
-    except fitz.FitzError as e:
-        return None, f"PDF processing error: {e}"
-    except Exception as e:
-        return None, f"An unexpected error occurred: {e}"
+def process_pdf_and_save_job_desc(pdf_file, job_description, api_key):
+    if not pdf_file:
+        return None, "No file provided"
+
+    # Configure the Gemini model using the provided API key
+    genai.configure(api_key=api_key)
+    model_vision = genai.GenerativeModel('gemini-pro-vision')
+    model_text = genai.GenerativeModel("gemini-pro")
+
+    doc = fitz.open(stream=pdf_file, filetype="pdf")
+
+    # Store results in a list and process all pages
+    json_data = []
+    images = []  # List to hold images of each page
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        pix = page.get_pixmap()
+        img_bytes = pix.tobytes("png")
+        image = Image.open(io.BytesIO(img_bytes))
+        images.append(image)
+
+        # ... Your image processing with the genai model
+        prompt = load_prompt("prompts/resume_parsing_prompt.txt")
+        response = model_vision.generate_content([prompt, image])
+        json_data.append(response.text) 
+
+    doc.close()
+
+    # Store data appropriately (consider a list of JSON objects, or a structured dict)
+    with open(INTERMEDIATE_JSON_PATH, "w", encoding='utf-8') as json_file:  # Specify UTF-8 encoding
+        json.dump(json_data, json_file)
+    with open(INTERMEDIATE_JOB_DESC_PATH, "w", encoding='utf-8') as file:  # Specify UTF-8 encoding
+        file.write(job_description)
+
+    return images, json_data  # Return the list of images
 
 def display_json():
     try:
@@ -98,30 +93,30 @@ def generate_content_based_on_json(example_functionality):
     except Exception as e:
         return f"An error occurred: {e}"
 
-def generate_interview_questions():
-    # Assuming json_data is a string containing JSON data
-    # Here, you would customize the prompt to include specific details
-    # from the jsodef generate_interview_questions():
+def generate_interview_questions(api_key):
     with open(INTERMEDIATE_JSON_PATH, "r") as json_file:
         json_data = json.load(json_file)
 
-    combined_data = " ".join(json_data)  # Combine with spaces(adjust as needed)
+    combined_data = " ".join(json_data)  # Combine with spaces (adjust as needed)
     prompt = load_prompt("prompts/interview_questions_prompt.txt") + combined_data
-    responses = model_text.generate_content(prompt)
-    return responses.text
 
+    # Configure the Gemini model using the provided API key
+    genai.configure(api_key=api_key)
+    model_text = genai.GenerativeModel("gemini-pro")
 
+    responses = model_text.generate_content(prompt)
+    return responses.text
 
-# Define the new Gradio interface for generating interview questions
+# Define the new Gradio interface for generating interview questions with API key input
 interview_interface = gr.Interface(
     fn=generate_interview_questions,
-    inputs=[],
+    inputs=[gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...")],
     outputs=gr.Textbox(label="Generated Interview Questions"),
     title="Generate Interview Questions"
 )
 
 
-def generate_skill_gap_analysis():
+def generate_skill_gap_analysis(api_key):
     try:
         # Read the saved resume data (JSON)
         with open(INTERMEDIATE_JSON_PATH, "r") as file:
@@ -131,6 +126,10 @@ def generate_skill_gap_analysis():
         with open(INTERMEDIATE_JOB_DESC_PATH, "r") as file:
             job_description = file.read()
 
+        # Configure the Gemini model using the provided API key
+        genai.configure(api_key=api_key)
+        model_text = genai.GenerativeModel("gemini-pro")
+
         # Construct a detailed prompt for the Gemini model
         prompt = load_prompt("prompts/skills_gap_prompt.txt").replace(
             "job_description", job_description).replace("json_data", json_data)
@@ -143,17 +142,21 @@ def generate_skill_gap_analysis():
     except Exception as e:
         return f"An error occurred: {e}"
 
-
+# Define the Gradio interface for generating a skill gap analysis with API key input
 skill_gap_analysis_interface = gr.Interface(
     fn=generate_skill_gap_analysis,
-    inputs=[],  # No inputs
+    inputs=[gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...")],
     outputs=gr.Textbox(label="Skill Gap Analysis"),
     title="Skill Gap Analysis"
 )
 
 
-def generate_cover_letter():
+def generate_cover_letter(api_key):
     try:
+        # Configure the Gemini model using the provided API key
+        genai.configure(api_key=api_key)
+        model_text = genai.GenerativeModel("gemini-pro")
+
         # Read the saved job description
         with open(INTERMEDIATE_JOB_DESC_PATH, "r") as file:
             job_description = file.read()
@@ -167,41 +170,37 @@ def generate_cover_letter():
             "job_description", job_description).replace("json_data", json_data)
 
         # Generate the cover letter using the model
-        response = model_text.generate_content(prompt, stream=True)
-        response.resolve()
+        response = model_text.generate_content(prompt)
 
         return response.text
 
     except Exception as e:
         return f"An error occurred: {e}"
 
-
-# Define the Gradio interface for generating a cover letter
+# Define the Gradio interface for generating a cover letter with API key input
 cover_letter_interface = gr.Interface(
     fn=generate_cover_letter,
-    inputs=[],
+    inputs=[gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...")],
     outputs=gr.Textbox(label="Generated Cover Letter"),
     title="Cover Letter Generator"
 )
 
 
-
-def gradio_pdf_interface(pdf_content, job_description):
-    images, _ = process_pdf_and_save_job_desc(pdf_content, job_description)
+def gradio_pdf_interface(pdf_content, job_description, api_key):
+    images, _ = process_pdf_and_save_job_desc(pdf_content, job_description, api_key)
     return images  # Return the list of images to be displayed in the Gallery
 
-# Define the updated interface for PDF processing
-# Define the updated interface for PDF processing with better descriptions
+# Define the updated interface for PDF processing with an additional input for the API key
 pdf_interface = gr.Interface(
     fn=gradio_pdf_interface,
     inputs=[
         gr.File(type="binary", label="Upload PDF Resume"),
-        gr.Textbox(label="Job Description", placeholder="Enter the job description here...", lines=5)
+        gr.Textbox(label="Job Description", placeholder="Enter the job description here..."),
+        gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...")
     ],
     outputs=gr.Gallery(label="Processed PDF Pages"),
     title="PDF Processing and Job Description",
-    description="Upload a PDF resume and provide the job description. The system will process the resume and extract relevant data. You can navigate through the processed pages below.",
-    theme=custom_theme
+    description="Upload a PDF resume, provide the job description, and enter your Gemini API key. The system will process the resume and extract relevant data."
 )