Update app.py

app.py

@@ -6,10 +6,6 @@ from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from transformers import pipeline
 
-
-
-
-# 2. Model Initialization
 # Initialize sentiment analyzer
 sentiment_analyzer = pipeline(
     "sentiment-analysis",
@@ -20,10 +16,10 @@ sentiment_analyzer = pipeline(
 # Initialize LLM
 llm = HuggingFaceHub(
     repo_id="deepseek-ai/deepseek-coder-33b-instruct",
-    model_kwargs={"temperature": 0.7}
+    model_kwargs={"temperature": 0.7},
+    huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN")
 )
 
-# 3. Templates
 email_template = PromptTemplate(
     input_variables=["previous_interaction", "situation_type", "tone", "urgency"],
     template="""Based on these details, generate a professional follow-up email:
@@ -41,39 +37,8 @@ Generate a personalized email that:
 """
 )
 
-scoring_template = """
-Analyze this follow-up email carefully and provide scores on a scale of 1-10 for each category:
-
-Email to analyze:
-{email_text}
-
-Please provide numerical scores and explanations in this exact format:
-
-CLARITY SCORE: [1-10]
-Explanation: [Why this score]
-
-PROFESSIONALISM SCORE: [1-10]
-Explanation: [Why this score]
-
-ACTION ITEMS SCORE: [1-10]
-Explanation: [Why this score]
-
-PERSONALIZATION SCORE: [1-10]
-Explanation: [Why this score]
-
-OVERALL EFFECTIVENESS SCORE: [1-10]
-Explanation: [Why this score]
-
-IMPROVEMENT SUGGESTIONS:
-1. [First suggestion]
-2. [Second suggestion]
-3. [Third suggestion]
-"""
-
-# 4. Create LangChain
 email_chain = LLMChain(llm=llm, prompt=email_template)
 
-# 5. Helper Functions
 def analyze_sentiment(text):
     try:
         result = sentiment_analyzer(text)[0]
@@ -86,85 +51,41 @@ def analyze_sentiment(text):
     except Exception as e:
         return 'Professional'
 
-# 6. Main Generation Function
 def generate_followup_email(previous_interaction, situation_type, tone, urgency):
     try:
         if not tone:
             tone = analyze_sentiment(previous_interaction)
-            
-        # Generate email
-        email_result = email_chain.run({
+        return email_chain.run({
             "previous_interaction": previous_interaction,
             "situation_type": situation_type,
             "tone": tone,
             "urgency": urgency
         })
-        
-        # Generate score
-        score_result = llm(scoring_template.format(email_text=email_result))
-        return email_result, score_result
     except Exception as e:
-        return f"Error generating email: {str(e)}", "Scoring unavailable"
+        return f"Error generating email: {str(e)}"
 
-# 7. Gradio Interface
 demo = gr.Interface(
     fn=generate_followup_email,
     inputs=[
-        gr.Textbox(
-            label="Previous Interaction",
-            lines=5,
-            placeholder="Describe the previous interaction with the customer..."
-        ),
-        gr.Dropdown(
-            label="Situation Type",
-            choices=[
-                "Complaint Resolution",
-                "Service Issue",
-                "Payment Dispute",
-                "Product Query",
-                "General Follow-up"
-            ]
-        ),
-        gr.Dropdown(
-            label="Tone (Optional - will be automatically detected if not specified)",
-            choices=[
-                "",
-                "Professional",
-                "Apologetic",
-                "Friendly",
-                "Formal",
-                "Empathetic"
-            ]
-        ),
-        gr.Dropdown(
-            label="Urgency",
-            choices=["High", "Medium", "Low"]
-        )
-    ],
-    outputs=[
-        gr.Textbox(label="Generated Email"),
-        gr.Textbox(label="Email Score and Suggestions")
+        gr.Textbox(label="Previous Interaction", lines=5, 
+                  placeholder="Describe the previous interaction with the customer..."),
+        gr.Dropdown(label="Situation Type",
+                   choices=["Complaint Resolution", "Service Issue", 
+                           "Payment Dispute", "Product Query", "General Follow-up"]),
+        gr.Dropdown(label="Tone (Optional - will be automatically detected if not specified)",
+                   choices=["", "Professional", "Apologetic", "Friendly", "Formal", "Empathetic"]),
+        gr.Dropdown(label="Urgency", choices=["High", "Medium", "Low"])
     ],
-    title="Smart Sales Email Generator with Quality Scoring",
-    description="Generate and evaluate follow-up emails based on previous interactions",
+    outputs=gr.Textbox(label="Generated Email"),
+    title="Smart Sales Email Generator",
+    description="Generate personalized follow-up emails based on previous interactions",
     examples=[
-        [
-            "Customer complained about slow website loading times and threatened to cancel subscription",
-            "Complaint Resolution",
-            "Apologetic",
-            "High"
-        ],
-        [
-            "Client requested information about premium features and pricing",
-            "Product Query",
-            "Professional",
-            "Medium"
-        ]
+        ["Customer complained about slow website loading times and threatened to cancel subscription", 
+         "Complaint Resolution", "Apologetic", "High"],
+        ["Client requested information about premium features and pricing", 
+         "Product Query", "Professional", "Medium"]
     ]
 )
 
-# 8. Launch App
 if __name__ == "__main__":
-    demo.launch()
-
-
+    demo.launch()