Update app.py

app.py

@@ -1,12 +1,14 @@
 import os
-import re
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# In Space Settings → Secrets, add HF_TOKEN
 HF_TOKEN = os.getenv("HF_TOKEN", "")
+
+# You can also add HF_MODEL in Secrets; otherwise use a default.
 DEFAULT_MODEL = os.getenv("HF_MODEL", "Qwen/Qwen2.5-72B-Instruct")
 
-MAX_TOKENS = 1400
+MAX_NEW_TOKENS = 1400
 TEMPERATURE = 0.4
 
 SYSTEM_INSTRUCTIONS = """
@@ -37,11 +39,10 @@ Under Message Examples:
 - One decision-maker message
 
 Tone: senior AE speaking to peers. No emojis.
-"""
+""".strip()
 
 USER_TEMPLATE = """
-Account (name or org URL):
-{account}
+Account (name or org URL): {account}
 
 Optional context:
 Industry: {industry}
@@ -55,36 +56,47 @@ Notes: {notes}
 Task:
 Create a structured outbound plan for this account based on public
 open-source platform usage and OSS → Enterprise conversion patterns.
-"""
+""".strip()
+
 
-def call_model(prompt, model):
+def call_model(prompt: str, model: str) -> str:
+    # IMPORTANT: Don't crash the Space if the token is missing.
     if not HF_TOKEN:
-        return "Missing HF_TOKEN. Add it in Space Settings → Secrets."
+        return (
+            "Missing HF_TOKEN.\n\n"
+            "Fix:\n"
+            "Space → Settings → Secrets → Add:\n"
+            "- Name: HF_TOKEN\n"
+            "- Value: your Hugging Face token\n"
+        )
 
     client = InferenceClient(model=model, token=HF_TOKEN)
 
-    response = client.chat.completions.create(
-        messages=[
-            {"role": "system", "content": SYSTEM_INSTRUCTIONS},
-            {"role": "user", "content": prompt},
-        ],
-        temperature=TEMPERATURE,
-        max_tokens=MAX_TOKENS,
-    )
-
-    return response.choices[0].message.content.strip()
-
-def generate(
-    account,
-    industry,
-    region,
-    primary_persona,
-    secondary_personas,
-    offer_focus,
-    goal,
-    notes,
-    model,
-):
+    # Most-compatible approach across endpoints:
+    full_prompt = f"{SYSTEM_INSTRUCTIONS}\n\n{prompt}\n\nReturn the output using the required 6 headings."
+
+    try:
+        out = client.text_generation(
+            full_prompt,
+            max_new_tokens=MAX_NEW_TOKENS,
+            temperature=TEMPERATURE,
+            do_sample=True,
+            return_full_text=False,
+        )
+        return (out or "").strip()
+    except Exception as e:
+        return (
+            "Model call failed.\n\n"
+            f"Model: {model}\n"
+            f"Error: {repr(e)}\n\n"
+            "Try:\n"
+            "1) Set a different HF_MODEL (an instruct model you have access to)\n"
+            "2) Or use a smaller model for reliability\n"
+        )
+
+
+def generate(account, industry, region, primary_persona, secondary_personas, offer_focus, goal, notes, model):
+    account = (account or "").strip()
     if not account:
         return "Please input an account name or org URL."
 
@@ -99,7 +111,9 @@ def generate(
         notes=notes or "Not provided",
     )
 
-    return call_model(prompt, model or DEFAULT_MODEL)
+    model = (model or DEFAULT_MODEL).strip()
+    return call_model(prompt, model)
+
 
 with gr.Blocks(title="Enterprise OSS → Outbound Ops") as app:
     gr.Markdown(
@@ -107,7 +121,7 @@ with gr.Blocks(title="Enterprise OSS → Outbound Ops") as app:
         "Input an **account name or public org URL** to generate an enterprise outbound plan."
     )
 
-    account = gr.Textbox(label="Account name or org URL")
+    account = gr.Textbox(label="Account name or org URL", placeholder="e.g., Emirates or https://huggingface.co/emirates")
 
     with gr.Row():
         industry = gr.Textbox(label="Industry (optional)")
@@ -121,29 +135,16 @@ with gr.Blocks(title="Enterprise OSS → Outbound Ops") as app:
     goal = gr.Textbox(label="Goal (optional)")
     notes = gr.Textbox(label="Notes / known public signals (optional)", lines=4)
 
-    model = gr.Textbox(
-        label="Model",
-        value=DEFAULT_MODEL,
-        help="Change only if you know what you're doing",
-    )
+    model = gr.Textbox(label="Model", value=DEFAULT_MODEL)
 
     run = gr.Button("Generate outbound plan")
     output = gr.Markdown()
 
     run.click(
         fn=generate,
-        inputs=[
-            account,
-            industry,
-            region,
-            primary_persona,
-            secondary_personas,
-            offer_focus,
-            goal,
-            notes,
-            model,
-        ],
+        inputs=[account, industry, region, primary_persona, secondary_personas, offer_focus, goal, notes, model],
         outputs=output,
     )
 
-app.launch()
+# IMPORTANT for Hugging Face Spaces:
+app.launch(server_name="0.0.0.0", server_port=7860)