Update app.py

app.py

@@ -2,24 +2,19 @@ import gradio as gr
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain.chat_models import ChatOpenAI
-from langchain.output_parsers import StructuredOutputParser, ResponseSchema
 
 # Replace this with a CPU-compatible LLM for Hugging Face Spaces
 llm = ChatOpenAI(temperature=0.2, model="gpt-3.5-turbo")  # Replace with HuggingFacePipeline if no OpenAI key
 
-schemas = [
-    ResponseSchema(name="MatchScore", description="Score from 0 to 100 for how well the resume matches the job description"),
-    ResponseSchema(name="KeySkills", description="List of key skills matched between resume and job description"),
-    ResponseSchema(name="Justification", description="Short reasoning for the score and skills match")
-]
-parser = StructuredOutputParser.from_response_schemas(schemas)
-
 prompt_template = PromptTemplate(
     template="""
 You are a professional resume screener AI.
 
 Below is a resume and a job description.
-Evaluate how well the resume fits the job and answer with the required JSON fields.
+Evaluate how well the resume fits the job and provide a plain text output with:
+- Match Score (0-100)
+- Key Skills matched
+- Justification for the score
 
 Resume:
 {resume}
@@ -27,11 +22,9 @@ Resume:
 Job Description:
 {job}
 
-Format your response as JSON:
-{format_instructions}
+Response:
 """,
-    input_variables=["resume", "job"],
-    partial_variables={"format_instructions": parser.get_format_instructions()},
+    input_variables=["resume", "job"]
 )
 
 chain = LLMChain(llm=llm, prompt=prompt_template)
@@ -39,9 +32,9 @@ chain = LLMChain(llm=llm, prompt=prompt_template)
 def screen_resume(resume, jd):
     try:
         response = chain.run(resume=resume, job=jd)
-        return parser.parse(response)
+        return response
     except Exception as e:
-        return {"error": str(e)}
+        return f"Error: {str(e)}"
 
 iface = gr.Interface(
     fn=screen_resume,
@@ -49,10 +42,10 @@ iface = gr.Interface(
         gr.Textbox(label="Paste Resume Text", lines=15, placeholder="Paste plain text from resume..."),
         gr.Textbox(label="Paste Job Description", lines=10, placeholder="Paste plain text from JD..."),
     ],
-    outputs="json",
+    outputs=gr.Textbox(label="Analysis Result"),
     title="Resume Screener Agent",
-    description="Upload a resume and a job description. The AI will match and score them.",
+    description="Upload a resume and a job description. The AI will match and score them."
 )
 
 if __name__ == "__main__":
-    iface.launch(share=True)
+    iface.launch()